ࡱ> q` 7.bjbjqPqP r::%Q" $)dkPL(, Xq6`"F$$H?pApApApApApAp$thvrepS "BSSepXXzp~SSSS:X88?pSS?pSS?KDMT sTLDVp`XqL$hwhwPMM\hwGNl~ ZSF $jepep+(XqSSSSKD$( %)TXXX    INTEGRATED GLOBAL OBSERVATION OF LAND An IGOS-P Theme  TIME \@ "MMMM d, yyyy" May 2, 2007 -DRAFT- Table of Contents  TOC \o "1-3" \h \z \u  HYPERLINK \l "_Toc165892151" 1 Introduction  PAGEREF _Toc165892151 \h 6  HYPERLINK \l "_Toc165892152" 2 The needs for IGOL  PAGEREF _Toc165892152 \h 8  HYPERLINK \l "_Toc165892153" 2.1 Agriculture  PAGEREF _Toc165892153 \h 8  HYPERLINK \l "_Toc165892154" 2.2 Forestry  PAGEREF _Toc165892154 \h 8  HYPERLINK \l "_Toc165892155" 2.3 Land degradation  PAGEREF _Toc165892155 \h 9  HYPERLINK \l "_Toc165892156" 2.4 Ecosystem goods and services  PAGEREF _Toc165892156 \h 9  HYPERLINK \l "_Toc165892157" 2.5 Biodiversity and Conservation  PAGEREF _Toc165892157 \h 9  HYPERLINK \l "_Toc165892158" 2.6 Human health  PAGEREF _Toc165892158 \h 10  HYPERLINK \l "_Toc165892159" 2.7 Water resource management  PAGEREF _Toc165892159 \h 10  HYPERLINK \l "_Toc165892160" 2.8 Disasters  PAGEREF _Toc165892160 \h 10  HYPERLINK \l "_Toc165892161" 2.9 Energy  PAGEREF _Toc165892161 \h 11  HYPERLINK \l "_Toc165892162" 2.10 Urbanization: sustainable human settlement  PAGEREF _Toc165892162 \h 11  HYPERLINK \l "_Toc165892163" 2.11 Climate Change  PAGEREF _Toc165892163 \h 11  HYPERLINK \l "_Toc165892164" 3 Stakeholders for GLOBAL Land Observations  PAGEREF _Toc165892164 \h 12  HYPERLINK \l "_Toc165892165" 3.1 Governmental stakeholders  PAGEREF _Toc165892165 \h 12  HYPERLINK \l "_Toc165892166" 3.2 International initiatives  PAGEREF _Toc165892166 \h 13  HYPERLINK \l "_Toc165892167" 3.3 NGOs  PAGEREF _Toc165892167 \h 13  HYPERLINK \l "_Toc165892168" 3.4 Science  PAGEREF _Toc165892168 \h 13  HYPERLINK \l "_Toc165892169" 3.5 General Public  PAGEREF _Toc165892169 \h 13  HYPERLINK \l "_Toc165892170" 3.6 Private Sector  PAGEREF _Toc165892170 \h 14  HYPERLINK \l "_Toc165892171" 3.7 Engagement of stakeholders  PAGEREF _Toc165892171 \h 14  HYPERLINK \l "_Toc165892172" 4 Products and observables  PAGEREF _Toc165892172 \h 15  HYPERLINK \l "_Toc165892173" 4.1 Land cover  PAGEREF _Toc165892173 \h 15  HYPERLINK \l "_Toc165892174" 4.1.1 Observation needs and technical requirements  PAGEREF _Toc165892174 \h 15  HYPERLINK \l "_Toc165892175" 4.1.2 Current status  PAGEREF _Toc165892175 \h 17  HYPERLINK \l "_Toc165892176" 4.1.3 Current plans  PAGEREF _Toc165892176 \h 17  HYPERLINK \l "_Toc165892177" 4.1.4 Major gaps and necessary enhancements  PAGEREF _Toc165892177 \h 18  HYPERLINK \l "_Toc165892178" 4.1.5 Product-specific critical issues  PAGEREF _Toc165892178 \h 18  HYPERLINK \l "_Toc165892179" 4.1.6 Principal recommendations  PAGEREF _Toc165892179 \h 19  HYPERLINK \l "_Toc165892180" 4.2 Land use, land use change  PAGEREF _Toc165892180 \h 19  HYPERLINK \l "_Toc165892181" 4.2.1 Observation needs and technical requirements  PAGEREF _Toc165892181 \h 20  HYPERLINK \l "_Toc165892182" 4.2.2 Current status  PAGEREF _Toc165892182 \h 20  HYPERLINK \l "_Toc165892183" 4.2.3 Current plans  PAGEREF _Toc165892183 \h 21  HYPERLINK \l "_Toc165892184" 4.2.4 Major gaps and necessary improvements  PAGEREF _Toc165892184 \h 21  HYPERLINK \l "_Toc165892185" 4.2.5 Product-specific critical issues  PAGEREF _Toc165892185 \h 22  HYPERLINK \l "_Toc165892186" 4.2.6 Principal recommendations  PAGEREF _Toc165892186 \h 23  HYPERLINK \l "_Toc165892187" 4.3 Biophysical properties relating to ecosystem dynamics  PAGEREF _Toc165892187 \h 23  HYPERLINK \l "_Toc165892188" 4.3.1 Observation needs and technical requirements  PAGEREF _Toc165892188 \h 23  HYPERLINK \l "_Toc165892189" 4.3.2 Current status  PAGEREF _Toc165892189 \h 24  HYPERLINK \l "_Toc165892190" 4.3.3 Major gaps and necessary enhancements  PAGEREF _Toc165892190 \h 24  HYPERLINK \l "_Toc165892191" 4.3.4 Principal recommendations  PAGEREF _Toc165892191 \h 27  HYPERLINK \l "_Toc165892192" 4.4 Fire  PAGEREF _Toc165892192 \h 27  HYPERLINK \l "_Toc165892193" 4.4.1 Observation needs / technical requirements  PAGEREF _Toc165892193 \h 28  HYPERLINK \l "_Toc165892194" 4.4.2 Current status of Satellite-based monitoring systems  PAGEREF _Toc165892194 \h 29  HYPERLINK \l "_Toc165892195" 4.4.3 Major gaps and necessary enhancements  PAGEREF _Toc165892195 \h 30  HYPERLINK \l "_Toc165892196" 4.5 Biodiversity and Conservation  PAGEREF _Toc165892196 \h 32  HYPERLINK \l "_Toc165892197" 4.5.1 Observation needs and technical requirements  PAGEREF _Toc165892197 \h 32  HYPERLINK \l "_Toc165892198" 4.5.2 Current plans  PAGEREF _Toc165892198 \h 33  HYPERLINK \l "_Toc165892199" 4.5.3 Major gaps and necessary enhancements  PAGEREF _Toc165892199 \h 34  HYPERLINK \l "_Toc165892200" 4.5.4 Product-specific critical issues  PAGEREF _Toc165892200 \h 34  HYPERLINK \l "_Toc165892201" 4.5.5 Principal recommendations  PAGEREF _Toc165892201 \h 35  HYPERLINK \l "_Toc165892202" 4.6 Agriculture  PAGEREF _Toc165892202 \h 35  HYPERLINK \l "_Toc165892203" 4.6.1 Observation needs and technical requirements  PAGEREF _Toc165892203 \h 36  HYPERLINK \l "_Toc165892204" 4.6.2 Current status  PAGEREF _Toc165892204 \h 37  HYPERLINK \l "_Toc165892205" 4.6.3 Current plans  PAGEREF _Toc165892205 \h 38  HYPERLINK \l "_Toc165892206" 4.6.4 Major gaps and necessary enhancements  PAGEREF _Toc165892206 \h 39  HYPERLINK \l "_Toc165892207" 4.6.5 Product-specific critical issues  PAGEREF _Toc165892207 \h 40  HYPERLINK \l "_Toc165892208" 4.6.6 Principal recommendations  PAGEREF _Toc165892208 \h 40  HYPERLINK \l "_Toc165892209" 4.7 Soils  PAGEREF _Toc165892209 \h 40  HYPERLINK \l "_Toc165892210" 4.7.1 Observation needs and technical requirements  PAGEREF _Toc165892210 \h 41  HYPERLINK \l "_Toc165892211" 4.7.2 Current status  PAGEREF _Toc165892211 \h 41  HYPERLINK \l "_Toc165892212" 4.7.3 Current plans  PAGEREF _Toc165892212 \h 41  HYPERLINK \l "_Toc165892213" 4.7.4 Major gaps and necessary enhancements  PAGEREF _Toc165892213 \h 42  HYPERLINK \l "_Toc165892214" 4.7.5 Product-specific critical issues  PAGEREF _Toc165892214 \h 42  HYPERLINK \l "_Toc165892215" 4.7.6 Principal recommendations  PAGEREF _Toc165892215 \h 42  HYPERLINK \l "_Toc165892216" 4.8 Human Settlements and Socio-Economic Data  PAGEREF _Toc165892216 \h 43  HYPERLINK \l "_Toc165892217" 4.8.1 Observation needs and technical requirements  PAGEREF _Toc165892217 \h 44  HYPERLINK \l "_Toc165892218" 4.8.2 Current status  PAGEREF _Toc165892218 \h 44  HYPERLINK \l "_Toc165892219" 4.8.3 Current plans  PAGEREF _Toc165892219 \h 45  HYPERLINK \l "_Toc165892220" 4.8.4 Major gaps and necessary enhancements  PAGEREF _Toc165892220 \h 45  HYPERLINK \l "_Toc165892221" 4.8.5 Principal recommendations  PAGEREF _Toc165892221 \h 46  HYPERLINK \l "_Toc165892222" 4.9 Water Availability and Use  PAGEREF _Toc165892222 \h 47  HYPERLINK \l "_Toc165892223" 4.9.1 Observation needs and technical requirements  PAGEREF _Toc165892223 \h 47  HYPERLINK \l "_Toc165892224" 4.9.2 Current plans  PAGEREF _Toc165892224 \h 49  HYPERLINK \l "_Toc165892225" 4.9.3 Major gaps and necessary enhancements  PAGEREF _Toc165892225 \h 50  HYPERLINK \l "_Toc165892226" 4.9.4 Principal recommendations  PAGEREF _Toc165892226 \h 50  HYPERLINK \l "_Toc165892227" 4.10 Topography  PAGEREF _Toc165892227 \h 50  HYPERLINK \l "_Toc165892228" 4.10.1 Observation needs and technical requirements  PAGEREF _Toc165892228 \h 50  HYPERLINK \l "_Toc165892229" 4.10.2 Current status  PAGEREF _Toc165892229 \h 51  HYPERLINK \l "_Toc165892230" 4.10.3 Current plans  PAGEREF _Toc165892230 \h 52  HYPERLINK \l "_Toc165892231" 4.10.4 Necessary Improvements and Major Gaps  PAGEREF _Toc165892231 \h 52  HYPERLINK \l "_Toc165892232" 4.10.5 Product-specific Critical issues  PAGEREF _Toc165892232 \h 52  HYPERLINK \l "_Toc165892233" 4.10.6 Principal recommendations  PAGEREF _Toc165892233 \h 52  HYPERLINK \l "_Toc165892234" 5 Integration issues  PAGEREF _Toc165892234 \h 53  HYPERLINK \l "_Toc165892235" 5.1 Validation and Quality Assessment  PAGEREF _Toc165892235 \h 53  HYPERLINK \l "_Toc165892236" 5.1.1 Principles  PAGEREF _Toc165892236 \h 53  HYPERLINK \l "_Toc165892237" 5.1.2 Current Status  PAGEREF _Toc165892237 \h 53  HYPERLINK \l "_Toc165892238" 5.1.3 Major gaps and necessary enhancements  PAGEREF _Toc165892238 \h 54  HYPERLINK \l "_Toc165892239" 5.1.4 Principal recommendations  PAGEREF _Toc165892239 \h 55  HYPERLINK \l "_Toc165892240" 5.2 Data fusion for analysis and modeling  PAGEREF _Toc165892240 \h 55  HYPERLINK \l "_Toc165892241" 5.2.1 Observation requirements  PAGEREF _Toc165892241 \h 56  HYPERLINK \l "_Toc165892242" 5.2.2 Current status  PAGEREF _Toc165892242 \h 56  HYPERLINK \l "_Toc165892243" 5.2.3 Major gaps and necessary enhancements  PAGEREF _Toc165892243 \h 56  HYPERLINK \l "_Toc165892244" 5.2.4 Principal recommendations  PAGEREF _Toc165892244 \h 56  HYPERLINK \l "_Toc165892245" 5.3 Data assimilation  PAGEREF _Toc165892245 \h 57  HYPERLINK \l "_Toc165892246" 5.3.1 Model-data synthesis  PAGEREF _Toc165892246 \h 57  HYPERLINK \l "_Toc165892247" 5.3.2 Major gaps and necessary enhancements  PAGEREF _Toc165892247 \h 57  HYPERLINK \l "_Toc165892248" 5.3.3 Principal recommendations  PAGEREF _Toc165892248 \h 58  HYPERLINK \l "_Toc165892249" 6 Data Delivery  PAGEREF _Toc165892249 \h 59  HYPERLINK \l "_Toc165892250" 6.1 Data and product access  PAGEREF _Toc165892250 \h 59  HYPERLINK \l "_Toc165892251" 6.1.1 Data access policies  PAGEREF _Toc165892251 \h 59  HYPERLINK \l "_Toc165892252" 6.1.2 Data documentation policies  PAGEREF _Toc165892252 \h 60  HYPERLINK \l "_Toc165892253" 6.1.3 Principal recommendations  PAGEREF _Toc165892253 \h 60  HYPERLINK \l "_Toc165892254" 6.2 Data and Information Delivery Systems  PAGEREF _Toc165892254 \h 61  HYPERLINK \l "_Toc165892255" 6.2.1 Provide Access to Data  PAGEREF _Toc165892255 \h 61  HYPERLINK \l "_Toc165892256" 6.2.2 Functionality for Assessing and Documenting Data Integrity  PAGEREF _Toc165892256 \h 62  HYPERLINK \l "_Toc165892257" 6.2.3 Data Mining and Analysis Capabilities  PAGEREF _Toc165892257 \h 62  HYPERLINK \l "_Toc165892258" 6.2.4 Distributed Archiving and Management Systems  PAGEREF _Toc165892258 \h 63  HYPERLINK \l "_Toc165892259" 6.2.5 Principal recommendations  PAGEREF _Toc165892259 \h 63  HYPERLINK \l "_Toc165892260" 7 capacity building  PAGEREF _Toc165892260 \h 64  HYPERLINK \l "_Toc165892261" 7.1 Background  PAGEREF _Toc165892261 \h 64  HYPERLINK \l "_Toc165892262" 7.2 Principles  PAGEREF _Toc165892262 \h 64  HYPERLINK \l "_Toc165892263" 7.3 Elements of capacity building  PAGEREF _Toc165892263 \h 64  HYPERLINK \l "_Toc165892264" 7.4 Principal actions needed  PAGEREF _Toc165892264 \h 65  HYPERLINK \l "_Toc165892265" 8 Relation of IGOL to other Themes  PAGEREF _Toc165892265 \h 67  HYPERLINK \l "_Toc165892266" 9 IMPLEMENTATION  PAGEREF _Toc165892266 \h 69  HYPERLINK \l "_Toc165892267" 9.1 Strategy  PAGEREF _Toc165892267 \h 69  HYPERLINK \l "_Toc165892268" 9.2 Mapping of IGOL Recommendations to GEO Tasks. (This is in progress and will be completed shortly.)  PAGEREF _Toc165892268 \h 69  HYPERLINK \l "_Toc165892269" 9.2.1 Reports and Meetings  PAGEREF _Toc165892269 \h 69  HYPERLINK \l "_Toc165892270" 9.2.2 Land cover (section 4.1)  PAGEREF _Toc165892270 \h 70  HYPERLINK \l "_Toc165892271" 9.2.3 Land use (section 4.2)  PAGEREF _Toc165892271 \h 70  HYPERLINK \l "_Toc165892272" 9.2.4 Fire (section4.4)  PAGEREF _Toc165892272 \h 70  HYPERLINK \l "_Toc165892273" 9.2.5 Biodiversity (section 4.5)  PAGEREF _Toc165892273 \h 71  HYPERLINK \l "_Toc165892274" 9.2.6 Agricultural production (section 4.6)  PAGEREF _Toc165892274 \h 71  HYPERLINK \l "_Toc165892275" 9.2.7 Soils (section 4.7)  PAGEREF _Toc165892275 \h 72  HYPERLINK \l "_Toc165892276" 9.2.8 Urban/SE (section4.8)  PAGEREF _Toc165892276 \h 72  HYPERLINK \l "_Toc165892277" 9.2.9 Water availability and use (Section 4.9)  PAGEREF _Toc165892277 \h 72  HYPERLINK \l "_Toc165892278" 9.2.10 Topography (section 4.10)  PAGEREF _Toc165892278 \h 72  HYPERLINK \l "_Toc165892279" 9.2.11 Calibration/validation (section 5.1)  PAGEREF _Toc165892279 \h 72  HYPERLINK \l "_Toc165892280" 9.2.12 Data fusion (section 5.2)  PAGEREF _Toc165892280 \h 73  HYPERLINK \l "_Toc165892281" 9.2.13 Data assimilation(section 5.3)  PAGEREF _Toc165892281 \h 73  HYPERLINK \l "_Toc165892282" 9.2.14 Data and product access (section 6.1)  PAGEREF _Toc165892282 \h 73  HYPERLINK \l "_Toc165892283" 9.2.15 Data and information delivery (section 6.2)  PAGEREF _Toc165892283 \h 73  HYPERLINK \l "_Toc165892284" 10 Concluding comments  PAGEREF _Toc165892284 \h 75  HYPERLINK \l "_Toc165892285" 11 Acknowledgments  PAGEREF _Toc165892285 \h 79  HYPERLINK \l "_Toc165892286" 12 References  PAGEREF _Toc165892286 \h 80  HYPERLINK \l "_Toc165892287" Appendix 1 List of acronyms  PAGEREF _Toc165892287 \h 84  HYPERLINK \l "_Toc165892288" Appendix 2 Participants in the IGOL Theme  PAGEREF _Toc165892288 \h 88  Introduction The ecological footprint of mankind continues to grow: every minute, two hectares of forest cover are removed (FAO, 2003); every hour, an average of three species disappear from Earth (UNEP, 2005); in 100 years, the global mean surface temperature has increased by 0.6C (IPCC, 2001); and human co-option of primary production has grown to more than half of total global production. The worlds population currently over 6.5 billion is still rapidly growing, particularly in the least developed countries. The latest population growth projection by the United Nations estimates a further 40% increase in population over the next 50 years growth equivalent to the worlds total population in 1950. Over the last five years an average of some 34 countries were affected by food emergencies every year; 16 million people in Eastern Africa (with over half in Ethiopia) faced severe food shortage (FAO/GIEWS) in 2000 alone. Over the next 50 years increased population and improved living standards are expected to prompt increases in global food demand by 60-70% over 2000 levels. Since there is only modest room for further expansion of arable land area and fresh water supplies per capita are diminishing, future increases in food production to satisfy the growing demand will have to be driven by intensification of land use. The 2000 United Nations Development Goals explicitly recognize that sustaining our future development that meets the needs of the present without compromising the ability of future generations to meet their own needs is a pillar upon which successful development efforts must be built. Changes in our environment are likely to affect quality of life not just by impacting demand for and supply of agricultural products, but by altering controls on water availability, energy supply, ecosystem states and fluxes, human health, biodiversity, and our susceptibility to disasters At the present rate of tropical deforestation, the world's rain forests might conceivably vanish within 100 years with concomitant effects on global climate and terrestrial biodiversity. Successful, sustainable use of natural resources will be crucially dependent on the continuous assessment and monitoring of the status of land resources, how those resources are being used, and the impacts of resource use on future resource availability. Vast quantities of land observations are collected and often used for environmental decision-making, but lack of international coordination and standardization of observations makes country-by-country and region-by-region comparisons difficult, hindering reliable overall understanding of land processes at a global scale. In other cases good observations are scant and decisions are based on expert estimates, or information extrapolated from spatially incomplete data. As a consequence, our capability to identify, assess, and solve environmental problems is still limited by our observational capacity even though several international conventions and programs explicitly require such information. This Integrated Global Observation of Land (IGOL) report provides a roadmap leading from land information requirements derived from the Group on Earth Observations societal benefit areas to data from satellite-based Earths observation systems, their integration with in situ observations, and processing into useful information products. An equally important function of this report is to serve as a mechanism by which feedback from agricultural, forestry and environmental decision-makers is transmitted to operators of Earth observation satellites regarding the characteristics of satellite-based data best suited to applied observational needs. One major challenge in developing the theme is the enormous variety of observations of the land that are regularly made. Therefore a filtering process has been adopted such that only those observations likely to benefit from working within the framework of the IGOS-Partnership are included. Observations must be needed at a global scale or observations are needed locally which benefit from global scale observations; A case had already been made for observations in the documents of the IGOS-Partners and related sources. Considerable efforts have already been made to specify land observations at global scales and these must be drawn upon in preparing the theme. Sources for such requirements will be drawn from the planning documents of GTOS, GCOS, FAO, UNEP, UNESCO, IGBP, WCRP and WMO and reports of international activities they sponsor such as the Millennium Ecosystem Assessment (MA) and GOFC/GOLD. Recommendations for change fall within the remit of IGOS Partners. Observations contribute directly or indirectly to spatially explicit disaggregated data products rather than to country or sub-country units. There is a realistic chance of any recommendations being implemented within the next 10 years. In the report we have tried to distinguish carefully between the needs for improved observations and the products and observations required to satisfy those needs. Hence we discuss in section 2 the various types of needs under the societal benefit areas adopted by the Group on Earth Observations. In section 3 the various types of stake-holders requiring information are outlined followed in section 4 by the relationship of IGOL to other themes agreed to by the IGOS Partnership. In section 5 the products and observations and in particular their enhancements are discussed. Note although some headings are similar between the two sections there is no simple one to one correspondence between the needs discussed in section 2 with the sets of observations discussed in section 5. For example land cover products are needed for every single societal benefit area. In section 6 a number of integration components are outlined followed by a consideration of data and information issues in section 7 and capacity building in section 8. Finally a strategy for implementation is provided in section 9. The needs for IGOL Agriculture Matching food production with the needs of an increasing population while protecting land and water resources is a growing challenge for agriculture. Sound knowledge of the areas on major agricultural crops at country levels is indispensable to major policy decisions concerning sustainable development planning and food security. Satellite remote sensing has contributed to more effective stratification of agricultural land based on its land cover/use, and improvement of sampling designs and surveys. Monitoring development of major food crops by satellite remote sensing enables an objective assessment of crop conditions and more accurate and timely forecasting of crop production. . Accurate information on the area of land used for different types of agriculture combined with forecasts concerning yields helps our policy makers and planners provide farmers in developed and developing nations with a reasonable standard of living, consumers with secure and safe- food supplies at fair prices whilst protecting our environment by avoiding over-exploitation of soil and water resources or avoiding unnecessary conversions of natural ecosystems to agriculture. Food security early warning systems like the FAO Food Security Global Information and Early Warning System (GIEWS) and the USAID Famine Early Warning System (FEWS) enable early identification of developing countries likely to be affected by large scale failures of food crops. Monitoring the growing conditions of selected food crops in major crop producing countries facilitate accurate forecasting of agricultural product supplies. Land observations can also inform strategies for sustainable management of agricultural land and rural development planning. Enhanced observation can provide objective, timely, and location-specific information on land cover and its changes, the state of agricultural crops, and land degradation at country-wide, regional and even global levels. Satellite remote sensing techniques are used operationally for crop production monitoring and yield forecasting in Europe and North America, and these techniques are now being used in other regions of the world, for example through close collaboration with the UN World Food Programme and the FAO; integrated global observations of the land will therefore help in trade, development assistance, humanitarian aid and environmental protection. Forestry In 1992, the United Nations Conference on Environment and Development (UNCED) focused the worlds attention on the alarming state of environmental degradation caused by growing population pressures and short-sighted development strategies. The UNCED Action Plan, the Agenda 21, noted that even the basic information on forests, such as the area and type of forests, is not reliable in many countries and recommended the global periodic assessment of forest resources. These concerns have been further emphasized in the Kyoto Protocol to the UN Convention on Climate Change. The provision of reliable and timely information on forest cover and its changes by remote sensing from Earth observation satellites can support sustainable forest management and policy and can strengthen environmental protection; spatially explicit information on forest change helps judge the effectiveness of forest protection/conservation projects, helps determine compliance with negotiated terms of commercial timber concessions (including issues associated with illegal logging and non-timber forest products), will help in the collection of national forest cover statistics for use in national resource planning/management and provide significant support for national reporting under many chapters of the GHG inventories called for by the UNFCCC (especially those linked to agriculture, grasslands, wetlands, forestry). Providing a neutral basis for verification of carbon trading linked to afforestation, reforestation and eventually also avoided-deforestation projects. Enhanced forest observations using satellite remote sensing can aid in early identification of areas with forest cover change either by natural causes or man-made activities, and can enable more accurate forecasting of the trends of such changes anywhere in the world, regardless of their accessibility or political circumstances. Land degradation Climate warming, changing rainfall patterns, and agricultural intensification (both agrarian and pastoral) in arid, semi-arid, and sub-humid dry zones is resulting in deterioration of soil characteristics, depletion of surface and ground water resources, reduction of land productivity, loss of biodiversity, soil erosion, food shortages, increased poverty, and forced migration of population in affected areas. Observations of the land can map changes to the boundaries of degrading areas by monitoring changes to the biophysical characteristics of the surface - such as changes in brightness and photosynthetic activity as well as the location and condition of key resources such as seasonal water bodies, pastureland, fuelwood and agricultural land. This information is of immediate value to farmers and range managers attempting to deal with land degradation whilst maintaining crop production and rangeland productivity. It is also critical information for governments attempting to implement programs to combat desertification in their countries/regions. Systematic global land observations concerning land degradation zones, rates and dynamics is clearly needed by the Parties to the United Nations Convention to Combat Desertification to help them in formulating appropriate policy responses. Ecosystem goods and services Human well-being is directly dependent upon ecosystems for provisioning of food, water, fiber, fuel, and other biological products, for regulation of disease and water supply, for pollination and waste treatment, and for enriching human existence through recreation and inspiration. Land observations are critical for sustainably managing our ecosystems and the services they provide. As illustrated by the preceding sections, knowledge about the location, amount, and condition of resource stocks in water surface-storage units and in agricultural, forest, and grazing land ecosystems are very important for natural resource decision-making. Observations that enable assessment of the propensity of ecosystems to continue to provide services are at least as important. Fundamental to monitoring changes in ecosystem services are such prime variables such as land cover change, but it is clear that many of the services also require intensive sets of in situ observations at sites and in networks that carry out long term terrestrial ecosystem monitoring. Biodiversity and Conservation The Millennium Ecosystem Assessment (2005) revealed major declines in biodiversity. Protected areas are one of the primary means for preserving biodiversity and natural environments while providing vital services and goods that support peoples livelihoods. Such areas have important intrinsic values as representative of the worlds wilderness and as repositories of outstanding areas of living richness (WCPA 2002). Much progress has been made in establishing protected areas across the globe, so that they currently cover 12% of the Earths surface (WCPA 2002). Despite the seemingly large proportion of the Earths surface designated as protected areas, there are great concerns about the adequacy of existing measures for maintaining these critical ecosystems (Dudley et al. 1999). Some protected areas may just be paper parks because they are not suitably demarcated. Others may not be effectively protected due to lack of adequate financial support or legal power (Terborgh and van Schaik 2002). Among the major threats are poaching, hunting, logging, urbanization, agriculture, mining, and road construction (Dugelby and Libby 1998; Terborgh and van Schaik 2002). Effective and timely monitoring of changes in the land cover within and along the borders of designated protected areas is thus needed to judge their effectiveness in protecting and conserving the regions as planned. Global land observations tuned to biodiversity indicators will be of immense valuable to the Parties to the UN Convention on Biological Diversity in helping them develop, implement and reach the conventions goals. Human health Human health is decidedly influenced by the terrestrial environment through the failure to supply adequate food, the shortage of potable water, and by its impact on diseases and their transmission. Disease transmission if often controlled by vectors like rodents and mosquitoes, whose distribution, in turn, is impacted by land variables. Disease vectors are often most common in transition zones between vegetation types, like that between forest and grassland or within riparian formations and increased patchiness of the landscape in many cases favors disease vector pests. Land cover land use observations are thus important for targeting disease treatments and also for optimizing efforts to eradicate disease vectors. Water resource management The volume of water in surface-storage units (permanent and ephemeral lakes, reservoirs, rivers and wetlands) is determined by atmospheric (precipitation, evaporation-energy) and hydrological conditions (surface-water recharge, discharge and ground-water tables) and critically by water use by humans. The availability of freshwater plays a crucial role in food production and food security and therefore all too often human security as it is already a documented source of conflict. Water resources control grazing patterns and crop irrigation. Irrigated land covers about 20% of the cropland, but contributes about 40% of total food production. Irrigated agriculture accounts for about 70% of all freshwater consumption worldwide and more than 80% in developing countries. In many parts of the world lakes and rivers are key parts of national and transboundary transport/communications infrastructures as well as providing a key source of food through fishing and aquaculture. In order to obtain improved quantitative and qualitative information on irrigated land and available water resources, data on their spatial distribution and change over time are essential. Information on changes in the level, area and even location of water surface-storage units will be of direct use in both short and long-term planning; planning not only for agriculture/aquaculture production, but also for security, for human and animal migrations and for long term climate change adaptation strategies. Disasters The need for observations to support abilities to forecast and mitigate disasters has been considered extensively in the reports of the Geohazards, Water Cycle, and Coastal Themes. In addition we note the increasing importance of wildland fires especially those near the urban interface. In addition to up-to-date weather observations, observations of vegetation condition and fuel loading, sediment discharges, stream flow, and topography would enhance the ability to forecast and manage wildland fires. Land use, land cover, and water use also influence land subsidence and landslides. Better information about land use/land cover in relationship to topography could help identify disaster-prone areas. Land cover is also a critical factor in determining flood risk within major river systems, and up to date information on land cover can play an important part in immediate assessments of relief requirements in the aftermath of major events, such as the 2004 Indian Ocean tsunami. Energy Biofuels including fuel wood, crop residues, biofuel crops, etc. have long been crucial resource and are being increasingly relied upon as a renewable energy resource. Land observations are necessary for assessment of biofuel production and production expansion, and for environmentally sustainable production of biofuels. Efficient siting and impact assessments for wind and hydro power generation also rely upon land observations. Oil and gas exploration and extraction, refining, and transport also rely upon accurate information about land cover and use, soils, and topography. Urbanization: sustainable human settlement This societal benefit area is not included within the GEO plan but represents a vital area of societal benefit since urban areas are increasingly where the human population resides: according to UN predictions, by 2030 60% of the worlds population will live in cities. Although urban areas occupy only c. 3% of the Earths surface, their impact on surrounding rural areas is also rapidly increasing. Urbanization not only concentrates people (and therefore concentrates demand all the social and economic services they require) it also creates hot spots for energy consumption, for natural resource consumption and for emissions of pollutants and greenhouse gases as well as acting as nodes linking communications and transport infrastructure themselves all too often a source of pressure on the surrounding environment. Climate Change Climate determines the distribution of natural vegetation distributions, so changes provide a way to monitor climate change. Land-cover changes also occur because of changes in land management practices and land use type (e.g., agricultural intensification or forest clearance for cropland). Changes in land cover force climate by modifying water and energy exchanges with the atmosphere, and by changing greenhouse gas and aerosol sources and sinks. Global land observations are used in the climate, carbon and ecosystem models which provide predictions and scenarios for use by the Parties negotiating development of the UN Framework Convention on Climate Change, and observations of land variables have to be made by Parties to this convention in order to document their own overall contribution to changes in the Earths atmospheric constituents including greenhouse gas concentrations. Many of the key terrestrial requirements have already been discussed in the Carbon Theme and in the GCOS plans for Essential Climate Variables and hence this document will not duplicate discussion of these requirements. Stakeholders for GLOBAL Land Observations Stakeholders across eleven key domains where global land observations are needed can be grouped into six categories: national, regional or local governments who need the information to assist in the development and implementation of their policies concerning each of the domains and national, regional or local governments who need the information to help them meet mandatory reporting requirements resulting from such policies; international initiatives helping countries develop and fund programs linked to all eleven domains, who need the information for the development of their policies and operational strategies and to direct the utilization of their resources; non-governmental organizations, who are either lobbying for particular policy directions or who are directly acting in the various domains; scientists and research teams, who need the information to improve our understanding of the processes and uncertainties associated with each of the eleven domains; the individual citizen, who should be able to access understandable, reliable information on global environmental trends; and the private sector, who need the information, or generate the information, to help them either partner or directly service the previous five categories. Governmental stakeholders Government, whether at local, regional or national scale, is key to the policy-driven use of global land observations. Governmental departments set the policy agenda. They program funding cycles to advance these policy agendas, identify specific projects associated with them and implement the projects. They also usually monitor the progress of such projects and often too perform some level of post project evaluation. Land observations play a role at all stages, with the particular nature of the governmental intervention determining the form this takes e.g. trends which set a particular policy priority, maps of pre-project conditions which help establish terms of reference for a project, statistics documenting rates of change during the lifetime of the project through to reports and environmental profiles, which confirm that the projects goals have been met. As part of the policy setting agenda governments have also generated a second stakeholder role for themselves, namely that of having to generate land observations information as a result of their own policy agendas. For example, almost all the countries of the world established and signed the UN Framework Convention on Climate Change at the Rio Summit in 1992 with the policy objective of reducing global warming and coping with whatever temperature increases are inevitable. As a direct result these same governments are now obliged to report various pieces of information related to land observations to the UNFCCCs Secretariat. And the UNFCCC is only one of many such Multilateral Environmental Agreements generating reporting obligations. Thirdly governments are a key stakeholder as it is government-funded agencies (Space Agencies, Environmental Protection Agencies, Hydrological service and the like) that provide much of the nascent capacity for global land observations - capacity from Earth observing satellites and in situ measurements alike. International initiatives Key international stakeholders include organizations that make up the UN System; FAO, UNEP, WMO, UNESCO and UNDP, among others, facilitate and promote international cooperation in order to promote respect for human rights, protect the environment, fight disease and reduce poverty. Their work involves setting standards for observations, coordinating observing networks, gathering and collating information, and of course analyzing the results. As well as a source of observations the UN Systems organizations are a significant user; global land observations, whenever available, are already used to help them develop their policy positions and their operational strategies, especially helping direct the use of investments for development purposes, such as the Global Environment Fund, UNDP grants and World Bank programs. Furthermore selected UN System organizations alongside the Intergovernmental Oceanographic Commission and International Council for Science sponsor the Global Terrestrial, Ocean and Climate Observing Systems (GTOS, COOS and GCOS). These three bodies are also important stakeholders as they provide advice on needs, gaps and future developments of observations as required by the UN System, the multilateral environmental agreements (such as UNFCCC, UNCBD, etc ) and associated scientific entities (such as the Intergovernmental Panel on Climate Change) and key entities such as the World Conservation Union IUCN. NGOs Global NGOs such as Greenpeace, Birdlife International and the Rainforest Foundation and more localized entities are stakeholders that use global land observations to bring additional voices to the policy table, and who implement many projects/actions on the ground. Policy formulation, project planning, execution and evaluation in the non-governmental world also rely on accurate information. Science Global land observations are vital to improved scientific understanding of key biogeochemical cycles, for further scientific development of climate (and weather) models, and to establish scientifically based certainties such as agreeing on global rates of deforestation, biodiversity trends, rates of desertification etc. Scientific requirements for terrestrial observations have long been articulated especially at the international level by IGBP, IHDP, Diversitas WCRP, and the Global Land Project (GLP 2005). Profound changes are occurring in the strategic direction of global environmental research over the next decade with more emphasis on issues of societal concern, more emphasis on regional scales, emphasis not only on climate change but on many other aspects of global change such as human induced land cover and land use change, and a scientific focus on coupled human environmental systems. The scientific community also develops innovative new approaches to the collection (and dissemination) of global land observations and thus as a stakeholder the scientific community is both a user of global land observations and a vital developer/producer of such observations. General Public The general public has an interest in many aspects of global environmental change and indeed political developments reflect this. Although regional (European) in scope the UNECE Convention on Access to Information, Public Participation in Decision-making and Access to Justice in Environmental Matters (The Aarhus Convention) codifies the citizen's participation in environmental issues and provides a legal framework for access to information on the environment held by public authorities. Through these sorts of access initiatives the citizen becomes a de facto stakeholder in any global land observing strategy. Private Sector The private sector has an established role in implementing government, NGO and internationally funded environmental monitoring programs. Their involvement in everything from satellite manufacture and operation to data collection, processing, analysis, education and training places them firmly as a stakeholder. Engagement of stakeholders Early involvement of stakeholders and users of land observations is essential for effective development of applications of land observations from space. In particular, end-users should define their geoinformation requirements, including the types of information, their levels/scales, timeframe, accuracy, and the formats of final products. The usual way of engaging the key stakeholders in active participation in the formulation and implementation of satellite remote sensing data applications projects and other related initiatives, is through their membership in the advisory boards established for such initiatives. Their involvement in decision-making process will assure that they understand the benefits to be obtained from the effective application of Earth observation technology to sustainable development and management of land and water resources, and become its early users. Products and observables On the basis of the needs articulated in section 2 and the requirements of serving the range of stakeholders outlined in 3 the following main classes of observations can be recognized. Land cover Land use Biophysical properties relating to ecosystem dynamics Biodiversity Agriculture Forestry Soils Human Settlements and Socio-Economic Data Water Availability and Use Topography Note that specific needs of terrestrial case-holders often require multiple observations so there is no simple one to one match between user requirements and sets of observations. Land cover Land cover refers to the observed biophysical characteristics on the Earths surface. This definition of land cover is fundamental and land cover should not be confused with land use as happens in many existing classifications and legends. Land cover is not only important in its own right but is vital in the estimation of many other terrestrial characteristics such as land use and properties relating to biodiversity and conservation and many other ecosystem services. Land cover information is also important to policy and decision makers relative to changes in land cover areas and conditions associated with key ecosystem services. In addition, land cover information provides critical information to hydrological and atmospheric drivers associated with biophysical properties associated with various land covers. Observation needs and technical requirements There are numerous local, national and even regional land cover products, though most of these are not regularly updated. At a global scale there is no true operational program, though several research groups have created global land cover products. Estimation of land cover change is even less well developed. There is therefore a key requirement to move from research to operational monitoring capabilities for land cover with operational data and product suites that are better defined, flexible, and openly available. Related implementation requirements are (Townshend and Brady, 2006): coordinated and consistent land cover data acquisition, both from satellite and in situ observations; polar orbiters that will provide status data on the current extent of various land covers, with known and reported validation estimates; coordination of various international satellite assets for land cover monitoring at both fine and moderate resolution; a focal point for international inquiries for both raw data and derived products through online data and information systems; standardized mapping and derivation land mapping products; harmonization and synergy of existing land cover/forest maps; rigorous validation of map products using internationally agreed procedures; improved match between data, data products and user needs, i.e. ensure adequacy and advocacy to serve international conventions; analysis, understanding and modeling of land change and spatial-temporal change processes; and a supportive data policy especially as it relates to costs and copyright. Two main classes of products have been identified (GOFC-GOLD,1999): those with moderate resolutions of 250m-1km, and those with fine resolutions from 10-50m, commonly known as Landsat-class observations. The former set of observations provides data at resolutions at least adequate for most modeling purposes and they can also be used over five year periods to flag the location of the most significant areas of land cover change. However for reliable monitoring of change the finer set of resolutions are essential. There are situations where even finer resolutions are needed, for example Kyoto-implementation is based on 0.05 ha and urban areas require ultra-fine resolutions of < 1m. However these are not true global wall-to-wall requirements. Resolution classSpatial resolutionExamples of sensorsCoarse resolution> 1 kmAVHRR GAC dataModerate resolution250m-1kmMODIS, MERISFine resolution10m-50mLandsat TM, SPOT-HRV, IRS, CBERSUltra-fine resolution <4mALOS, Quickbird, IKONOSTypes of sensors in of spatial resolution as used in this report.. Currently terminology is very confused with some referring to Landsat TM as a medium resolution instrument and others referring to it as a fine resolution instrument. The frequency with which change is monitored also needs to be established. Moderate resolution imagery is regularly acquired globally by several systems, but except for unusually large land cover changes, such imagery is inadequate for reliably measuring the area of change. Fine resolution imagery is sufficient to measure many non-urban changes in land cover and proposals have been made to monitor change with a five yearly interval. However in some parts of the Earth such as tropical forests and some temperate ones, such a frequency is not adequate to capture the dynamics of anthropogenic change and more frequent imaging may be needed. Additionally it should be noted that reporting of statistics by most countries is on an annual basis, so a future goal should be annual reporting of land cover change globally. Certain phenomena, such as fires (see next section), and areas, like wetlands, may require daily monitoring to fully capture their dynamics. Current status Within the last few years, large volumes of high-quality global remotely-sensed data have become available, provided by such orbiting instruments as SPOT-Vegetation (CNES, 2000), MODIS (Justice et al., 1998), and MERIS (ESA, 2004), leading to land cover products typically presented as a digital thematic map in raster format with pixels in the range of 500-1000 m. Thus far, global land cover maps have been constructed using data from AVHRR for IGBP (Loveland et al., 2000), SPOT-Vegetation for GLC2000 (Bartalev et al., 2003), and MODIS Land Cover since 2000 (Friedl et al., 2002), and future maps are also planned from MERIS (Arino et al. 2005) and NPOESS system on a quarterly basis (Townshend and Justice, 2002). Finer resolution data are needed for larger scale products. The most commonly used remote sensing observations are those of Landsat and SPOT-HRV. Reduced coverage of the former during the last three years has had a serious impact on our ability to map land cover. Regional- and continental-scale efforts exist such as Africover (FAO, 2004), CORINE in Europe (EEA, 1995), and MRLC2001 in the United States (USGS-EDC, 2003) reaching scales of 1:25,000. With reference to the LULUCF Good Practice Guidance (IPCC 2003) the smallest measurable mapping area of 0.05 ha is required, translating to 10 m pixel resolution. The main source of ultra-fine resolution data is from commercial satellites, though with the launch of ALOS by JAXA, 2.5 m panchromatic stereoscopic data from the PRISM sensor is becoming more widely available for scientific and other users. One of the main uses of such data for land cover mapping is to provide validation data for coarser resolution products. Current plans A long term commitment of funding from individual countries and agencies is essential to provide continuity and consistency on all observation scales. US systems like MODIS, AVHRR, and upcoming NPP/NPOESS along with European systems from SPOT VEGETATION, ENVISAT MERIS, and upcoming Sentinel 3 provide and will provide quality data for coarse to moderate scale land observations. Fine resolution land mapping has widely relied upon sensors like Landsat TM/ETM (US), SPOT (France), ERS 1+2 and ENVISAT-ASAR (ESA), and IRS satellites (India). Future systems will include ALOS-PALSAR (Japan), TERRASAR-X and RapidEye (Germany) and other national satellite programs (e.g. India, Russia, China, and Korea). However, there are strong concerns about the continuity of long term fine resolution land observations. Data from the next Landsat is unlikely to be available for at least four years and data from ESAs Sentinel program will take a similar length of time. Currently there is heavy reliance on the aging Landsat 5 and regular global data from this system are not available. The Landsat Data Continuity Mission (LDCM) planned by the US and the Sentinel series funded by Europe will provide the necessary continuity for Landsat-SPOT type of data beyond 2011. The European Sentinel-2 should have a swath of almost 300 km a systematic acquisition of all land surfaces with a revisiting period of 10 days at a resolution ranging between 10 and 60 meters in 12 bands providing radiometric continuity with previous missions, including SPOT-5. For the future it is recommended that space agencies coordinate their efforts in relation to choice of orbit such that fine resolution optical data are acquired with an increased frequency. The European Sentinel-1 should ensure the continuity of SAR data started with ERS-1 in 1992 and currently ensured by ERS-2, ENVISAT and RADARSAT-1 in C-band. This continuity will also cover SAR interferometry useful for DEM building as well as impervious area determination in urban areas. Finally the NPOESS-VIIRS and European Sentinel 3 should ensure the continuity of the MODIS, VEGETATION, MERIS, and (A)ATSR instruments respectively. Major gaps and necessary enhancements One of the key issues for many types of observations is in ensuring that acquisition strategies are optimized in time and space. An example is the Long Term Acquisition Plan (LTAP) of Landsat 7, which ensured for the first time in the 25-year history of this program that global, seasonal coverage of fine-resolution data were collected. The expected interruption of satellite remote sensing with Landsat ETM and SPOT series data will have adverse effects on study of land cover dynamics. The main advantage was the combination of 30m ground resolution with large area of image scenes (170x185 km). Most land cover maps at 1:50 000 scale were based on TM/ETM data. It is therefore recommended to minimize the interruption of fine resolution type of remote sensing coverage. In the medium term until the new assets described below become available it is recommended that space agencies coordinate fine resolution acquisitions so that an approximation to the Long Term Acquisition Plan of Landsat is duplicated. Radar sensors have particular value in areas with very high cloud amounts: a SAR with L-band frequency is particularly suitable for monitoring tropical forests, due to its sensitivity to above-ground biomass. One key issue which deserves attention is the coordinated acquisition of data from radar systems and optical systems for the purpose of land cover monitoring. It is clear that many areas can only be observed very infrequently using optical data because of high cloud cover. The location of such areas should be used to define the acquisition strategy for radars so that regular global monitoring of land cover can be achieved. The main obstacle to the interoperability among existing land cover databases has been the lack of an internationally accepted land cover classification system. The Land Cover Classification System (Di Gregorio and Jansen, 2000), which has been successfully used by several land cover projects at global, regional and country levels and adopted by the former LUCC project and the current Global Land Program, should be adopted as its classification standard for land cover mapping. Calibration and validation issues related to fine scale in situ observations to verify coarser scale satellite mapping remain as challenges. Greater effort is needed to provide coordinated and more standardized information of in situ observations. International cooperation is needed to make such data accessible and usable in an international context. Strahler et al (2006) have provided an outline of the procedures needed to validate moderate resolution land cover products. Product-specific critical issues The European initiative Global Monitoring for the Environment and Security (GMES) which is the European contribution to GEO is currently scaling up three services based on institutional requirements that use land cover information at a certain stage of the service. These three ESA projects are GMES Service Element (GSE) Land, GSE Forest Monitoring and GSE Flood and Fire have been running since 2003 and are delivering operational services to European users. A fourth GSE project, Global Monitoring for Food and Security (GMFS) focuses initially on African countries. In addition, the European Commission is putting in place the first elements of a GMES Land fast-track service. In the initial phase this will concentrate on Europe, generating a new version of the CORINE land cover map which will include a very high resolution (1 m) urban layer. This builds on the pre-operational land cover monitoring services implemented by the ECs GEOLAND project. The following are regarded as the highest priority product-specific issues relating to land cover. These formed a key component of the terrestrial section of the GCOS Implementation Plan, which has been endorsed by the Parties to the UN Framework Convention on Climate Change, and has been adopted by GEO as part of the GEOSS implementation plan concerning climate change. commit to continuous 10-30m resolution optical satellite systems with data acquisition strategies at least equivalent to the Landsat 7 mission for land cover data as an essential component of an integrated and operational terrestrial observation strategy; develop an in situ reference network and apply CEOS-Cal-Val Working Group validation protocols for land cover; generate annual products documenting global land-cover characteristics at resolutions between 250m and 1km, according to internationally-agreed standards and accompanied by statistical descriptions of the maps accuracy; generate maps documenting global land cover at resolutions between 10m and 30m at least every five years, according to internationally-agreed standards and accompanied by statistical descriptions of the maps accuracy (as noted above more frequent imaging is required regionally); a longer term goal should be annual monitoring. ensure delivery of information to users in an appropriate format. Principal recommendations Develop acquisition strategies for land cover data that optimized coverage in time and space. Minimize interruption of fine (30m) resolution data. Ensure future continuity of fine resolution multispectral and SAR L-band data. Coordinate radar and optical data acquisition so that radar data can be used for regular, global monitoring of land cover. Agree upon an internationally accepted land cover classification system. Coordinate international collection of in situ data for calibration/validation efforts. Land use, land use change Land use is defined as the arrangements, activities and inputs people undertake within a land cover type to augment, enhance, change or maintain it (GLP 2005). Land use is distinct from land cover in that specific use characteristics are associated within a land use category, whereas a land cover may be used for a variety of activities or purposes. Characteristics related to the intensity, extent and duration of land use activities provide additional information to distinguish various properties associated with a land use. This information provides an indication of the impact on land surface properties, biophysical and biogeochemical fluxes, and linkages to ecosystem services. Land use characterization is needed for evaluation of land resource productivity (e.g., wood production, crop production, etc.), decision making associated with land management options, and for implementation of policy. The Global Land Project identifies the key needs for Land Use products. There is an urgent need for land use maps, especially at global and regional scales. Currently, most global mapping products are land cover classifications, with land use categories limited to cropland, pasture and urban. Land use information is needed to document the extent and intensity of anthropogenic activities on the land, including cropping systems, irrigation, fertilisation, crop yields and livestock density. Although available at the administrative level, such data are not always compatible between different countries, and are not always in a spatially explicit format suitable for ecosystem modelling. Data harmonisation and gridding are therefore often required. Observation needs and technical requirements Land use is not always readily apparent from visual inspection and can change quickly, so monitoring land use is more challenging than monitoring land cover. Several sources of optical remotely sensed data (fine resolution broad area coverage such as Landsat, Terra, IRS, ResourceSat, CBERS, DMC satellites, and ultra-fine resolution such as Ikonos, Quickbird, Orbview, Eros) have been used routinely to characterize selected aspects of land use. However, many aspects of land use are not amenable to remote detection. For example a comprehensive understanding of agricultural land use requires information on management inputs, including the technologies used, the timing of interventions, the products and services generated, the location and spatial extent of different land uses as well as the socio-economic context. But multispectral data allows discrimination between many crop types and ultra-fine resolution data allows land use types such as olive plantations to be identified It is evident from the above requirements that in situ observations are essential for fully characterizing agricultural land use. However, in situ surveys are costly. Thus, depending on the particular development issue being tackled, the spatial extent of the area of interest, and budgetary constraints, the information from less-costly remotely sensed imagery are used to complement limited in situ observations. These practical considerations strongly suggest that an emerging area of interest/opportunity for IGOL is the development of cost-effective survey designs involving combinations of remotely sensed and in situ measurements to meet the information requirements of national development issues (including obtaining reliable agricultural land-use statistics) at various scales and covering all types of land use (i.e. integrated land-use surveys). Already in many parts of the European Union operational crop production forecasting and yield estimates are regularly made. Current status Comprehensive well validated global land use maps are currently unavailable. Many products purporting to depict land use in fact show land cover. Key land use characteristics have been mapped such as cropland extent, grazing land extent in built-up land and the distribution major crops extent for the early 1990s by the Center for Sustainability and the Environment at the University of Wisconsin. A digital global map of irrigated areas is available through the University of Kassel, which was developed with contributions from FAO (AQUASTAT) in raster format with a resolution of 0.5 degree by 0.5 degree and the percentage of each 0.5*0.5 degree cell that was equipped for irrigation in 1995 (George and Nachtergaele 2002). At a country level, many countries carry out annual and periodic national agricultural surveys (including decennial agricultural census) and FAO, as part of its mandate, collects agricultural data, including land use data, from all countries, though for many developing countries the accuracy may be relatively low. There is no definitive universally accepted land use classification. The LCCS has been increasingly widely adopted but even within the FAO alternatives are used. Overall very few global databases containing land-use information exist. Currently available maps suffer a number of shortcomings including limited number of classes, non standard definitions, and insufficient information on management aspects. Similarly, comprehensive land use maps with national coverage do not exist for most developing countries. Current plans Current plans to generate improved global land use maps remain fragmented and there are apparently no funded activities to provide improved global land use products. Within developed countries land use maps are frequently produced (George and Nachtergaele). Notable regional efforts for the developing world include Africover providing maps mainly for East Africa. Plans to carry out similar work in West Africa are underway. However building consistent/ harmonized global datasets by compiling separate national datasets requires prior development of a land use correlation system. International organizations and other entities should support the development and validation of such a system. Two other institutes redistribute FAOSTAT national production figures into 5 min grid cells by using land cover and Global AEZ information which allows associating suitable biophysical conditions for specific crops with crop distribution in each cell. IFPRI has produced a Beta version which gives for each grid cell the presence of the twenty most important crops. IIASA has produced for each grid a distribution of 7 land use classes: forests, pasture, open water, rainfed cropland, irrigated cropland, barren land and urban land. This database will be released before the end of the year as part of GAEZ-2007. Further details on agricultural land use monitoring are provided in section 4.6.2.1. Spatially explicit information on land use changes related to forests will be gathered for FAO's next Global Forest Resources Assessment to be completed in 2010. This will involve the establishment of permanent sample plots at each one-by-one degree latitude/longitude intersection, the interpretation of Landsat and other remote sensing imagery for each of these for different points in time (1975-1990-2000-2005) supplemented by auxiliary information - including local knowledge and information from field sampling - in order to transform the first step land cover classification into a land use classification. Special emphasis will be placed on the land use change processes related to forests. Major gaps and necessary improvements The extent to which spatially explicit information on land use can be provided remains unclear because of the relatively coarse level of aggregation at which land-use aspects can at present be reliably inferred from remotely sensed imagery. The frequency with which land use needs to be monitored in order to assess land-use change would vary depending on local conditions. Some designated-use areas (e.g. protected areas) may change slowly and land-use information for such a location need only be updated at relatively long intervals. However, in other jurisdictions where enforcement is ineffective, protected areas may be subject to unauthorized land uses, monitoring of change on a relatively frequent basis would be a necessary pre-requisite for corrective action. Some small-scale global applications require maps with only broad land-use characterization. For example the upper level classes specified in the IPCC good practice guidelines (the basis for the consistent representation of land areas) include only forest land, cropland, rangelands/pasturelands, wetland, settlements, and other land. These classes may reliably be inferred from satellite imagery. Information needs may therefore be met using data from existing observation systems, several of which were cited earlier. Potential major constraints, if high spatial detail is required, are the cost and time for image interpretation. In general, such small-scale global maps should be updated every five years or more frequently in regions of rapid land-use change. As stated earlier, for applications at national to sub national scales requiring information on land management aspects, both remotely sensed and in situ observations are necessary. For cases where only statistical estimates of the various land uses or of land-use changes are needed, these could be met using appropriate sampling strategies. In this regard, high-resolution (<1m) imagery would be needed to support the in situ operations (e.g. field orientation, data collection, planning, etc.). As for global applications, a desirable frequency for repeating observations is five years, except in zones of rapid land-use change. The following are the preliminary steps need to create a global land use data base. A widely acceptable legend needs to be agreed upon. The LCCS provides a useful start but effort needs to be directed towards gaining consensus on it from all stakeholders including the various new burgeoning scientific activities of the ESSP including the GLP and GECAFS. The legend should be relevant to viability of short- and long-term land uses and also to land potential and sustainability. It is recommended that any legend needs to include a measure of intensity of land use. It should be noted that Harmonization of Land Use classes from diverse sources remains very challenging (Jansen 2005). Nearly all land on the planet is used in some way, but land use intensity remains low in many areas. The land surface should therefore first be stratified into areas of low and high intensity use, based on published sources with the use of widely available data sets such as Landsat. The first would largely include intact forests, desert areas and ice sheets. Within the areas of high intensity use, map the following readily observable categories using fine resolution data (likely Landsat given global coverage of free data): mechanized agriculture, pivot irrigation and other readily observable irrigation types, tropical plantations and areas deforested for agriculture and husbandry, urban areas and infrastructure (including roads dams and powerlines). Use ancillary information available at sub-country levels on crop production, livestock densities and fertilizer use to refine land use discrimination using the spatially explicit information to spatialize the information. Using the above, identify residual areas where land use characterization has not been possible and develop an approach based on finer resolution data and in situ knowledge. Product-specific critical issues Filling gaps in available land-use information and addressing issues of data discontinuity and lack of standardization among existing data are high priority, especially for regional- or global-scale assessments. Similar land-use data are often collected for different reasons, making intercomparison challenging, time consuming, or even impossible. Moving towards broad data collection and uniform collection and processing standards for both remotely sensed and in situ data would lower data barriers to broader-scale assessments and improve transparency of documentation and certification for international agreements. In addition, fruitful exchange of land use data requires clear descriptions of methods, implicit assumptions, and database limitations. Principal recommendations Develop a widely accepted land use classification system that is relevant to viability of short- and long-term land uses and also to land potential and sustainability and stratified by low and high land use intensity. For intensively used areas, map at 1:500,000 scale mechanized agriculture, pivot irrigation, tropical plantations, areas deforested, and urban areas. Integrate remotely sensed and in situ information to map crop production, livestock densities, and fertilizer use. Biophysical properties relating to ecosystem dynamics Direct observations of the changes in ecosystem characteristics associated with states (i.e., biomass pools) and fluxes (i.e., material exchanges associate with harvest, aerosols, erosion and gaseous emissions) are observed at multiple scales from in situ to remote sensing observations. These observations are applicable to all ecosystems from terrestrial to freshwater systems, from human-dominated to natural ecosystems (for instance urban, forest, grassland, savanna, wetland, and aquatic ecosystem types). Spatial and temporal characteristics, associated with ecosystem pattern and development, are also being affected by human-activities and climate change so that fragmentation of ecosystems and changes in the pattern of succession are being altered. A special class of observations is associated with ecosystem services. These can be derived from integration of a number of observations of ecosystem properties and human activities. These services include provisioning, supporting, and regulating services associated with natural and human-dominated systems. For instance, provisioning of food production can be derived from the association of land cover to land use and levels of productivity; regulating water quality can be derived from integrating land and water use, intensity of human activities, characteristic of land cover fragmentation, and availability of water resources; and supporting of soil fertility can be deduced from intensity of land use, soil physical-chemical properties, nutrient and organic matter management, and stability of landscapes. Observation needs and technical requirements Key observations related to the state ecosystems include: species composition; vegetation structure, height, and age; net primary productivity; net ecosystem productivity; spatial pattern of ecosystems; biomass estimates of vegetation, soils, and anthropogenic stocks of C and N; and spatial patterns associated with a mixture of land cover types (e.g., landscape pattern, fragmentation, integrity, coherence, etc). In addition, temporal observations provide a way to estimate seasonal dynamics or phenology of ecosystem properties from which productivity can be inferred, disturbance events associated, periodicity of inundation, frequency (e.g., seasonal and inter-annual manipulations) of large scale human modification of ecosystem structure, and ecosystem recovery and age from disturbance can be characterized. Many aspects of ecosystem dynamics are not directly observable and need to be estimated by integrating various in situ, survey, and remote sensed information and repeated observations to derive these products. Data-model fusion is needed to estimate these derived products from remotely sensed data coupled with in situ observations to provide estimates of ecosystem dynamics useful for agricultural needs (e.g., forestry, cropland, and rangeland productivity), vegetation recovery and succession, and exchanges of key vertical and lateral fluxes. Current status Remote Sensing The growing range of Earth observation satellites with optical and radar remote sensing systems, improved spatial and spectral resolution of satellite images and higher frequency of coverage have greatly enhanced the operational use of satellite remote sensing in forest mapping and monitoring. For example, the multispectral, moderate resolution (250m 1km) image data from the TERRA and AQUA MODIS remote sensing systems, which have been available since 2000, or SPOT-VEGETATION, (A)ATSR, or MERIS are compatible with forest and other land cover mapping at global and regional scales. New generation of satellite SAR systems, the ALOS-PALSAR (L-band), launched in January 2006, and Radarsat 2 (C-band) to be launched in early 2007, will be particularly useful for monitoring of tropical forests where reliable information on forest cover changes is difficult to obtain because of clouds. Vegetation monitoring data are operationally available on a global scale based on NDVI, Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR), with ground resolution of 250 meters to 1km (Gobron et al, 2005). Multispectral remote sensing data are the main inputs for forest and other land system change mapping and monitoring, SAR data are increasingly used for ecosystem characterization and change monitoring in areas with frequent cloud cover, such as in humid tropical zones, because they can be recorded day-and-night, in all weather conditions. In situ observations Current networks of inventory, biological census, and flux data associated with regular collection of repeated sampling (forest or crop census data) and recent continuous point sampling (e.g., FLUXnet) provides enhanced measurements of ecosystem processes related to information important for forest or grazing land management, net primary productivity and net ecosystem productivity, nutrient, and long-term ecosystem and landscape dynamics (Gobron et al, 2006). Most countries with significant forest industry have information from which key forest parameters can be estimated, within constraints such as those mentioned above. In practice, more accurate and reliable biomass estimates may be made if the inventory is based on a sample of plots re-measured at regular intervals, rather than re-mapping (usually from aerial photographs) with limited field sampling - particularly if the sampled sites vary between the inventories. Major gaps and necessary enhancements Remote Sensing The use of moderate resolution high temporal frequency multispectral sensors such as MODIS and MERIS has shown their ability to estimate several key biophysical properties, but their moderate resolution is too coarse to resolve individual landscape elements especially in managed ecosystems. To overcome this, the remote sensing of biophysical observations needs enhancements. A remote sensing capability with fine spatial resolution data with the spectral properties and temporal resolution of moderate resolution instruments will ultimately be required for such applications. Key to documenting forest structure and advancing understanding of the functioning of many ecosystems is information on the vertical arrangement of components of the canopy. Active optical technologies also show considerable potential for this key variable. The use of lasers from aircraft shows considerable potential for these observations. Continued development of these technologies to allow deployment on a satellite of a canopy lidar is strongly encouraged. Multi-angular optical remote sensing systems, such as MISR, are also showing great potential for extraction of information concerning canopy heterogeneity (Diner et al, 2005). The saturation of radar backscatter alone at higher levels of biomass is a known limitation of these radar technologies. However, advanced SAR technologies, i.e. integration of multi-temporal observations (Kurvonen et al.,1999), interforemetric SAR using C-, L- and P-band (e.g. Santoro et al.,2002; Askne et al.,2003; Wagner et al.,2003) and very high frequency SAR, though limited to airborne sensors (Fransson et al.,2000), have proven further potential for forest biomass mapping up to at least 200 m3/ha (Santoro et al., 2002; Santoro et al.,2006). The full advantage of SAR remote sensing (i.e. cloud-free global coverage) should be ensured through providing appropriate satellite observations. These include the long-term continuity and global availability of existing SAR data records in C- and L-band including interferometric capabilities, the establishment of combined short- (X- and C- band) and long-wave (L- and P-band) Radar observations in multiple polarizations (including cross-polarized or full-polarized) and in interferometric mode. Such data are of particular importance for forest mapping (structure, height, biomass) and timely agricultural monitoring. There is a strong need for better synergistic use of SAR data products with passive and active optical remote sensing approaches in the context of vegetation monitoring. In Situ observations Development of standards for in situ observations and data exchange and common data protocol for reporting in situ observations of ecosystem properties and dynamics across a gradient of human-affected ecosystems are needed for regional to global integration and interpolation of inventory data, census data, and other socio-economic information. More generally, detailed characterization of emissions, both fossil/anthropogenic and natural, is required for multiple species (e.g., source isotopic or stoichiometric ratios for fuels and for terrestrial ecosystem sites) if these are to be used as additional inversion constraints. Specific information is required on the timing and location of the emissions, their measurement uncertainties and, where gridded or generalized data are provided, the horizontal resolution and covariances of the uncertainties are needed. More spatially and temporally detailed emission data products should also be prepared based on statistical reports within countries. Enhanced observations of ecosystem dynamics associated with vegetation and faunal changes are needed from long-term observations. Disturbance events associated with insect and disease outbreaks, storm, droughts, and other events causing structural and flux changes need enhanced measurements of ecosystem characteristics. TEMS, the Terrestrial Ecosystem Monitoring Sites database, is an international directory of sites (named T.Sites) and networks that carry out long-term, terrestrial in-situ monitoring and research activities and is operated by GTOS. The site provides information about sites but provides no direct access to data sets themselves. GTOS/TEMS has recently completed an agreement with EcoPort RSA aimed at demonstrating the potentiality of a close collaboration between the two systems. EcoPort is a wiki-like database containing inter-disciplinary information about biodiversity. Individuals can add information such as pictures, documents, links to the entity (record) of interest and contribute in this way to create knowledge by integrating data in a communal database. The International Long Term Ecological Research (ILTER) consists of networks of scientists engaged in long-term, site-based ecological and socioeconomic research. Its mission is to improve understanding of global ecosystems and inform solutions to current and future environmental problems. ILTERs ten-year goals are to: 1. Foster and promote collaboration and coordination among ecological researchers and research networks at local, regional and global scales 2. Improve comparability of long-term ecological data from sites around the world, and facilitate exchange and preservation of this data 3. Deliver scientific information to scientists, policymakers, and the public and develop best ecosystem management practices to meet the needs of decision-makers at multiple levels 4. Facilitate education of the next generation of long-term scientists. These laudable goals are not surprisingly taking a substantial time to realize. ILTER was based on the US Long Term Ecological Research Network. Plans are now under way to develop The National Ecological Observatory Network (NEON) which is a continental scale research instrument consisting of geographically distributed infrastructure, networked via state-of-the-art communications. Cutting-edge lab and field instrumentation, site-based experimental infrastructure, natural history archive facilities and/or computational, analytical and modeling capabilities, linked via a computational network will be funded. It is intended that NEON will transform ecological research by enabling studies on major environmental challenges at regional to continental scales. Scientists and engineers will be able to use NEON to conduct real-time ecological studies spanning all levels of biological organization and temporal and geographical scales. Data from standard measurements made using NEON will be publicly available. The high costs of its implementation mean that it is currently an unlikely model to introduce into developing countries. Model improvement Given the independent nature (not fitted against flux data) and the simplicity of the MODIS-GPP model, its overall performance in predicting GPP is remarkable under normal conditions (r2 between 0.7 and 0.95). The assimilated meteorology does not capture all day-to-day variation, but matches the local tower data well on an eight-day scale. However, at certain sites the meteorological bias influences estimates of GPP significantly. Furthermore, there is potential for considerable improvements of the GPP algorithm by better accounting for soil drought effects, by reducing the radiation-use efficiency under high-radiation conditions, and by introducing more geo-biological variability. It has been shown that these parts of the MODIS-GPP algorithm can be re-parameterized using eddy covariance data, so the synergistic use of MODIS and C Flux data will improve the ability of a global terrestrial observation system. Processes in terrestrial ecosystems exhibit high variability in time and space, and their local to regional impact is of interest for a variety of policy and economic reasons. Furthermore, from the perspective of the global carbon cycle we are interested in ecosystems aggregate impact on the atmosphere. Spatial variability is high enough that measurements alone cannot provide adequate estimates of fluxes (or changes in stocks) over large regions, implying that models must be used in the interpolation of local observations. Yet, without regional wall-to-wall observations, such models cannot be convincingly evaluated because of an incomplete sampling associated with in situ measurements. Spatial/gridded in situ data sets are therefore needed for several reasons: as input to models, as constraints for model dynamics and parameterizations, and for verification of model results. Issues related to data fusion and data scaling methods need to be dealt with in model calculations so that a transparent methodology is available for review, ease of updating, and assessment of uncertainties or error analysis. Principal recommendations Global fAPAR products from 1997 onwards have been generated by space agencies and other data providers (e.g., ESA, NASA, ECs JRC, etc). These products are typically available at a spatial resolution of 12 km, daily, weekly or monthly. Finer resolution products, at 250 300 meters can be generated but are not available operationally on a global and sustained basis. The latter would offer significant improvements in terms of national or regional scale reporting on the terrestrial carbon sink, or as one input in the generation of land cover maps. The higher resolution products are also easier to compare with the point measurements made at reference sites. Space agencies and data providers should continue to generate gridded fAPAR and LAI. Reprocessing of available archives of fAPAR and LAI to generate and deliver global, coherent and internationally agreed values. Further efforts should also be made to re-analyze the historical archives of NOAAs AVHRR instrument, ensuring the long-term consistency of the product with current estimates throughout the entire period. CEOS Working Group on Calibration/Validation should continue to lead international benchmarking and product intercomparison and validation exercises including fAPAR and LAI. These efforts should take full advantage of existing networks of reference sites for in situ measurements whenever possible. Fire The need exists for satellite information on fires since fire changes the surface cover type and properties and releases trace gases and particulate matter into the atmosphere, affecting ecosystem functioning and composition, hydrological processes, atmospheric chemistry, air quality and climate. Fire is an important ecosystem disturbance with varying return frequencies, resulting in land cover alteration and change on multiple time scales. Fire is a widely used land management tool and in tropical, temperate, and boreal regions and is an indicator of land use change and human activity (Mollicone et al 2006). Fire is used for clearing and preparing of agricultural land, maintaining pastures, hunting and removing crop residue. Fire can also have adverse impacts on human health, livelihoods and economies. Wildfires have become increasingly a significant hazard at the suburban-wildland interface. Fire observations are needed by land and environmental managers, including those organizations responsible for the management of protected areas, global change researchers and for national and international assessments. Observations provide information at various stages in the evolution of fire events; for fire early warning of fire prone conditions, for early fire detection, tactical and strategic fire management, post fire assessment and monitoring the impacts of fire events and fire management policies. Satellite derived fire information can be used for improved fire and land management., For instance, hotspots derived by A(A)TSR observations since 1995 are posted in Near Real Time by ESA and in the World Fire Atlas (Arino et al, 2005), to-date providing information to more than 800 registered users. Requirements for fire observations have been developed at the international level by the GOFC/GOLD Fire Implementation Team (gofc-fire.umd.edu). Long term fire monitoring with consistent data records is needed to study how fire regimes are changing as a function of climate and changing land use and fire policies. One of the primary goals of fire monitoring systems is to provide information to support decision making, leading to improved fire management, reducing hazards and the negative impacts of fire on the environment. For fire fighting purposes, emphasis must be given to the timeliness of delivery of observations. The CEOS Disaster Management Support Group specified the need for data to be received within 15 minutes of fire detection (Dull and Lee 2001). This latter requirement can only be met by continuous monitoring by an ultra- and very fine, geostationary capability, or by aircraft or unmanned aerial vehicles, in areas where fire has already broken out. This is clearly a goal for developed countries with fire fighting capabilities, but for countries with large tracts of territory where fire management is either infeasible or only targeted at key valuable resources, the delivery requirements are less stringent. Observation needs / technical requirements Satellite observation needs Satellite observation needs for fire can be divided into three types; pre-fire early warning, active fire detection, post-fire monitoring. Fire Early Warning Fire early warning requires a combination of recent weather data and information on vegetation composition and condition. Weather data are obtained from a combination of satellite observations and data from in situ weather stations, often through data assimilation models. Timely weather information and temporally composited vegetation indices providing information on the condition of vegetation are used to develop fire danger indices. To determine fire danger, information is also needed on the amount of vegetation available for burning (i.e. fuel load). At the crudest level, an average value for fuel load obtained from the literature of sample ground measurements can be assigned to a given land cover type. In a more sophisticated approach, fuel load can be modeled using a dynamic vegetation model, with inputs on vegetation type, rainfall and satellite data. Time-series satellite vegetation indices at 500m 1km provide input for both early warning and vegetation modeling. Some models use satellite estimated FAPAR and LAI products to help calculate above ground production which is allocated into fuel components. Improved characterization of fuels is anticipated from structural information obtained from vegetation canopy lidars. Active Fire Detection. Satellite data from the middle and shortwave infrared are used to identify burning or active fires from their surrounding conditions (Giglio et al., 2003). Moderate resolution polar orbiters currently provide sub-pixel detection (<1km) of active fires orbiting twice in a day. Geostationary data with coarse- resolutions (>1km) provide a more frequent half hourly sampling of the diurnal cycle of fire activity. The channels used for fire detection need to be capable of detecting flaming fires at 750Kelvin without saturation. Fine spatial resolution sensors (<30m) provide the means for a more complete characterization of fires and the validation of moderate resolution fire detections. Recent development in active fire detection have included calculation of Fire Radiative Power (FRP) which is related to biomass consumed (Wooster et al. 2005). Burned Area Following fire, the ground surface conditions are changed, vegetation is burned off and charred material and ash often remain. The resultant fire scars can be mapped from space using optical and infrared sensors. In some regions the fire scars persist for a number of years, whereas in others the char is blown away, or the recently burned field is ploughed or perennial grasses sprout within a few days of the burn, making automated mapping of the fire affected area difficult. For national mapping of burned area or regional fire emissions modeling, maps of monthly burned area, accumulated during the year are adequate. Such burned area mapping is currently performed using data from the near-IR and SWIR parts of the spectrum at 500m -1km. For rapid post burn assessment of fire impact in ecologically sensitive areas, fine resolution data (10-30m) are needed within 48 hours of the fire to assess fire extent, severity and ecosystem and hydrological impact. National Fire Statistics Most developed countries compile annual statistics on fire extent and distribution. The public availability of these data is varied. Traditionally these statistics are derived from field based reports or aerial surveys. Recently some countries have utilized satellite methods to acquire fire statistics over large areas e.g. in Russia and Canada (e.g. Lee et al. 2002). There is no standard approach to the compilation of national fire statistics and the results from different countries are variable in their accuracy. National statistics are gathered and redistributed by the Global Fire Monitoring Center, Freiburg, Germany. Current status of Satellite-based monitoring systems Regional active fire products are being generated by geostationary satellite systems with half hourly repeat frequency (e.g. GOES, MSG) and validation of these geostationary products is in progress. There are several possible sources for active fire data, but currently MODIS is the only system providing both day and nighttime active fire detections globally, and which has the spectral band characteristics (specifically wide dynamic range MIR and TIR channels) necessary to derive unsaturated Fire Radiative Power (FRP) measurement for almost all detected events. The AVHRR, provides the longest record of mid-IR terrestrial observations (1983-present), but the 3.9 micron channel saturates at a low level, the 1 km data have not been collected globally, and the drift of the satellite orbit provides an inconsistent data record. With future plans to acquire global 1 km data from the NOAA AVHRR and METOP, these data could contribute to a long term global fire data record, resuming the global 1 km data set collected by the EDC DAAC for 1992-1999. The global ATSR data go back to 1995 and provide a consistent source of nighttime fire observations. However, since the diurnal fire cycle is at a minimum at night, this record will very much represent a limited sample of the true fire activity. The U.S. Air Force Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) can also detect fires at night via low light imaging in the visible wavelength region. There are a number of efforts in Europe to develop global burned area products. A global burned area product developed from AVHRR 8 km (1981-1999) data by the Joint Research Centre, Ispra (Carmona-Moreno et al. 2005). The product has severe limitations for science use, due to inaccuracies in detection resulting from the aggregation of the GAC data and calibration consistency issues. Regional 1 km AVHRR burned area data sets have been generated but not on a systematic basis or with validation. Two global burned area products were developed using European data for the year 2000, the GBA2000 product from SPOT-VEGETATION data (Tansey et al. 2005) and the GLOBSCAR product from ESA ATSR data. Systematic intercomparison of these products shows major inconsistencies at regional and continental levels (Korontzi et al. 2004, Boschetti et al. 2006). A current effort as part of the ESA GLOBCARBON program is developing burned area from the ATSR sensor. The MODIS burned area product is starting to provide a global multiyear record of monthly burned area. Preliminary validation results show that at least 85% of the total burned area is mapped by the MODIS automated algorithm (Roy et al 2006). Major gaps and necessary enhancements Developing a Global Geostationary Satellite Fire Network. Geostationary data provide the best opportunity for capturing the diurnal cycle of fire activity (Prins et al 2001). Although geostationary satellites cover most of the World, not all geostationary imagers provide fire information. Geostationary systems with middle infrared sensors are being developed with higher spatial resolutions and thus become increasingly attractive for active fire detection. Through the international GOFC/GOLD program there is an initiative to coordinate a global network of geostationary satellites, providing active fire detection with a 15-30 minute frequency. This initiative requires the support of the operational space agencies and weather services responsible for the geostationary satellite systems. Moderate Resolution Fire Data Continuity Fire detection and burned area mapping from the AVHRR operational imager were greatly improved by the MODIS instruments. The experimental MODIS imagers on NASA Aqua and Terra will be replaced by the NPP VIIRS in 2009, providing the start of a new operational satellite program, NPOESS. The active fire detection and characterization capability of the VIIRS will be seriously impacted by a lower saturation level of the 11 micron band than MODIS and on-board data aggregation, thus effecting product continuity. It is recommended that fire detection is undertaken prior to pixel aggregation and that FRP be included as part of the VIIRS Fire Environmental Data Record. For the future, the US Integrated Program Office needs to raise the saturation level of its middle thermal infrared 11 micron sensor to enable fire detection and characterization without saturation on the next build of the VIIRS instrument. MODIS, SPOT Vegetation, AATSR and MERIS all provide moderate resolution data which can be used for burned area mapping. For instance within ESAs GLOBCARBON (Plummer et al., 2005) daily observations from Vegetation, MERIS, ATSR-2 and AATSR data are used to cover a 10-complete years timeframe from 1998 to 2007. A consistent, global long term record of burned area is needed. It is critical that products generated from these systems are fully validated to CEOS Land Validation Stage 3. A coordinated international effort is needed for the validation of the global burned area products, using the CEOS Burned Area Validation Protocol established by the CEOS Land Product Validation Working Group. The monthly and near real time burned area products generated from the current research instruments need to be transitioned to the operational polar imagers for long-term data provision. Fine Resolution Data Availability for Fire Monitoring. Fine resolution data are used for post fire assessment and the validation of moderate resolution products. A data gap has occurred for fine resolution data due to the Landsat 7 SLC off problem. Landsat was the only system providing systematic global acquisition of fine resolution. There are a number of fine resolution systems in orbit which could be coordinated to provide observations within 48 hrs of large or hazardous fire events for current and post fire assessment. Future fine resolution imaging systems (<20 m) need to be designed to include active fire observation and characterization (fire radiative power) capabilities. Improved Access to Fire Data and Information. There are a number of obstacles currently to the use of satellite data for fire management. The primary obstacle is the cost and availability of fine resolution imagery. Near real time data of active fires and burned areas are needed by the fire management community. Web-based GIS systems greatly facilitate access to and use of the fire data products and such enhancements are needed to the current operational data systems to increase access to and use of the satellite fire data. Standardization in the compilation and open access to the reporting of national fire statistics are also needed. Principal recommendations Coordinate an international network of geostationary imagers, providing global active fire detection every 15-30 minutes and make these data available in near real time for fire alert and management. Modify the NPOESS VIIRS sensor for the non-saturated detection and characterization of active fires. Monthly and near real time burned area products should be included in the operational product suite from NPOESS. Reprocess the AVHRR archive held by NOAA (and NASA), with correction for known deficiencies in sensor calibration, and also for known directional/atmospheric problems. Support a coordinated international effort to validate the current and future global burned area products to CEOS Land Validation Stage 3. The GOFC/GOLD Regional Networks provide an opportunity for expert product validation Coordinate and target acquisition of data from the international fine resolution assets to provide fine resolution imagery (<20m) of large and hazardous fire events within 48 hours of the event. The data need to be affordable and easily accessible by the international fire management and research community. Future fine resolution systems should include the capability for active fire detection. Enhance the access to and utility of their fire products, through the use of near real time delivery systems and web-gis. Implement standardization of national fire data collection and reporting and promote open access to these data. These data should be spatially explicit and georeferenced. Initiate an international program on Global Fire Early Warning, integrating satellite and in situ fire weather data. Biodiversity and Conservation The Convention on Biodiversity (CBD) goal, agreed upon at the World Summit on Sustainable Development (WSSD 2000), is to significantly reduce the current rate of loss of biological diversity by 2010. Decision VII/30 of the CBD and the Millennium Development Goals lays out specific biodiversity indicators and measurements needed to achieve conservation and development targets. The biodiversity data requirements deal primarily with the abundance and richness of wild species of plants and animals, their habitat, and threats to their habitat. The loss of biodiversity has implications that become regional and in some cases global in scale. Endemic plants and animals have by definition very restricted ranges while other species may have dispersal and migration patterns that are nearly global. As a result, a global framework for monitoring is essential. The major biodiversity data needs are in situ, but need to be supplemented with remote sensing-based data to monitor changes in the distribution and status of ecosystems. Observation needs and technical requirements Biodiversity observations address the GEO societal benefit on conservation and biodiversity. The required data contribute to understanding trends in local to global biodiversity, e.g., changes in ecosystems, and species abundance and distribution, and in addressing the fundamental threats to biodiversity, such as land change eroding habitat quality, population pressures on natural habitats, invasive species, trade in threatened plants and animals, and climate change (Balmford et al, 2005). A special meeting on biodiversity Earth observation requirements held in November 2005 in Washington DC (Janetos and Townshend 2005) resulted in the identification of numerous datasets that are essential for either creating additional biodiversity-specific datasets or for understanding biodiversity status. Several of the specific datasets serve multiple purposes and are identified elsewhere in this report. These include DEM, vegetation structure, land cover change and land cover fragmentation, land use, ecosystem classifications, soils, and land degradation. The following needs sections identify the biodiversity-specific in situ, observation, and modeled data needs. Although much important data is still lacking at the global scale in terms of change in extent of habitat types many of the most challenging biodiversity data needs will require in situ collection strategies. These include: Trends in species abundance and richness by location. Longitudinal species databases needed to understand population dynamics as a function of land change or other threats are rare. One of the few examples is the Breeding Bird Surveys conducted in North America and Europe, for which field-based observation of species by location are needed. Locations and distributions of threatened or endangered species. This is needed in order to understand global priorities for conservation action to protect and manage critical habitats. Publicizing specific locations of threatened or endangered species in some cases should be controlled in order to avoid illegal poaching or destruction. Protected areas extent and conservation status. A global geospatial database of protected areas with attributes describing the level of protection provided by each conservation holding is maintained and being updated by the United Nations Environment Programmes World Conservation Monitoring Centre, and is able to provide information on the level of habitat protection available within ecosystems. Currently 12.5 percent of the global terrestrial area is protected at some level, but the specific location and status of habitat of many protected areas is uncertain. However, these efforts are often limited by the availability of data at the national level on protected area distribution and level of protection. Protected areas status: Regular fine resolution mapping of human disturbances within protected area boundaries would allow threats to biodiversity in those areas set aside for biodiversity conservation to be identified. Combining these data with the active fire products outlined above would provide a near-real-time indicator of a major pressure on protected areas. Observation Needs Ecosystems and ecological regions data, and ultimately maps are needed to provide information on trends in ecosystems and land use, and a framework and context for assessing broader biodiversity trends. Both fine and moderate resolution imagery are needed, along with geospatial information on other environmental variables, such as soils, topography, and infrastructure. The datasets and maps must be designed for monitoring trends and overall ecosystem health and sustainability. A first step is to establish international standards and definitions for ecosystems. Habitat maps prepared from high-resolution imagery, must provide the basic floristic and physiognomic characteristics needed for species distribution models. The data needed include trends in land cover, land use, biophysical conditions, fragmentation, and other ecosystem variables. Invasive species maps showing the locations and spread characteristics of specific invasive species are needed. Ultra-fine resolution, multi-spectral and hyper-spectral observations are most suitable. Modeled Data Needs A suite of measures describing habitat patterns are needed to understand fragmentation, landscape patch size, and other metrics that relate to habitat condition. Habitat maps showing trends described earlier are key inputs to this work. Trends in species distributions, linked with habitat, are needed to model species ranges, and to evaluate carrying capacities for individual species. This will require a variety of ecosystem-specific models in which habitat maps and species occurrence data are combined to identify trends in species distribution and abundance. A comparison of species distributions and habitat protection status will lead to identification of conservation gaps and the identification of the types of ecological conditions (e.g., habitats) that require additional protection. The combination of land cover and protected areas maps will permit identifying fragmentation and encroachments using remote sensing for the purpose of assessing the effectiveness of protected areas status in meeting conservation goals. An important part of this is the periodic assessment of the rates of encroachment into protected areas. Current plans The Convention on Biodiversity will continue to be a strong driver for improving biodiversity data sets, and the requirements of the Convention should be considered when planning biodiversity data developments. Other organizations that have programs underway that contribute to the biodiversity data needs include: UNEPs World Conservation Monitoring Centre is maintaining the World Database on Protected Areas on behalf of the World Commission on Protected Areas, and coordinating the 2010 Biodiversity Indicators Partnership, delivering the full range of biodiversity indicators associated with the 2010 biodiversity target, in addition to producing a range of ecosystem and species assessment products (www.unep-wcmc.org). The World Conservation Union (IUCN) Species Survival Commission is continuing monitoring trends in threatened species (Red List Index) (www.iucn.org/ssc). The Global Biodiversity Information Facility (GBIF) is serving as a catalyst for digitizing making available local specimen distribution data (www.gbif.org). DIVERSITAS is developing frameworks for international research, promotes standard methods, facilitates construction of global databases, and synthesis and integrates biodiversity activities. Key topical interests of DIVERSITAS include observing, monitoring, and assessing biodiversity levels, understanding ecosystem functioning, and developing knowledge that guides policy and decision making (www.diversitas-international.org). The RAMSAR Convention Secretariat is exploiting outputs from the GLOBEWETLAND project as input to their technical documentation. Major gaps and necessary enhancements The current availability of biodiversity information is deficient in both content and characteristics, particularly as regards to consistent measurement of trends. Regarding specific observation needs, there is an urgent need to use remote sensing to provide information on trends in land cover and habitat types, and fine resolution imagery to document unauthorized land uses in protected areas. Other needs include the following: While global maps of biomes and ecoregions exist and are helpful in understanding broad ecosystem characteristics and threats, information on specific plant and animal species is too often limited in time, geographic extent, and consistency. It is recommended that the conservation community adopts a consensus ecosystem classification hierarchy and map product. Additional resources are required for maintaining updated information in the World Database of Protected Areas. Comparability of existing data collections is often affected by taxonomic inconsistencies. Efforts such as the Integrated Taxonomic Information System (ITIS) established by several North America agencies is narrowing the taxonomic divide in one part of the globe, and is linking to the international efforts of Species 2000, which aims to document all known species of organisms on Earth as the baseline dataset for studies of global biodiversity. Biodiversity information too often does not include the essential location coordinates needed to understand biodiversity in a geospatial context, or that of time-series data, essential for the understanding of trends and the effectiveness of interventions. Georeferenced socio-economic observations are needed to understand causes and consequences of biodiversity losses Product-specific critical issues Many national agencies and IUCN, GBIF, and DIVERSITAS have species data management and distribution policies that should be consulted. Principal recommendations Biodiversity Update world database of protected areas. Ensure availability and comparability of existing data collections. Georeference all new socio-economic observations. Enhance availability of 30m global topography, which play a critically important role in both correction of imagery data, in habitat delineation, and as model input data. Ensure delivery of very high resolution cloud-free imagery at low cost for rapid response in key areas, with ability to monitor cloudy areas for illegal logging, road-building in sensitive areas, and so forth. Maintain continuity of long-term seasonal record of land-cover change and fragmentation at 30m resolution. A key attribute, or derived characteristic of such a land-cover product would be the derivation of disturbance patterns and frequencies. Develop a long-term record of critical land-use characteristics, at a spatial scale that is commensurate with the land-cover change product, but that includes additional information on the human use of land resources such as crop type at sufficient spatial resolution to identify small land-holders (ca. 0.5 ha). Generate seasonal freshwater distribution and flow data products sufficient to detect irrigation schemes. Improved models for predicting species distributions on existing landscapes and develop better guidelines for their use by the scientific community and conservation organizations. Organize observational data from in situ research sites in order to develop a validation database for existing products of relevance to biodiversity issues. Adopt a consensus ecosystem classification hierarchy and map product that describes how systems are mapped, how to add detail, and how to extend the classification scheme to all ecosystems (including human dominated systems). Agriculture Observations are needed in support of four different aspects of agricultural monitoring; the collection of agricultural statistics at the national and sub-national level; the monitoring of major food crops and crop production; the forecasting or early warning of harvest shortfalls, for example due to drought, pests or excessive rain; and for long term monitoring of changes in the extent and productivity of agricultural lands and their sustainability. For official development aid planning purposes two additional elements of information are needed; land tenure/ownership maps and land availability/suitability maps. Long term monitoring of trends in production and distribution can contribute to questions concerning agricultural sustainability. Information derived from Earth observations can help reduce risk and increase productivity and efficiency at a range of scales from global to the farm unit level. The primary goal of agricultural monitoring systems is to provide information to support decision making, leading to improved agricultural management and production and food security. A global agricultural observing system would enable the following seven results: mapping and monitoring of changes in agricultural type and distribution; global monitoring of agricultural production, facilitating reduction of risk and increased productivity at a range of scales; monitoring of changes in irrigated areas; accurate and timely national agricultural statistical reporting; accurate forecasting of shortfalls in crop production and food supply; effective early warning of famine, enabling a timely mobilization of an international response in food aid; and reliable and broadly accepted 5, 10, and 20 year projections of food demand and supply as a function of changing demographics, markets, agricultural practices and climate. The diverse nature of agricultural practices and the need for timely delivery of information for decision-making, places some unique requirements on agricultural observing systems. The distribution of field size and rapid changes in crop condition require both a fine spatial resolution and a frequent revisit time combined with near real time delivery for the satellite observations. Observation needs and technical requirements General requirements for mapping agricultural land and monitoring change in extent are described in the land cover and under land use sections. For agriculture, measurements from optical sensors (visible, NIR, SWIR*) provide the primary input data to map and characterize crop area, crop type and crop condition. For global scale mapping and monitoring, products derived from daily, moderate resolution (c. 100-500m) sensors can be used. For regional scale studies and agricultural areas with small or poorly defined fields, monitoring is undertaken with higher spatial resolution (10 - 30m) satellite data. Crop type discrimination and mapping is commonly performed using a combination of multispectral and multitemporal analyses. Targeted imaging of local crop conditions can be undertaken using very fine spatial resolution data (1-3m), currently available from commercial satellites. Mapping and monitoring of wetland rice, irrigated areas, water impoundments, and areas with persistent cloud can benefit from the use of microwave data. Multitemporal moderate resolution, tandem SAR data can be used to provide detection of crop emergence and estimation of acreage. Monitoring of plant water regimes and deficits may be undertaken using SWIR and thermal data. The determination of soil moisture is being investigated using thermal and microwave data. The monitoring of reservoir heights can be done using radar altimeters and snow amount can be determined using optical, thermal and microwave data to provide information on agricultural water supply in irrigated areas. Flooding of agricultural lands can be monitored using visible, infrared and microwave data. Remotely sensed data from thermal and microwave sensors are also used to estimate rainfall. Monitoring of agricultural residue fires and the occurrence of slash and burn agriculture is undertaken with high saturation sensors in the middle and thermal infrared. Monitoring of crop phenology and condition is undertaken using various vegetation indices, formed from time-series data from multiple channels, requiring good pixel geolocation and band to band registration. Anomalies in the vegetation signal associated for example with agricultural drought or insect infestations, can be identified using comparative analysis of time-series data from previous growing seasons which requires a consistent and well calibrated data record. A summary of the temporal and spatial requirements for monitoring crop growth is provided in Figure X. Remotely sensed data when combined with mechanistic models, meteorological information and other auxiliary information, enable estimation of crop yield and forecasting of production. Remotely sensed data can also be used to optimize the parameter set and improve the performance of process-based crop models at regional and national scales, using data assimilation technique. Food insecurity monitoring and famine early warning are undertaken using a combination of satellite, meteorological, in situ and survey data and socio-economic indicators. Spatially explicit modeling of future scenarios of agricultural demand or production is undertaken at global and regional scales with inputs on climate, economic and demographic projections. Current status Agricultural statistical reporting and in situ observations In-situ and survey data are collected in support of global, regional and national agricultural monitoring systems providing information on area planted, germination rates, crop type and condition, crop yield, crop residue and fertilizer application. In-situ data are also collected on river discharge, reservoir, lake and well levels. Nationally and regionally socio-economic data are collected routinely on farming practices, market prices, crop production and production for economic purposes. Additionally a larger suite of data on population, food supply, health, markets and nutrition are collected locally in support of specific regional famine early warning programs. Data in FAOSTAT are aggregated at the country level. Specific global data sets related to land use include those on primary crops, agricultural area, arable and permanent crops, arable land, permanent pasture, forest and fuelwood, non-arable and non-permanent, irrigated areas, agricultural machinery, fertilizers and pesticides, production and agricultural machinery. Subsets of data from FAOSTAT are available at other sites, notably that of the World Resources Institute. AgroMaps is a global spatial database of agricultural land-use statistics aggregated by sub-national administrative districts which identifies crop yields, extents and production figures for the major crops. The AgroMaps database (on-line at: www.fao.org/landandwater/agll/agromaps/interactive/index.jsp) is however not comprehensive but continuous upgrades are undertaken by FAO in partnership with SAGE and IFPRI. Livestock densities are available globally in a 3 arc min grid dataset (FAO 2007). Weather observations and in particular rainfall data from meteorological stations play an important role in crop monitoring. In general, in developing countries the network of stations is in decline and for some agricultural regions additional observations are needed. For some stations, data are still recorded on paper and there is an urgent need for digital archives to be developed for all stations. Alternative approaches of community involvement in making observations and low cost technologies for increasing the density of rain gauge stations have been demonstrated in India. Currently weather data are provided globally for a limited number of sample stations by the WMO. Satellite-based monitoring systems Forecasting of major food crop production in selected countries world-wide has been operational since the mid-1980s, with the objectives to support food security in developing countries and to provide information to the global market of agricultural crops. A number of programs utilize satellite observations for global agricultural monitoring, traditionally relying upon coarse resolution (8km) data from the NOAA AVHRR and more recently on moderate resolution (250m 1km) data for example from MODIS, Vegetation and MERIS. Routinely generated global or regional, temporal (8, 10 or 16 day) composite data sets of vegetation indices are augmented with higher resolution (30m) data on a sampling frame or to monitor representative areas at critical periods in the growing season. Daily near-real time data at 250m or targeted fine resolution (c. 30m) data are used to image disaster areas. Global to regional maps of crop type and change are being generated experimentally from time series of moderate resolution (250m) data. Regional and local maps of crop type and change are generated using single or multiple fine resolution data collected at critical times in the growing season. The comparative paucity of satellite-based microwave sensors has limited the use of these data but promising results have been demonstrated using ERS 1 and 2, ENVISAT ASAR, and RADARSAT for rice crop acreage and yield estimates. The global monitoring of reservoir height and lake levels is already being undertaken by ESA and NASA/USDA/UMD using radar altimetry. The utility of spaceborne, hyperspectral imaging is currently being evaluated for crop diagnosis (pest, disease and stress) using data for example from EO1 Hyperion. Similarly fine resolution thermal data, for example from ASTER are being evaluated for estimation of soil moisture; however initial findings indicate that the full potential of these capabilities for agricultural monitoring will require high temporal resolution data. Model output Data assimilation techniques are enabling the provision of global precipitation grids from a combination of satellite and ground based measurements in near real time. In the research domain, radiative transfer models are being coupled with crop growth models to improve the models and fully utilize the physical quantities derived from the satellite data. A number of crop production forecasting models are based on integration of data relevant to assessment of crop conditions, such as remote sensing, climatic, rainfall and its frequency during growing season, extent of irrigation schemes, state of land degradation, agronomic inputs, and historical crop yields. These models are for the most part experimental and require additional research and development for operational use. Current plans With the planned missions of NPOESS VIIRS with spatial resolutions at 375 and 750m, the prospect for the ongoing provision of operational moderate resolution data over the next decade is ensured from the U.S. It should be noted that this falls a little short of the 100-300m (visible to SWIR) requirement for crop mask and agricultural vegetation monitoring identified in Figure X. There is no such plan for the operational transition of the CNES SPOT Vegetation instrument, which currently is used extensively for agricultural monitoring. However, a number of other moderate resolution systems are planned by Japan, ISRO, EUMETSAT and ESA. Attention needs to be given to ensuring data product continuity and quality, requiring instrument inter-calibration and product intercomparison. Data continuity between instruments can be greatly enhanced by a consistent central wavelength and bandwidth for the core vis SWIR vegetation monitoring bands. Major gaps and necessary enhancements For agricultural monitoring, a continuous fine resolution data record is needed, providing multiple cloud free observations each year. This is currently the largest gap for agricultural observations. Problems with Landsat 7 have created a critical gap in global fine resolution observations for the agricultural monitoring community and a replacement and improvement of the functionality of Landsat 7 for agricultural monitoring is urgently needed. In the short-term coordinated acquisition from other on-orbit fine resolution assets such as Landsat 5, IRS, SPOT, ASTER, EO1 and CBRS could help fill this data gap. For agricultural purposes, systematic acquisition and near real time delivery of fine resolution data are needed at critical periods in the growing season. In the mid-term (3-5 years) a fine resolution system is needed that will provide 5-10 day cloud free coverage of all agricultural areas. Such a system is technically feasible and could be facilitated by international cooperation. There should also be equitable and consistent data and pricing policies and data should be provided in standardized formats, facilitating inter-use. Provision of orthorectified products would facilitate data inter-use. Operational status is urgently needed for fine resolution systems with planned instrument replacement, to avoid future breaks in coverage. There are no globally recognized standards for in situ or survey data collection, although GPS are used increasingly for precise location. Different groups collect these data using different methods and benefit would be accrued from increased standardization of data collection and increased data sharing. In general these data are not made accessible outside of the project for which they are collected. In some cases high impact, low cost improvements such as access to the internet would greatly improve data access. In other cases increased coordination and capacity building for data collection and dissemination are needed. With respect to the desired improvements to microwave satellite systems, the tandem-like operation of two satellites with C and L band, HH+HV polarization, a 300km swath and 10-20m resolution with a temporal resolution of 10-15 days would be well suited for crop monitoring. This would allow use of both intensity and coherence measurements allowing monitoring of cropping activities. In situ and survey data Increased standardization of collection and improved quality and availability of in situ agronomic variables is needed at the sub-national level. An assessment is needed as to where effort is needed in this regard and what initiatives can be taken at the international level. For understanding regional trends in agriculture there is a need for improved spatially explicit survey data on agricultural technologies, practices, land use and ownership (e.g., land tenure, collectivization, clan or family ownership, government or corporate land ownership) and processes of transferring land ownership for single or multiple or overlapping purposes and the public appropriation of land. Product-specific critical issues Continued provision of vegetation index, crop yield indicators, crop area, crop type, vegetation stress, fire products are needed at moderate resolution (250-1km). A globally reliable crop mask is a high priority. All products intended for operational use should be validated with known accuracies. In particular, methods for crop area estimation should be improved and products validated. A moderate resolution product detecting change in agricultural area on an annual basis would guide fine resolution mapping of change in agricultural extent. There is also the need for increased availability of gridded (5km) precipitation estimate products (with 30 minute accumulations) from assimilation of satellite and in situ data and associated derived products and indicators relating to crop water balance and drought. Improvements are needed in the modeling of crop yield for example satellite information on sowing and emergence date is needed to initiate the models. LAI and fAPAR are needed at the crop level for adjusting the models. Once validated and tested, the models should be transitioned from research to operational model. Reliable three month weather forecasts are needed for use in agricultural decision making. The potentially rapid and dramatic changes in crop condition means that agricultural monitoring has specific needs of the observing systems with respect to timeliness of data delivery. Near real time data is needed in addition to summaries on a 5-10 day basis. With the increased access to the internet, open and rapid access to data is now feasible. A community wide effort is needed to improve access to the data that are current being collected. The creation of an agricultural data sharing network is needed to improve the timely dissemination of satellite, in situ, survey data and model outputs. The shared data would conform to agreed-upon data format standards and quality. Attention is required to ensuring partners in the network have access to the internet. Principal recommendations Standardize collection and dissemination of annual national statistical and other in situ data. Enhance rain gauge data collection network and lower barriers to timely data access. Improve seasonal climate prediction accuracy. Provide fine resolution (10-20m), cloud free coverage with a 5-10d return period. Ensure continuity of moderate resolution (1km, 100-300) observations. Improve targeting and reduce costs of very fine resolution (1-3m) imagery. Improve spatial resolution, targeting, and height accuracy of radar altimetry and operationalize data collection. Provide near real-time access to regularly collected microwave data (10-30m) that can be fused with data from optical systems. Soils Soil is a central component of land ecosystems that impacts agricultural and forest productivity, ecosystem states and fluxes, biodiversity, water quantity and quality, and human health and settlements. Soils are extremely diverse and their status, health and potential for sustainable use depends on local terrain, hydrology, vegetation and geology, as well as current and past land use and management practices. Soil characteristics comprise basic information necessary for making good land use and land management decisions. International environmental conventions, like the Convention to Combat Desertification, the Convention on Biodiversity, and the UNFCCC and the Kyoto protocol are all reliant upon accurate global soil observations. Observation needs and technical requirements Soil type, texture, salinity, erodability, nutrient and organic matter content, and water holding capacity are the basis upon which decisions about land use and management are made and from which higher level soil characteristics, like soil fertility, yield potential, carbon storage potential, water supply, sediment yield, erodability, and agricultural suitability mapping, are derived. Since multiple soil observations are crucial input to models that simulate crop growth, calculate anticipated yields and water balance, assess the environmental impact of different land use practices, and that identify major agricultural potentials and constraints, various soil observations for any given map unit must be of the same vintage and available at the same resolution. Obviously enhanced resolution and accuracy will lead to more accurate assessments, but most primary soil observations are based on field sampling, which is very labor intensive. Current status Global soil resource information exists at 1:5 M scale in paper map and digital format in the digital FAO/UNESCO Soil Map of the World. This map links to global and regional databases of soil properties, problem soils, and fertility capability classifications. The SOTER (SO for Soil, TER for Terrain) program was initiated to consistently map areas with distinctive landform, morphology, slope, parent material and soil patterns at 1:1 million scale. SOTER was originally intended to be worldwide in scope, but has not been fully implemented. The recent release of the moderate resolution SRTM Digital Elevation Model (90 by 90 meters) has made it possible to generate SOTER terrain units globally and efforts continue to link with soil databases. Geo-referenced and quality-controlled soil profile information is limited to about 6,000 profiles worldwide, though the quality and quantity of the soil information gathered varies greatly from country to country. Current plans In 1998 the International Union of Soil Science (IUSS) endorsed a new international soil correlation system: the World Reference Base for Soil Resources. It is hoped that development of this unique international soil correlation system endorsed by all soil scientists, has now solved the problem of geographically inconsistent soil taxonomies, although it will probably take years for the system to be fully adopted everywhere with current levels of resource allocation. The main challenge for developing countries will be the sophisticated analyses (e.g. volcanic glass content, total reserve in bases, etc.) required to identify certain diagnostic horizons and properties and to classify the soils accurately. Under a major update of the Global AEZ study (Fischer etal, 2002) FAO and IIASA with other partners intend to release in 2007 a harmonized World Soil Database which brings together all existing SOTER studies, The European Soil Database (which includes Northern Eurasia) and the most recent Chinese Soil Map with the gaps being filled by the FAO/Unesco Soil Map of the World. Another initiative was launched by Columbia University in December 2006 who with other partners would undertake a similar exercise. Major gaps and necessary enhancements The status of regional and global soil profile databases is unsatisfactory given the relatively limited quantity of data present. Because of the emphasis on analytical laboratory data rather than morphological descriptors, many soil profile databases fail to reflect soil reality, and are often aimed at a single specific field of application. The number of controlled georeferenced soil profiles in the public domain is extremely limited (1,100), while the total number of verified georeferenced soil profiles, shows many gaps in terms of soil types for which this information is available. In general, most developing countries have scattered soil surveys only partly correlated with one another and of variable age and quality. But tracking the coverage and quality of the many ad hoc surveys is not easy. The methods for soil chemical and physical analysis vary worldwide and results obtained are often difficult to correlate. For instance, most of Eastern Europe, the former USSR and China use analytical methods different from those in other countries, making it difficult to compare things like soil texture or organic matter contents. A final problem with the soil profile databases is that no accepted standard for the storage of these data exists. Although generally the FAO Guidelines for Soil Profile Description or the USDA Soil Survey Manual are well accepted guidelines internationally, each country has developed local variants (linguistic or otherwise). The soil classification used largely determines the units identified on the map and this has hampered the development of a universal framework to store soil data. The following are necessary to fill the above mentioned gaps and to generate useful soil observations: harmonized, small-scale (1:1 Million) soil resource information on a global scale; finalization of a global soil and terrain database, in particular information in West Africa and Southeast Asia; quality-controlled, geo-referenced soil profile information collection should be vastly expanded particularly in areas where none or very little of this information has become available (China, Former Soviet Union); analytical and procedural decisions should be the prevue of single body (most logically IUSS), with binding decisions for all organizations involved with soil classification, mapping and soil analytical methods; and interpretations of soil data need to be improved. They must be more accessible and intelligible to non-soil scientists. Product-specific critical issues Problems of data access should be tackled by international political agreements such as those that can be arbitrated by the World Trade Organization and guarantee intellectual property. There exists a problem of limited access to soil information which hampers research and is counter productive. The problem is particularly serious in Europe but is quickly spreading to tropical countries. Principal recommendations Develop harmonized, small-scale (1:1,000,000) soil resource and terrain (SoTeR) database on a global scale. Expand quality-controlled, georeferenced soil profile information collection, particularly in areas where none or very little of this information has become available (China, Former Soviet Union). Encourage a single body (most logically IUSS), to develop analytical and procedural standard methods that are binding for all organizations involved with soil classification, mapping, and analyses. Make interpretations of soil data more accessible and intelligible to non-soil scientists. Human Settlements and Socio-Economic Data Human beings tend to cluster in spatially limited habitats occupying less than 5% of the worlds land area. Today more than half of the worlds population lives in urban areas, with the most rapid increases occurring in the developing countries of Asia and Africa. Latin America is already highly urbanized, and in Europe, North America, and Japan 80% or more of the population lives in urban areas. Worldwide, the trend is for increasing numbers of people to concentrate in settlements and for the settlements to expand at their perimeters. Sprawl on the urban fringe and exurban development are two of the more conspicuous signs of urban change but structural change permeates urban areas through continuous processes of intensification of use, decay, and development, and aging urban infrastuctures are undergoing continuous replacement and change. Thus, urban areas are in a constant state of flux that reflects both growing urban populations and the evolution of urbanizing technologies. Settlements and infrastructure also are indicative of intensification of resource use of the area and its surroundings. Activities associated with settlements and other human infrastructure developments often affect the surrounding air, water, land, and biotic resources. These effects have both social and Earth system implications due to trends in human-well-being and to the status of the biophysical, biogeochemical and biodiversity of the areas affected by settlement and infrastructure development. Human settlement can be viewed on a continuum ranging from largely unsettled wilderness areas at one extreme to dense urban settlement at the other. Although they are relatively unexploited for urban areas, remote sensing approaches are by far the most systematic means for collecting spatial information on human settlements. Not surprisingly, much socioeconomic data (such as GDP or birth rates) cannot be directly obtained with remote sensing data. But remote sensing products can be integrated with socioeconomic data from other sources. For example enumeration or survey data for administrative areas such as countries, provinces, or municipalities can be integrated with spatial data from remote sensing. In addition, remote sensing data can be used as proxies for socioeconomic phenomena (Hay et al., 2005). Thus surveys, censuses, and other conventional sources of socioeconomic data remain essential in understanding global socioeconomic patterns and trends, but they become more useful when combined with remote sensing data. Global remote sensing of human settlements can significantly improve decision making in a number of application areas, including: spatial modeling of population variables such as population and settlement density (both urban and rural), land use patterns, civil infrastructure, and some types of economic activity (e.g., Toenges-Schuller et al. 2006); improved modeling of the flow of food, water, energy, disease vectors, and their consequences for natural systems (ecosystem and planetary metabolism); the location and density of infrastructure for use in hydrologic modeling, flood prediction, the assessment of land use and land use change, analyzing human impacts on biodiversity, and threats to public heath; monitoring, management, and mitigation of natural disasters; urban planning and more effective location decisions and development of support infrastructure; and spatial modeling of atmospheric emissions associated with fossil fuel consumption and other anthropogenic activities. There are many other types of socio-economic data, which require improvement discussed in the sections on biodiversity, agriculture and water. There are many other types of observations that are beyond the scope of IGOL at this stage. Observation needs and technical requirements The environmental, management, and policy applications described above have a substantial overlap in their remote sensing product requirements. Thus a suite of product types can be defined that satisfy multiple user communities. Below is a listing of a basic product suite for human settlements. Because of the rapid growth in human settlements worldwide, these key datasets would ideally be updated on a regular and frequent basis to measure rates of change and identify where the most rapid change is taking place. These products include: products depicting the geographic "footprints" of human settlements of all sizes, including the outline of the developed areas, specific estimates of constructed area and volume (based on building heights), should be updated at or near an annual increment to measure growth rates; associated biophysical and biogeochemical properties of these built environments; location and extent of rural and exurban population patterns; objective identification of classes intra-urban land use, such as mixed urban land use or largely residential, commercial or industrial areas, the distribution of urban vegetation, and open lands within urban areas; vectors for streets, roads and intra-urban transport; and measures of phenomena that influence economic activity, such as the extent of the energy infrastructure, such as electric power grids, vulnerability to natural disasters, such as floods, and threats to public health. Current status Infrastructure, including road vectors, can be mapped with ultra- to very fine spatial resolution (~1 meter resolution) satellite imagery. The vertical structure of urban cores can be derived from very fine spatial resolution stereo imagery (e.g. the JAXA PRISM). Moderate resolution systems, such as SPOT and Landsat, offer the potential for global data collection on an annual basis. Such data have been used effectively for mapping urban areas and tracking growth in local settings. Synthetic aperture radar systems have substantial capabilities that could be used for global mapping and monitoring of human settlements. However, there are few ongoing programs to produce regular maps of human settlements from these sources. Examples have been demonstrated by the ESA DUP project URBEX. ESA GMES projects GUS and SAGE consolidated production of urban expansion maps (scales: 1: 10.000-25.000) and soil-sealing maps (scales 1:25.000-100.000) with updates every 3/5 years based on EO data, which are now generated over selected European regions and urban functional areas by ESA GSE Land. NASA's Shuttle Radar Topography Mission (SRTM) is an example of an archived data source that is known to have substantial potential for derivation of a global map of urban areas. In early 2006 JAXA launched the PALSAR (Phased Array type L-band Synthetic Aperture Radar) capable of collecting useful urban observations. An alternative approach to global mapping and monitoring of human settlements is through the detection of nocturnal lighting. NOAAs National Geophysical Data Center have successfully made annual maps of human settlements at one kilometer resolution using low light imaging data from the Operational Linescan System flown on the U.S. Air Force Defense Meteorological Satellite Program (DMSP). The data have been widely used in applications requiring geographical locations of human settlements and spatial distribution of economic activity at a kilometer scale (Amaral et al., 2005, Ebener et al., 2005, Sutton et al., 2006) However, coarse spatial resolution (2.7 km) and a lack of radiometric calibration limit the applications which the DMSP lights can address. Moreover, although light sources provide good proxies for most large urban settlements, the smaller settlements of poor countries are not fully electrified and significant portions of these settlements may lie below the detection threshold of the sensor. In addition, investigators have encountered ambiguities in the interpretation of changes found in the DMSP time series because the system records light in only a single spectral band. These data have been combined with gridded population data at various time periods in CIESINs global map of urban settlement extents. Current plans The Visible/Infrared Imager/Radiometer Suite (VIIRS) and its day/night band (DNB) planned for the National Polar-orbiting Operational Environmental Satellite System (NPOESS) represent an improved instrument to measure nocturnal lighting. The NPOESS VIIRS instrument will provide low-light imaging data with improved spatial resolution (0.742 km), wider dynamic range, higher quantization, on-board calibration, and simultaneous observation with a broader suite of bands for improved cloud and fire discrimination over the OLS. While the VIIRS will acquire improved nighttime lighting data, it is not optimal for this application. In particular, VIIRS low-light imaging spatial resolution will be too coarse to permit the observation of key nighttime lighting features within human settlements and the spectral band to be used for the low-light imaging is not tailored for nighttime lighting. Major gaps and necessary enhancements A number of satellite remote sensing systems collect data relevant to the global mapping and monitoring of human settlements. There is a need for an ongoing international collaboration to produce consistent global maps of human settlements using multiple sources. It is possible to use a multi-stage approach to such efforts to reduce the spatial extent to be collected and processed for ultra- and very fine spatial resolution systems. For instance, coarse resolution nighttime lights could be used to define the collections plans for higher spatial resolution systems. Key to the value of any effort for global mapping of human settlements is timely product generation and distribution. In general updates are required on an annual basis with a distribution latency of a year or less. Currently the capability to acquire ultra-fine spatial resolution satellite imagery exceeds our capability to fully exploit the data. There is a need for improvements in the algorithms for automated extraction of required products from ultra- and very fine spatial resolution sources. This includes the extraction of the outlines for constructed features (streets and roads, buildings, parking lots, and others), the extraction of building heights from stereo imagery, and vectors for streets and roads. Earth observation satellite sensors have traditionally been designed for observation of natural systems, such as clouds, sea surface temperature, vegetation and topography. Much less attention has been given to designing sensors and processing capability for the unique remote sensing observables associated with human settlements. We have identified four major opportunities for improved satellite observations of human settlements. 1) Development of improved methods for measuring building heights. Stereo optical imagery is the standard here, but other sensing methods such as lidar and microwave should be considered. Building height data would improve the modeling of parameters such as population density and modeling atmospheric transport. 2) Modeling the spatial distribution of fossil fuel emissions could be improved through direct detection and monitoring of thermal and short-wave infrared emissions from combustion point sources. With hyperspectral data it is in some cases possible to identify the composition of the atmospheric emissions. The capability to detect point sources of hydrocarbon combustion in urban areas has been demonstrated with both nighttime Landsat and airborne hyperspectral data. 3) An as yet untapped area with great potential as an indicator of the spatial distribution of economic activity is the remote sensing of radio and microwave emissions from communication devices, appliances, and power lines. 4) The current and planned systems for low light imaging (DMSP-OLS and VIIRS) are at too coarse a spatial resolution to adequately delineate intra-urban classes and measurement of annual growth increments. Color camera imagery from the International Space Station indicates that it would be possible to design a satellite sensor dedicated to moderate resolution (30-100 meter resolution) detection of nighttime lights. If the detection limits were low enough such a system could detect nighttime lights spanning from sparse rural settings to the cores of urban centers. Principal recommendations Devise a classification scheme for built environments, settlements, and infrastructure developments. Enhance ability to use ultra-fine spatial resolution data. Improve methods for measuring building heights. Model spatial distribution of fossil fuel emission. Develop the capability to map the distribution and intensity of radio and microwave emission patterns as an indicator of economic activity and income levels. Provide enhanced spatial resolution and multiple spectral bands from low light imagery. Water Availability and Use Water data products represent the quantity, quality, and extent of water resources. Water products deal with wetlands, lakes, reservoirs, canals, wells, dams, streams, estuaries, and groundwater. They are required for a wide range of applications including agriculture, forestry, and municipal and industrial water supply, disaster assessment (e.g., drought and flooding), public health issues including exposure to pollutants and contaminants and the presence and spread of infectious and vector-borne diseases, ecosystem health, and many more. Water supply and quality observations are both local and regional and require a combination of in situ instrumentation (e.g., stream gauges) and satellite observations. Factors affecting water quality and quantity are often the result of events and practices significant distances from the areas of need. As a result, water variables must be geospatially coupled, often with watershed frameworks. Due to the obvious importance of water availability and use, data collection programs exist in most parts of the world. There are significant differences in the density and frequency of in situ measurements, which creates regional deficiencies in data availability The way forward must consist of both increasing the density and quality of in situ programs (e.g., stream gauging, chemical and biological sampling, water clarity measures), satellite observations of water use and extent, and use of models for assessments. Observation needs and technical requirements Many of the water observations requirements needed to address IGOL topics are identified in the 2003 report of the Global Water Cycle Theme Team report titled A Global Water Cycle Theme for the IGOS Partnership. To meet the water variables needed by IGOL, close cooperation and joint planning with the IGOS Global Water Cycle Theme activities should be pursed. The following sections outline observation, in situ, and modeled product needs that are not included in the water cycle plan. For completeness, those water data needs are identified, as are those observations, in situ variables, and modeled data needs specified in other sections of this report such as DEMs, land cover, land use, and soils that are described elsewhere must be available in order to produce a number of the required water products. Observation Needs and Technical Requirements Observation products relate to water supply (surface and ground water), water use, and water quality. Water supply products describe the extent, quantity, and delivery of water. Precipitation, evaporation and evapotranspiration, and snow depth observations are specified as observation priorities in the 2003 IGOS global water cycle report. IGOL interests require spatially explicit observations; those requirements must be communicated to water cycle data planners. In addition, DEM, land use, land cover, and soils datasets described in other sections of this report are needed to produce water availability and use assessments. IGOL-provided water availability and use observation needs including: Surface water type, extent, and change: Fine resolution imagery is needed to categorize and map the extent of wetlands, lakes, and streams. Water bodies should be categorized according to ecological and hydrological processes. Water infrastructure: Dams, canals, and other infrastructure elements must be mapped using a combination of high- and very high-resolution satellite images. Lake levels: A lake elevation monitoring system will permit understanding hydrologic variability associated with human use, climate change, and other activities and can be used to estimate water supply. A first step is to determine a sample of lakes that are sensitive to environmental change or are vital for human survival. The Global Climate Observing Systems Global Terrestrial Network on Lakes (GTN-L) has identified the 150 key lakes for climate studies. Radar altimetry measurements are a source of lake elevation data. Although there is no current global data archiving capability established, around 1000 lakes have been monitored by Topex/Poseidon (1992-2006) and Jason-1 (2001- ). Vegetation index time series: Global moderate resolution data are needed to produce weekly to biweekly vegetation index products that can be used to identify vegetation stress and to quantify drought severity. An irrigated area water use observation product is needed on a biannual basis. Because irrigation uses such a large percentage of the available global water supply, frequent global maps derived from moderate resolution remotely sensed data are needed. Fine-resolution irrigated area maps are required on at least a decadal basis. Monitoring of the thermal properties of soils can provide useful input in relation to their energy balance, soil moisture, ET and hence irrigation water needs (Anderson et al 2007). A sedimentation and deposition observation product is needed to assess the transport of sediments. Fine resolution remote sensing techniques should be used to map and estimate the quantity of sedimentation into water bodies. Land cover derived from remote sensing is also needed to identify erosion sources. In Situ Observation Needs The primary in situ products are measurements of water quantity and quality. In both cases, automation in data collection and use of satellite communications to rapidly telemeter measurements to central locations. Many in situ needs, including streamflow rates, stream volume, groundwater capacity and extent, and water quality measures, are specified in the IGOS water report. Additional water data requirements needed by IGOL include: Water rights data dealing with the use of water is needed to understand the potential basin-wide availability of water for both economic uses and to ensure provision of water for natural systems. Nutrient contents and eutrophication levels: Levels of nitrogen and phosphorous are needed to determine eutrophication levels and to identify hazardous conditions that threaten the health of aquatic ecosystems. Water clarity and sedimentation: In situ measure of secci depths or equivalent are needed to monitor water quality and sedimentation rates associated with land use practices of natural erosion. While water clarity and eutrophication measures come mainly from field sources, hyperspectral imaging has the potential to provide equivalent measures in a spatially explicate context enabling more efficient and timely clarity and eutrophication assessments can be made. Modeled Product Needs A series of modeled products, most of which derive from the previous observation and in situ products, are needed. The IGOS water report includes modeled assessments of climate change, water hazards including flooding and drought, and human health. These areas are also very important to IGOL requirements. A major modeling initiative is the development of watershed based water use models that estimate the per sector demands balanced by the available water supply. The key elements of the watershed use models should include water demand (e.g., the integration of municipal and industrial water needs, irrigation and other agricultural uses, and water needs for natural systems), and data on water availability from all sources. The goal is a global watershed-based database of water sources, surpluses and deficits. To achieve this, model development dealing with the following topics must be initiated in order to produce the watershed water availability model: ecosystem plant water requirements, including both terrestrial and aquatic components of ecosystems; water requirements associated with key sectors, such as municipal and industrial water, irrigation, and other consumptive uses will be closely linked to land use; watershed variables including watershed delineation and extent, stream networks, stream order, and flow lengths to be developed from appropriate DEMs; and impacts of climate change on land variables. Progressive improvements in the mapping of the distribution of irrigated areas has been achieved through cooperative efforts between Center for Environmental Systems Research, University of Kassel with the Land and Water Development Division of the Food and Agriculture Organization of the United Nations: the latest version has a resolution of 5 min (area 9.25 km by 9.25 km at the equator). Current plans A number of initiatives are underway that can contribute to the definition, coordination, development of management of water availability and use products. The following is an incomplete set of potential water product partners: GEMS Water compiling data on lakes and rivers water quality Global Runoff Data Center compiling stream gauge data UNESCO and the International Hydrological Programme provides multidisciplinary support for addressing regional and national water needs. FAO - addresses water availability by country; also do irrigated area estimates World Hydrological Cycle Observing System (WHYCOS) of the World Meteorological Organization (WHO) has a goal to improve the basic in situ observation activities, and strengthen the international cooperation and promoting free exchange of datain the field of hydrology. WHO also plays a key role in setting international water-related data collection standards. Major gaps and necessary enhancements Continuity of global fine resolution remote sensing is essential for meeting water availability and use needs. The testing and implementation of hyperspectral imaging to measure water clarity and eutrophication is another improvement. Perhaps the most significant development that must be addressed within the context of the IGOL water availability and use data needs is the establishment of the watershed water use model. This will require integration of data from across IGOL and the IGOS global water cycle initiative. The further development of global irrigated lands data will be challenging but will require a commitment to analyze both high- and moderate-resolution remotely sensed data to document the seasonality and intensity of irrigation. This effort should be coordinated with the IGOL land use and land cover data development activities. Principal recommendations Ensure continuity of fine resolution (10-30m) remotely sensed data. Test and implement methods to use hyperspectral imaging to measure water clarity. Enhance methods to model water use/demand on the watershed scale. Map irrigated land area using high and moderate resolution remotely sensed data. Topography A vast range of land-related applications need height information, as highlighted in this report and also by other IGOS themes such as Geohazards, Water or Cryosphere. Topographic information derives from ground or remotely acquired data processed with different procedures, depending on the original acquisition device (tying to existing geodetic networks, correcting for acquisition times and local errors, reconstructing geometric/geophysical properties of the acquisitions, interpolating, etc). This explains why formats in which topographic information might be expressed can range from height points to contour lines, to TINs, to grids, etc. This section will focus on Digital Elevation Models (DEM), which is the generic term for a representation of heights on a regular grid. Within this category, two sub-categories may be identified depending on the type of surface which is represented: Digital Terrain Model (DTM, a bare-earth DEM which refers to the heights of underlying Earth) or Digital Surface Model (DSM, a top-of-canopy or top of buildings DEM). Availability of information related to both surfaces allows computation of relative heights and therefore volumes, and is of particular interest to urban applications. Currently available global topographic datasets have coarse resolution (90 m to 1Km) and were derived from spaceborne observations (SRTM), from ground observations (GTOPO30) or from a mixture of ground, airborne and satellite observations (GLOBE or ACE, the latter resulting from the combination of spaceborne altimeter data with GTOPO inputs). Finer resolution datasets already exist, for instance acquired by SRTM (C band at 30m resolution), but they have not been released worldwide. DEMs derived from the DLR/ASI X band radar onboard SRTM (30m resolution) also exist, but they do not uniformly cover the Earth. Observation needs and technical requirements Many calls have been made for improved topographic maps of the world such as the Global Map at a scale of 1:1 million proposed by the International Steering Committee for Global Mapping with an effective resolution of 1 km. Standards dealing with quality and accuracy of topographic datasets already exist (e.g. DTED-2 defined from NIMA has a post spacing of one arc second -approximately 30 meters), and a global, topographic dataset with such type of resolution would be welcomed by the IGOL community. This would in fact provide natural resources managers and decision makers, modellers and scientists with information well suited to their standard scale of work. In addition, many local topographic datasets might be improved by such global dataset, as it already happened in the past for regional DEMs, when global ACE helped correcting discrepancies. It has to be noted however that in areas of low topography such as coastal zones and flood plain areas, there is a need for topographic information with much higher resolution (in the order of 1 m), because of the impact of small topographic variations on the likelihood of flooding. Besides resolution/accuracy issues, temporal requirements for updates and delivery may vary. Recent information is requested from the modeling, planning and management communities (e.g. floodplain management, environmental and microclimate studies), whereas up-to-date information, instantaneously delivered, is necessary for disaster and relief management, mapping for humanitarian aid or market-related applications. Products directly derived from topographic datasets, are relevant for hydrologic parameters extraction, soil information systems as well as for geomorphologic analysis. In addition, a key issue for all the land sub-themes, is the additional benefit offered by availability of distributed and accurate topographic information. This enables orthorectification of remotely sensed data, hence facilitating geographic intercomparison of various data sets and creation of products which do not need additional processing for ingestion in GIS or data fusion. Current status Current spaceborne sources of information rely on techniques exploiting optical as well as radar sensors. The principal characteristic of all such techniques is that they tend to provide information about top of canopy surface and additional processing work and data might be needed to derive information about bare-earth heights. The key-advantages reside in the repeatability of systematic observations (to generate up-to date datasets) and the independence of acquisitions from ground conditions (such as political or accessibility issues). Currently flying optical sensors for high-resolution stereo-mapping (posting of less than 10m) include SPOT5 or IKONOS which are extremely expensive, and PRISM onboard ALOS (launched in Q1 2006), availability of which may be linked to data-policies issues. The key limitation of optical sensors (including also ASTER) resides on cloud cover, which may drastically affect availability of up-to-date information in tropical and subtropical areas. Radar data acquired by ERS, J-ERS1, Radarsat-1, SRTM, Envisat or ALOS can also provide DEMs, exploiting different techniques. In the case of InSAR, performances depend on geometry constrains, surface conditions or atmospheric/tropospheric artifacts. Use of large data-stacks has been shown useful to improve DEM quality, implying intensive processing. In addition, C and especially L-band measurements refer to an average surface defined by the first cm of ground (or canopy), not to the top surface itself. Concerning with the global availability of SRTM data at 90m, it has to be noted that this accounts for 80% of the land surface and its geographic coverage is comprised between 56 degrees south and 60 degrees north. On the other hand, ground-based observations may provide data for DTM (especially when based on ground surveys, e.g. leveling or GPS) or DSM (when based on airborne datasets such as radar or optical stereo-pairs or Lidar). The main issues in the case of ground-based observations reside in the cost of ground-surveys, maintenance of networks as well as accessibility and weather conditions. Current plans Radarsat-2, to be launched in early 2007, will collect very fine to fine resolution C-band SAR data. Spaceborne acquisitions of X-band data should be guaranteed by the foreseen Terrasar-X and CosmoSkymed satellite constellations. Necessary Improvements and Major Gaps There is a need to extend the coverage of the existing 90m SRTM datasets, for instance by integrating it with other existing datasets (e.g. SPOT-5 or ERS acquisitions in Tandem mode). A major gap is then represented by the lack of a medium scale global dataset, publicly available: SRTM data exist at suitable resolution over 80% of the land areas, but US data policy constraints limit their availability. For areas of low topography such as coastal zones and flood plains, there is a need for topographic information with much higher resolution (in the order of 1 m). Local, high quality, standardized topographic information should be made available, to facilitate validation efforts of global datasets. In addition, to facilitate the combination of elevation data from different sources and to incorporate the elevation data into other information and products, a common geodetic reference frame is required. The adoption and implementation of a global datum based on the ITRF (or WGS 84) is probably needed. One of the important uses of topographic data is to allow the orthorectification of satellite data as was carried for the Landsat GeoCover products. For data fusion and activities such as change detection this is extremely beneficial. It is recommended that all medium and finer resolution remote sensing data is routinely made available as orthorectified products. Product-specific Critical issues The SRTM data base is only openly available for the US at 30m horizontal resolution but exists for all areas for which there is SRTM coverage (58 degrees N and 60 degrees south). It is recommended that these extremely valuable data be made openly available for all areas. Principal recommendations Improve global coverage of 90m SRTM datasets (possibly by integration with SPOT-5 or ERS acquisitions in tandem mode). Ensure public availability of the 30m spacing DEM from the SRTM. Provide very high resolution (1m) topographic information for low/flood prone areas Distribute data using a common geodetic reference frame. Provide terrestrial remote sensing data in an orthorectified form so far as possible. Integration issues Validation and Quality Assessment Principles More and more, satellite-derived products are constructed to represent geophysical parameters. As these products are increasingly used in operational and scientific environments, it is important to understand the accuracy with which they represent the particular phenomenon. The accuracy should be determined by comparison with independent data sets with known, and, presumably, higher, accuracy. The process of these comparisons is referred to as validation. Validation activities are most effective when they involve those with an intimate knowledge of the products input data and algorithms. Further, validation results can also lead to algorithm and product improvements. It is therefore more efficient and logical that validation responsibility lies with the data producer. In this regard, funding for product validation needs to be included in the budget of the agency responsible for generating the product. There are several overarching principles that will help ensure the maximum utility from validation activities. First, the overall objective should be user-focused. The accuracy statements associated with the product should provide the user with sufficient information to justify using the product and assess the impact on the intended use. Second, transparency and collaboration will promote rigor and integrity. Data used for validation should be made available so that the results are both reproducible and verifiable. Third, sensors change over time, algorithms are modified, and user needs evolve. Given these realities, although an intensive validation activity is needed when the product is first generated, a validation strategy is needed during production and throughout the life of the product. Finally, with the time and effort needed to implement a program of validation, both producers and users should anticipate incremental validation results. It is important that validation results are subjected to peer review and are published in the open literature. It is expected that unvalidated (Beta) products will be distributed for community evaluation while in the process of being validated. Inter-comparison with another product of unknown accuracy as part of the evaluation can provide an early and useful indication of congruency and helps build confidence, but does not constitute validation. True validation can start at just a handful of sites. However, continued work should attempt to further the validation progression by expanding to a more widely distributed set of sites representative of the range of conditions encountered. Ultimately, a rigorous and statistically robust validation would require a statistical sample that represents global conditions and variability. Current Status The CEOS Working group on Calibration and Validation was established in 1984 with an emphasis on instrument calibration. CEOS has defined validation as the process of assessing by independent means the quality of the data products derived from the system outputs (Justice et al., 2000). In 1999, with the recognition of the need for international validation coordination, the Land Product Validation (LPV) Working Group was established. Currently the LPV has coordination initiatives underway for land cover, vegetation continuous fields, albedo, fire, LAI and surface temperature ( HYPERLINK "http://lpvs.gsfc.nasa.gov/" http://lpvs.gsfc.nasa.gov/,). Accuracy assessment of land cover products has been undertaken for the last three decades, providing information on class and overall accuracy, starting with individual or local imagery and progressing to global, multi-temporal products. With the increase in computing capacity it has become feasible for individual scientists to generate and distribute global products. The availability of the same global products from multiple sources emphasizes the need for validation standards. In this context, harmonization and validation are parallel efforts towards interoperability, product synergy, and improved usability of land cover products (Herold et al., 2006a). Understanding comparative map product accuracies is key to build user confidence for applying a particular product. Global product validation requires evaluation of accuracy over the range of conditions for which it is provided. In some cases different agencies or researchers are generating the same product from similar or different data sets. Validation of satellite imagery is commonly undertaken using a product of known accuracy, generated at a finer spatial resolution, providing a more precise representation of the land surface. For field-collected data to be used in validation it is important to consider questions of the scale of measurement. With the costs associated with field data collection and ultra-fine spatial resolution imagery, there are advantages to be gained from international cooperation on global and regional validation efforts. However, such collaboration and a distributed approach to validation requires the establishment of standards and validation protocols. The first such cooperation was associated with the land cover product generated under the auspices of IGBP-DIS (Loveland et al., 2000). A stratified random sample was undertaken, and international cooperation provided interpretation of high-resolution global images. Another example is the Global Land Cover 2000 product (Mayaux et al., 2006). The joint experiences have been recently compiled into a consensus land cover validation protocol and reporting standards and a hierarchy for validation (Strahler et al., 2006). Stage 1 Validation: product accuracy has been estimated using a small number of independent measurements obtained from selected locations and time periods. Validation assessed locally under a limited range of geographic conditions for a limited period of time. Stage 2 Validation: product accuracy has been assessed over a widely distributed set of locations and time periods. Validation assessed over a significant range of geographic conditions and for multiple time periods and seasons. Stage 3 Validation: product accuracy has been assessed and the uncertainties in the product well established via independent measurements in a systematic and statistically robust way representing global conditions. Validation assessed over the full range of global conditions for all time periods. GOFC/GOLD has been promoting international cooperation for the validation of global products through its regional networks (e.g. Justice et al. 2005, Roy et al 2005, Jin et al 2006). Roy et al (2005) developed a consensus protocol for the validation of moderate resolution burned area products. Community participation in validation has the added advantage of developing a user community closely familiar with the product accuracy. Major gaps and necessary enhancements In the near term there are a number of activities which will contribute to international cooperation on product validation. The CEOS LPV will continue to help develop, document and promote community protocols and standards for land product validation. GOFC-GOLD and the CEOS LPV team have started to developed a joint land cover harmonization and validation initiative (Herold et al., 2006a). A conceptual system for operational land cover validation has been developed including an establishment and operational phase. The implementation relies on the contribution of a number of key international partners taking responsibility for different components of the system (Herold et al, 2006b). The political framework and the organizations for international cooperation as well as methodological resources exist to implement validation as part of operational land cover observations. However, previous efforts have suffered from a lack of funding since resources for validation and harmonization have not been properly allocated during initial project or program developments. ESA and NASA should continue to cooperate on global land cover utilizing the GOFC-GOLF regional networks. On the longer term the CEOS LPV should establish a core set of calibration and validation sites that can be used to assess products from individual sensors as well as the growing time-series from multiple sensors. GOFC/GOLD should continue to maintain communication with user communities to establish how to relay accuracy information, how accurate the products need to be, and how close current and planned efforts are to meeting those needs. Principal recommendations Any land product which is being developed in the framework of GEOSS must be validated and the associated accuracy assessment provided. Validation results and the associated validation data sets should be made openly available and results should be published in the open literature. Validation activities should continue through the life the sensor/product. Results can be developed incrementally, but for critical climate data records, the ultimate goal is to have validation results across a statistically valid, globally representative sample. The Space Agencies should support the CEOS LPV working group in its efforts to realize efficiencies and establish protocols for coordinated international land product validation. CEOS LPV should engage the user community in its validation activities, in particular, validation should involve the regional networks of scientists participating in GOFC/GOLD, IGBP-START and other networks that may evolve from GEOSS. Data fusion for analysis and modeling Data fusion is the process of integrating data from different sources, and often of a different nature, to increase the quality of information over that contained in either data source alone. The aim of data fusion is to derive an unambiguous data product that integrates the richness and complexity of disparate data from different sources. It may involve the integration of multiple sensor observations (collected by remote sensing or in situ) or the integration of single sensor observations collected over space or time, for instance to fill in or replace missing data. The challenge of data fusion is to efficiently and effectively integrate those data which can be of different types, collected from different platforms with different orbital geometry, and having different spectral, temporal, and spatial resolution. The focus in the framework of this IGOL report is given to multiple source data integration, which represents a main challenge, given the fact that observations are taken by different devices, each having its own characteristics in terms of properties measured, temporal frequency and size of the sampled observable. It has to be noted that data fusion requires development of new analytical methods to integrate disparate data and sources of uncertainty, for which detailed, specific analytical methods have already been carefully developed. Observation requirements Accurate geospatial data alignment is the foundation for all data fusion activities, therefore orthorectified, systematically produced products are needed. Careful quality control of input and output data, analysis of data product sensitivity to parameters and structure of algorithm, and independent checks on the data product behavior, including ground-truthing against independent data of sufficient spatial and temporal resolution, are equally important. Current status Data fusion has been used for improving land cover mapping, land use classification, and forest attribute description, assessing urban land expansion and impacts on net primary productivity, describing spatial distribution of soils and soil salinity, soil moisture, and soil erosion risk, and monitoring crop yield potential and documenting agricultural practices. For example, combining interferometric synthetic aperture radar data with other remotely sensed data can enhance characterization of forest biophysical variables. Similarly, both visible and radar data can be used to monitor crop condition, but fusing data from both sources collected at different times allows integration of disparate sources of information about crop condition. Major gaps and necessary enhancements Progress in data fusion activities is limited by incongruities of potentially compatible data, yet-to-be-developed methods for fusion of particular sources of data, and limited independent data for evaluating derived data sets. Data incompatibility will obviously be a problem for data collected in distinct places or time periods, but can also be a barrier if data cannot be accurately geocoded, if data are not reliably collected, or if data distribution lags. The key requirement is therefore to produce and release orthorectified products, expressed in a common reference system. The potential to adjust orbital synchrony (or asynchrony) to enhance the utility of data fusion products should be considered. Development of new data fusion products is contingent upon new research exploring the potential for fusion of new and existing data and into new data fusion techniques; we strongly recommend supporting this type of research. We also recommend quality assessment and evaluation to document bias and variance, for instance associating error-bars or quality assessment to each spaceborne-derived product. Principal recommendations Ensure that land products are orthorectified and expressed in a common reference system. Support research of potential for new data fusion products and techniques. Support efforts to carry out quality assessments of data fusion products. Data assimilation Model-data synthesis Model-data synthesis is a family of techniques that enables integration of a model of a system with independent observational data about the system and associated estimates of uncertainty for both to arrive at the best possible match between model output and observations. Model-data synthesis was developed to improve numerical weather modeling by updating model parameters to best match observations. As for weather modeling, integrating model output and land observations, and weighting those data according to their uncertainties, model-data synthesis for land applications is capable of producing better consistency across data sources, thus enhancing scientific credibility. Data assimilation is one type of model-data synthesis that enables adjustment to model parameterizations in order to optimize model results to a known state of the system at a particular time and spatial domain. Data assimilation interpolation techniques can take fullest advantage of models to fill gaps in observations given synoptic observation of a state or flux and the necessary drivers. Data assimilation can be used to integrate synoptic observations and modeling applications, providing insight into land system process associated with biophysical and human derived changes. Data assimilation has been extensively used to generate better constrained estimates of carbon fluxes from terrestrial ecosystems by integrating remotely sensed observations with in situ flux data or other ancillary data. For example, regional carbon budgets can be generated using bottom-up ecosystem modeling or top-down atmospheric CO2 concentration data. Data assimilation enables information transfers between these two observation systems to derive the best carbon budget solution. Data assimilation also acts as a framework to better integrate uncertainties and errors associated with either the model or observation system. Data assimilation techniques are extremely powerful, though computer intensive, due to the optimization routines associated with error reduction. New techniques involving Markov-chain Monte Carlo techniques are being adapted to statistically resolve parameter estimation within a set of observations. Several different numerical data assimilation techniques have been developed, and results can be used either to adjust model parameters through model recalibration or to invert the model and optimize state variables. Data assimilation has been used to assess crop productivity and condition, for soil moisture monitoring, to estimate land and snow cover distribution, and to describe forest productivity, phenology and biophysical properties. The synoptic, repeatable, and uniform natures of remotely sensed data make them particularly amenable to integration using model-data assimilation. Major gaps and necessary enhancements Model-data synthesis applications for land are relatively new. Most examples of successful model-data synthesis have been carried out on small plots with good sources of independent constraint data. For broader-scale model-data synthesis with land observations, major research challenges remain. The inherent spatial heterogeneity of the land surface alone dictates finer resolution data to better constrain uncertainty, potentially leading to significant computation and interpretation challenges. The best observations for model-data synthesis are those with small measurement and representation errors. At a minimum, those uncertainties must be accurately characterized for the products to be ingested. Observations coincident with model output, at the same spatial scale and most directly comparable with model output will enable the most robust model-data syntheses. Principal recommendations Support efforts to advance data assimilation methods for a wider range of land observations. Ensure that calibration/validation efforts strive to generate estimates of observational uncertainty. Facilitate efforts to coordinate in situ and remote observations to ensure compatibility between disparate data sources. Data Delivery A grand challenge for land observation systems is to make land observation products and science relevant and accessible to a virtually unlimited potential user community. This must be done in ways that respect the policies of data originators and ensures confidentiality and sensitivity where appropriate. Open and unrestricted access, to the greatest extent possible, should be the ambition of data delivery capabilities for land observations. Data and product access The ideal access strategy for land observations involves unrestricted and no-cost access to all data and products and all data providers are urged to establish open access data policies. For clarification, such a data delivery system uses a comprehensive definition of data and products. These not only include observation, in situ, survey, and other geospatial data sets, but also information products, reports, assessments, metadata, and other forms of documentation. Because land characteristics and processes are fundamentally place-based and collectively represent the geographic variability of the Earth, all archived land observation data will require some level of geocoding in order to be gathered, combined, and used in environmental models. Standards for geocoding, and implementation of appropriate data integration and analysis capabilities should be an immediate priority so that the overall system capabilities support the necessary functions. The efforts of the Open Geospatial Consortium (OGC) should be considered in the establishment of land observation data system geospatial analytical capabilities. Data access policies The two primary data access components involve data use restrictions and charging for data. Access to land observation data can be restricted by copyright, privacy, or national security or interest issues. For example, the highest quality global digital elevation model data are classified, and access is restricted. Higher resolution data are necessary for activities like hydrological modeling and land use planning. Strong encouragement and support should be given to efforts to lower barriers to access either by wholly enabling new access to exiting data or developing data access systems that enable retention of some modicum of spatial uncertainty, anonymity, or recognition yet still maintain copyright, privacy, or security standards. Socio-economic and in situ inventory data are often restricted due to confidentiality concerns relating to individual privacy concerns. Biodiversity data are often controlled due to either perceived or real threats to the survival of endangered species. In both cases, data aggregation or synthesis capabilities are necessary to respect the confidentiality of all data with real restrictions. Data pricing policies create significant barriers for many types of observations and applications. Costs certainly restrict access to data. Like lowering other access barriers, lowering costs would increase use of land observation data. In many cases costs are limited to costs associated with distribution of a copy of the data to a user. Such minimal costs are appropriate and present minimal access limitation. In cases where costs are higher, policies that promote access to the widest variety of users will be best. Land observation effectiveness is impacted by data charging policies. Data users must respect national data pricing policies, but should negotiate with programs to make costs as low as possible. Implementation of the recommendations offered by GEOSS to make data as inexpensive as possible or at the marginal cost of reproduction is of high importance. An example of the right direction for an appropriate data cost policy in that by ESA which, since 1999, is providing worldwide access to its data for science and applications development at cost of reproduction. The impact of eliminating charging for products can be seen in the cases of Landsat Geocover products and CBERS products. For both products the elimination of charging led to more than a magnitude increase in distribution. It is unreasonable to expect data generators to provide no-cost customization of data sets for special purposes of single customers. Payment for custom product generation is appropriate in any setting. However, costs associated with post-production processing like extraction, subsampling, re-formatting, etc. can be reduced through advancement of visualization software and creation of more refined products. Efforts to advance processing software development and to generate easily manipulable land data products should be supported. Data documentation policies Several of the challenges to land observation data access arise from the vast number of diverse observations collected at different scales and times. Data collection, authentication, calibration, and quality assurance are absolutely necessary, but can be time consuming and can significantly limit or delay access. For satellite remotely sensed data, efforts to generate validated products have greatly enhanced data access. On the other hand, many data are not amenable to remote sensing and must be collected in situ. In situ data collection is more labor intensive and geographically restricted, and in situ may be collected for slightly different purposes in different places, may be a-spatial, and are likely to be less uniform in space and time. Production of consistent, comprehensive, validated data products from in situ data is a challenge and likely a strong limitation to data accessibility and utility, potentially precluding some applications. Documentation and metadata are the necessary foundation upon which methods and software to integrate disparate data can be built. Efforts should be supported to assemble data from different sources, develop sound metadata, catalog by those metadata, and collaboratively develop interoperable data products. We strongly support efforts to complete and clarify metadata documentation of all processing steps and associated estimates of uncertainty, which will enable broad, accurate, efficient use of land observations. The growing need to address environmental and societal concerns demonstrates the need to minimize barriers to land observation data access and use. It is clear that lowering access barriers, including costs will serve as an investment in collaboration and exchange, advancing development of data processing and enhancing data utility; thus we recommend that costs be kept to a minimum and access is open to all users. Principal recommendations Encourage lowering barriers to access either by wholly enabling new access to exiting data or developing data access systems that enable access while maintaining privacy and security standards. Encourage implementation of GEOSS recommendations to make data available at the marginal cost of reproduction. Support research to develop methods and software to lower the cost of post-processing data customization. Data and Information Delivery Systems Land observation data delivery systems must consist of both technology-based capabilities and programmatic functions and policies. Paramount is data sharing policies the fundamental motivators of the delivery system. Delivery systems must provide support and incentives to observation generators to change their ways so that they are more forthcoming and sharing of data. The technology-based elements of land observation should start with the formal identification and development of key nodes that make up a dispersed land observation data and information system. This system must provide the means for the community to catalog, search, analyze, and retrieve the data and information components critical to achieving land observation needs. A practical implementation strategy should consist of identifying the key data and information system functions and the centers that currently have capabilities and capacity that contribute to land observation. The optimal land observation data delivery system will consist of a multilevel network of global and national data sets, Earth observation data, interpreted products, in situ data, survey data, a wide collection of information products related to societal relevant problems, and all supporting documentation that make all data sets meaningful. This will require a distributed network of information systems that ensure rapid and equal access to all holdings. Especially important is the establishment of a land observation data delivery system that reaches out to all types of users with the kinds of data through results that are needed to solve a problem. An optimal system should consist of two levels connected by clearinghouse capabilities. Level I will be a relatively small number of observation data providers. These organizations, largely comprised of space agencies, will provide access to processed satellite images (e.g., ortho-rectified imagery), and robust distribution capabilities. Level II consists of the specialized network of theme data producers and investigative centers that provide everything from in situ and survey data to final products and information. Both levels and the distributed centers of each level must be connected via a clearinghouse capability that permits searching for products and other forms of results. This system must include the ability to provide access to a wide range of disparate data types, including Earth observation data, other geospatial maps and statistics, and in situ data. It will also be necessary to provide the functionality needed for assessing and accessing data integrity. It is also necessary to provide some level of functionality for distributed data analysis and integrity. Finally, long-term data archiving centers and data management protocols must be established that ensure that land observation data and information products can be used, reused, and stored as evidence of the condition of the planets landscape. The development of data dissemination capabilities should be transparent and embrace efforts underway by other organizations. For example, the CEOS Working Group on Information Systems and Services (WGISS) efforts to establish global network services should be monitored, and experience shared with the GCOS global data centers, such as the World Glacier Monitoring Service, the National Snow and Ice Data Center and the Global Run-Off Data Centre. Provide Access to Data Improvements in data access are crucial if the full utilization of Earth observations and other required data are to be achieved. This means there have to be integrated systems across agencies for data query, data browsing, data ordering and data delivery. Data and information holdings exist in various forms and in many places, each with varying levels of capabilities. Connecting users to data is going to require a combination of technical and programmatic advancements. The technical issues that must be immediately addressed related to Internet connectivity, data exchange protocols, ensuring adequate and equitable network transfers, and catalog compatibility and interoperability. Programmatic topics must focus on providing as open and unfettered access to data as is programmatically possible. Providing for wider and easier data and information access for users, assisting users to locate and utilize data, and enabling them to collaborate with other users, must be a goal. Crediting incentives for data and information sharing, waiving of fees, copyrights, and other use restrictions are particularly important. The initial nodes of the data access system should rely on proven existing capabilities (e.g., the vast Earth observation archives of ESA, NASA, and USGS; geospatial holdings of national mapping agencies; and inventory statistics of national natural resources agencies). Next, connecting secondary level data holdings will be required. Finally, upgrading data and information connectivity linking smaller data centers and new users will be required. The CEOS WGISS is pursuing the development of global data services for users of Earth observation information (including satellite and related datasets). While this effort is focused on wider use of Earth observation data, the result of this development should serve the broader goals of land observation. Functionality for Assessing and Documenting Data Integrity Effective use of land observation data holdings depends on data centers following community data standards, determining the accuracy, precision, or other appropriate measures of uncertainty, and ensuring that all data and information are documented and meet minimum metadata standards. The Global Spatial Data Initiative (GSDI) forums on data and metadata standards should be considered as a land observation standard. GSDI strives to foster spatial data infrastructures that support sustainable social, economic, and environmental systems integrated from local to global scales, and it promotes the informed and responsible use of geographic information and spatial technologies for the benefit of society goals consistent with the needs for land observations. There are considerable differences in the emphasis various data providers place on validation and calibration. Generally, the remote sensing land products producers have made much progress in including some type of accuracy assessment in mapping projects. It is important that accuracy assessments based on community standards be part of all data collection efforts. When wading in a sea of related, overlapping, or seemingly similar datasets, it is absolutely essential that prospective data and information users have the ability to make wise choices. At a minimum, accurate, complete and timely metadata standards are vital to successful utilization of land observations. Data Mining and Analysis Capabilities Enhanced capacity to process, assemble, and analyze observations from multiple sources is needed for the full range of land observation data including satellite and in situ observations from National Resource Management agencies and international organizations. In the near future, as data archives grow in content and volume, it will become more efficient to establish distributed data mining and analysis capabilities than to transfer all of the required data to a home workstation. Required functionality includes the provision of distributed capabilities for computing, data search, algorithm development, data caching and temporary storage, and cooperative tools. In addition, terrestrial monitoring will require extensive data mining in order to establish baseline conditions and variations from normal. The challenges associated with data mining and distributed analytical functions begin with a single fundamental issue. Distributed Archiving and Management Systems Archival land observations will most likely remain distributed with little centralization under any one agency. Land observation partners must act together to ensure long-term archiving and management of land observation data and information. Some actors, such as the USGS EROS, have well-established data archive management capabilities, but others with smaller holdings follow more ad hoc data archiving and management strategies. A minimum set of standards that ensures permanence and long-term access to the archives of data needed within the land observation community must be immediately established. Principal recommendations Foster collaboration for development of a distributed network of information systems that ensure rapid and equal access to all holdings. Encourage establishment of a land observation data delivery system that provides the kinds of data needed to solve problems. Ensure that land observation data and information products can be used, reused, and archived as evidence of the condition of the planets landscape and change over time. Integrate systems for data query, data browsing, data ordering and data delivery across agencies. This requires addressing data exchange protocols, ensuring adequate and equitable network transfers, and cataloging compatibility and interoperability. Consider the Global Spatial Data Initiative forums on data and metadata standards as a land observation standard. Strongly encourage implementation of accurate, complete and timely metadata standards. Establish immediately a minimum set of standards that ensures permanence and long-term access to the archives of land observation data. capacity building Background Capacity building is a priority for all governments and organizations and issues encompassing human, scientific, technological, organizational, institutional and resource capabilities should be core components of the mandate and work of IGOL. The World Summit on Sustainable Development (WSSD, October 2002) reconfirmed the priority of building capacity to assist developing countries to obtain their sustainable development goals. Over thirty-five references are made to capacity building in the WSSD Plan of Implementation. WSSD recommended to provide financial resources to developing countries to meet their capacity needs for training, technical know how and strengthening national institutions. The issue of capacity building has become a major priority within the global Conventions. However, the current capacity building efforts remain highly fragmented and uncoordinated. Principles The contemporary view of capacity-building goes beyond the conventional perception of training. The central concerns are to manage change, to enhance coordination, to foster communication, and to ensure that data and information are shared - require a broad and holistic view of capacity development. The basic rationale for capacity building include: closing digital divide, fostering economic growth, enhancing social mobility, promoting equity, and spreading democracy- access to information and participation in the decision making process. Broad operational principles for effective capacity building includes ensuring national ownership and leadership, basing capacity building efforts on national priority issues, integrating capacity building in wider science and technology input to sustainable development efforts, promoting partnerships, accommodating the dynamic nature of capacity building, and adopting a learning-by-doing approach. Elements of capacity building Within the context of land observations, generally speaking, capacity building encompasses following: Development of national research, monitoring and observations capacity, including training in observations, assessment and early warning; Support to national and regional institutions in data collection, analysis and monitoring of trends; Access to scientific and technological information, including information on state-of-the-art technologies; Education and awareness raising, including networking among universities with programs of excellence in the field. Production and communication of data and information content that supports programs and policies Land observation partners could contribute towards all of them with various degrees of intensity and depending upon specific requirements of countries. Principal actions needed Currently there are a number of barriers related to collection of land based observations in developing countries which needs to be overcome. Some of the specific capacity building actions needed are: Educational and Enabling Activities Training of more personnel, training of trainers in in situ data collection, spaceborne imagery processing and interpretation and web mapping tools. Training in standardized land cover and ecosystem classification systems. Fostering of twinning arrangements between appropriate North- South and South- South institutions by sponsors of IGOL. Strengthening of capacities of selected universities in land observations. Provision of standardized data collection and analysis manuals, high quality teaching and promotional materials, support to the development of distance learning modules. Facilitating adequate dialogue, by organizing workshops and development and dissemination of outreach materials etc, between decision makers and technical personnel as most of decision makers are not aware of potential benefits of such tools and technologies. Build upon success of Google Earth/ NASAWorld Wind. Mobilizing resources. Assist in mobilizing financial resources to meet the high costs of equipment and maintenance particularly for the African region. Explore potential use of US$100 laptops being developed by MIT. Assist in establishing community of users by funding internet based discussion groups and blogs and networks. Access to data, models and tool kits. Disseminate information on best practices and case studies related to integration of data sets from multiple sources for solving real life problems. Space agencies in cooperation with international organizations should provide high quality, cloud free, orthorectified satellite data and ultra-fine resolution DEMs at an affordable cost/free to developing countries institutions. Research agencies in partnership with international organizations should provide access to appropriate rapid mapping, modeling and decision support system (DSS) tools kits to users in developing countries. Advocate for integration of various information systems following common standards and protocols for improved interoperability. Advocate for investments in establishing operational land cover monitoring capabilities instead of ad hoc observation exercises. Advocate for improved Internet connectivity to research institutions and universities in collaboration with the private sector for faster access to satellite observations and other derived products. Coordination and Partnerships. Provide a platform for interagency cooperation within a country. Develop synergy with GEO and other capacity building efforts. It is recognized that many of these recommendations are common to the needs described in other themes. It is clear that a more coordinated approach to capacity building is needed for earth observations in general Relation of IGOL to other Themes Transfers of matter and energy and drivers of those fluxes tightly link Land Theme observations with those of other IGOS-P Themes: the Atmospheric Chemistry, Carbon, Coastal, Geohazards, and Water. These tight linkages with other IGOS-P Themes amplify the importance of IGOL: land-based observations comprise important baseline data to many of the other IGOS-P Themes. Many observations evaluated in this Land Theme Report are required by the other Themes: Atmospheric Chemistry Theme: albedo for energy budgeting (biophysical observations) air pollutant emissions (human settlements and socioeconomic data) heat sinks (water availability, topography) Carbon Theme: vegetation status and activity (fire, biophysical observations, biodiversity) fossil fuel emissions (human settlements and socioeconomic data) bio-fuel production (land use) forest biomass inventories, harvests (land cover, biophysical observations) soil carbon stocks (soils, land cover, land use) lake, reservoir C stocks (water availability) wetland extent (land cover) Geohazards Theme: digital elevation models (topography) soil physical parameters (soils) Water Cycle Theme: soil moisture modeling (soils) surface water storage (water availability and use) wetland extent (land cover; water availability and use) water quality (water availability and use) Cryosphere Theme: frozen soils (soils) The domain of the Land Theme Report was identified by attempting to limit overlap with observational domains of the other IGOS-P Themes: Soil water content for drought and soil moisture monitoring were addressed in Water Cycle Theme. CO2 stock and flux observations on land were addressed in Carbon Theme. Frozen soils were not explicitly addressed in Land Theme, but they will form a central component of the Cryosphere Theme. Biodiversity in wetland and surface water systems was not singled out within Land Theme, but Land Theme recommendations apply to wetland and surface water systems. Aquaculture was not addressed in the Land Theme. Several classes of Land Theme observations spatially overlap with Coastal observations. Improving land observations to enhance societal benefits will rely upon observations and methods derived from other disciplines. Activities that promote interaction across disciplines and developing links across IGOS-P Themes will contribute to ensuring gaps are covered and redundancies minimized, while fostering coordination of observational priorities and diffusion of methods and applications across scientific communities. IMPLEMENTATION Strategy Implementation of the recommendations of IGOL will be the responsibility of numerous international and national bodies. In the following section (9.2) we highlight the main recommendations from this report and the body or bodies identified as responsible for them. In terms of overall international coordination, the report is being completed at a time of considerable flux in roles and responsibilities. IGOL was set up under the framework of the IGOS Partnership and this report is formally submitted to that body. One of the main mechanisms for implementation in the framework of IGOS-P was to set up an Implementation Team, with one of the IGOS partners agreeing to chair the Team. It is fair to comment that this approach has had only mixed success in large part because of the difficulty in capturing resources for the coordination activity. We do not recommend that such a body be set up for IGOL though as noted below we do recognize the need for oversight of progress in achieving the recommendations of IGOL Since the time that IGOL was created, an inter-governmental body, GEO, has been set up with responsibilities for the implementation of a comprehensive GEOSS. It has defined a large number of tasks several of which if successfully implemented will undoubtedly contribute to the goals of IGOL. Implementation of many of the coordination tasks recommended in this report are part of existing tasks or should form new tasks of GEO. It is anticipated that CEOS will continue to play a central role with respect to coordination of space observations either acting in its own right or through the chief body responsible for the coordination of implementation responding to tasks identified by GEO. Notwithstanding the significance of these overarching bodies, many of the coordination responsibilities for terrestrial observations still lay with UN agencies notably the FAO, UNEP, UNESCO and WMO and their subsidiary organizational structures. In terms of the international coordination of terrestrial observations, GTOS has the broadest current remit. A consideration of its current scope and that of IGOL clearly shows that the latter is significantly broader in many respects, though with its burgeoning activities in biodiversity and conservation and in situ flux measurements the differences have been lessened. It is recommended that GTOS review the scope of activities of IGOL and consider expanding its role to better match the totality of terrestrial observations discussed in this document. One of the tasks of a rescoped GTOS should be to monitor progress in the implementation of IGOL and advise the bodies responsible for implementation, as described in the next section, where progress is inadequate. Since GEO is the principal body charged with coordination of implementation we provide in the next section the mapping of IGOL recommendations to GEO tasks highlighting any tasks identified by IGOL which are not in the current tasks. The GEO tasks identify those charged with the implementation Mapping of IGOL Recommendations to GEO Tasks. (This is in progress and will be completed shortly.) Reports and Meetings IGOL report (EC-06-05, US-06-03) Agricultural monitoring meeting, Rome 2006 (AG-06-01; AG-2yr-1) Biodiversity meeting, Washington 2005 (BI-06-02, BI-06-04) Land cover (section 4.1) Develop acquisition strategies for land cover data that optimized coverage in time and space (AG-06-03; related to AG-6yr-4). Minimize interruption of fine (30m) resolution data (AR-06-07). Ensure future continuity of landsat-spot-type data (AR-06-07; EC-2yr-4; AG-2yr--6). Deploy a remote sensing system designed for land cover mapping at 1:250 000 scale that includes a multispectral scanner with 50-100m ground resolution and SAR with L-band frequency (10-50m ground resolution). (AG-06-03) Coordinate radar and optical data acquisition so that radar data can be used for regular, global monitoring of land cover (AR-06-02). Agree upon an internationally accepted land cover classification system. (related to EC-06-02, AG-06-04, and DA-06-04). Coordinate international collection of in situ data for calibration/validation efforts (AR-06-02). Land use (section 4.2) Develop a widely accepted land use classification system that is relevant to viability of short- and long-term land uses and also to land potential and sustainability and stratified by low and high land use intensity. (related to EC-06-02 and DA-06-04) For intensively used areas, map (1:500,000 scale) mechanized agriculture, pivot irrigation, tropical plantations, areas deforested, and urban areas. (AG-06-06; AG-2yr-7; AG-10yr-3) Integrate remotely sensed and in situ information to map (scale??) crop production, livestock densities, and fertilizer use. (AG-06-05; AG-2yr-1; AG-10yr-4) 4.3 Biophysical/ecosystem services Ensure continued generation of gridded fAPAR and LAI (related to EC-6yr-5). Reprocess available archives of fAPAR and LAI to generate and deliver global, coherent and internationally agreed values. (related to EC-06-06, EC-6yr-5). Re-analyze the historical archives of NOAAs AVHRR instrument, ensuring the long-term consistency of the product with current estimates throughout the entire period. (related to EC-06-06) CEOS Working Group on Calibration/Validation should continue to lead international benchmarking and product intercomparison and validation exercises including fAPAR and LAI. These efforts should take full advantage of existing networks of reference sites for in-situ measurements whenever possible (related to DA-06-02). Fire (section4.4) Coordinate an international network of geostationary imagers, providing global active fire detection every 15-30 minutes and make these data available in near real time for fire alert and management (AG-2yr-8). Modify the NPOESS VIIRS instruments for the non-saturated detection of active fires at 3.9 microns. Monthly and near real time burned area products should be included in the operational product suite from NPOESS. Support a coordinated international effort to validate the current and future global burned area products to CEOS Land Validation Stage 3. The GOFC/GOLD Regional Networks provide an opportunity for expert product validation (related to DA-06-02). Coordinate and target acquisition of data from the international high resolution assets to provide high resolution imagery (<20m) of large and hazardous fire events within 48 hours of the event. The data need to be affordable and easily accessible by the international fire management and research community. Future high resolution systems should include the capability for active fire detection (related to DI-6yr-5, AG-2yr-8). Enhance the access to and utility of their fire products, through the use of near real time delivery systems and web-gis. (DI-06-13, AG-2yr-8) Implement standardization of national fire data collection and reporting and promote open access to these data. These data should be spatially explicit and georeferenced. Initiate an international program on Global Fire Early Warning, integrating satellite and in-situ fire weather data. (DI-06-13) Biodiversity (section 4.5) Update world database of protected areas. Ensure availability and comparability of existing data collections. (BI-06-03; BI-06-05; BI-2yr-1, BI-2yr-4) Georeference all new socio-economic observations (related to AR-06-08; related to BI-2yr-3). Adopt a consensus ecosystem classification hierarchy and map product that describes how systems are mapped, how to add detail, and how to extend the classification scheme to all ecosystems (including human dominated systems). (BI-06-01, EC-06-02, and DA-06-04; EC-2yr-3, EC-6yr-1). Agricultural production (section 4.6) Standardize collection and dissemination of annual national statistical and other in situ data. (related to AG-06-07) Enhance rain gauge data collection network and lower barriers to timely data access. (related to AG-06-05; WA-2yr-1, WA-2yr-3) Improve seasonal climate prediction accuracy. (related to AG-06-05; related to WA-10yr-4) Provide high resolution (10-20m), cloud free coverage with a 5-10d return period. Ensure continuity of moderate resolution (1km, 100-300) observations. Improve targeting and reduce costs of hyperspatial (1-3m) imagery (related to EC-2yr-7). Improve spatial resolution, targeting, and height accuracy of radar altimetry and operationalize data collection (related to AR-06-06; related to CL-2yr-1). Provide near real-time access to regularly collected microwave data (10-30m) that can be fused with data from optical systems. Soils (section 4.7) Develop harmonized, small-scale (1:1,000,000) soil resource and terrain (SoTeR) database on a global scale (AG-6yr-2). Expand quality-controlled, georeferenced soil profile information collection, particularly in areas where none or very little of this information has become available (China, Former Soviet Union). Encourage a single body (most logically IUSS), to develop analytical and procedural standard methods that are binding for all organizations involved with soil classification, mapping, and analyses. Make interpretations of soil data more accessible and intelligible to non-soil scientists. Urban/SE (section4.8) Produce consistent global maps of human settlements on an annual basis using multiple data sources (EC-10yr-2). Enhance ability to use high spatial resolution data (related to EC-2yr-7). Improve methods for measuring building heights. Model spatial distribution of fossil fuel emission (CL-2yr-13). Develop more robust methods to relate radio and microwave emission data to poverty and economic activity. Provide enhanced spatial resolution of low light imagery. Water availability and use (Section 4.9) Ensure continuity of high resolution (10-30m) remotely sensed data (AR-06-07; EC-2yr-4). Test and implement methods to use hyperspectral imaging to measure water clarity (related to WA-6yr-14). Enhance methods to model water use/demand on the watershed scale. (WA-06-04) Map irrigated land area using high and moderate resolution remotely sensed data (AG-06-06; AG-2yr-7; AG-10yr-3). Topography (section 4.10) Improve global coverage of 90m SRTM datasets (possibly by integration with SPOT-5 or ERS acquisitions in tandem mode)(AR-06-06; DI-2yr-2). Ensure public availability of medium (?) scale global topographic dataset (AR-06-06). Provide high resolution (1m) topographic information for low/flood prone areas ( DI-6yr-1). Distribute data using a common geodetic reference frame (related to DA-06-06). Calibration/validation (section 5.1) Any land product which is being developed in the framework of GEOSS must be validated and the associated accuracy assessment provided. Validation results and the associated validation data sets should be made openly available and results should be published in the open literature. Validation activities should continue through the life the sensor/product. Results can be developed incrementally, but for critical climate data records, the ultimate goal is to have validation results across a statistically valid, globally representative sample. The Space Agencies should support the CEOS LPV working group in its efforts to realize efficiencies and establish protocols for coordinated international land product validation (AR-06-01, DA-06-02). CEOS LPV should engage the user community in its validation activities, in particular, validation should involve the regional networks of scientists participating in GOFC/GOLD, IGBP-START and other networks that may evolve from GEOSS (AR-06-01; EC-6yr-4). Data fusion (section 5.2) Ensure that land products are orthorectified and expressed in a common reference system (related to DA-06-04; DU-2yr-4). Support research of potential for new data fusion products and techniques (DA-06-03; HE-10yr-5; DU-2yr-2). Support efforts to carry out quality assessments of data fusion products (DA-06-03). Data assimilation(section 5.3) Support efforts to advance data assimilation methods for a wider range of land observations (DA-06-03; WA-2yr-10; WA-6yr-5; DU-2yr-2). Ensure that calibration/validation efforts strive to generate estimates of observational uncertainty (related to DA-06-05). Facilitate efforts to coordinate in situ and remote observations to ensure compatibility between disparate data sources (AR-06-02; HE-2yr-4; CL-10yr-1; EC-2yr-7; AG-10yr-4; DU-2yr-3). Data and product access (section 6.1) Encourage lowering barriers to access either by wholly enabling new access to exiting data or developing data access systems that enable access while maintaining privacy and security standards (DA-06-01, DA-06-07; DU-10yr-1). Encourage implementation of GEOSS recommendations to make data available at the marginal cost of reproduction (DA-06-01). Support research to develop methods and software to lower the cost of post-processing data customization (DA-06-08). Data and information delivery (section 6.2) Foster collaboration for development of a distributed network of information systems that ensure rapid and equal access to all holdings (AR-06-05, DA-06-04, DA-06-06, related to AR-06-01, DA-06-01; WA-2yr-13; EC-2yr-6; DU-2yr-3; DU-6yr-1; DSU-10yr-1; DSU-10yr-2). Encourage establishment of a land observation data delivery system that provides the kinds of data needed to solve a problems. (US-06-01, DA-06-04, and related to US-06-02, US-06-03; HE-2yr-3; DSU-2yr-2; DSU-10yr-1) Ensure that land observation data and information products can be used, reused, and archived as evidence of the condition of the planets landscape and change over time (DA-06-05; DSU-10yr-2). Integrate systems for data query, data browsing, data ordering and data delivery across agencies. This requires addressing data exchange protocols, ensuring adequate and equitable network transfers, and cataloging compatibility and interoperability (AR-06-05, DA-06-01, related to AR-06-01; WA-6yr-4; DU-2yr-3; DU-6yr-1; AR-2yr-1; DSU-6yr-2; DSU-10yr-1). Consider the Global Spatial Data Initiative forums on data and metadata standards as a land observation standard (DA-06-06; DU-2yr-4). Strongly encourage implementation of accurate, complete and timely metadata standards (DA-06-04). Establish immediately a minimum set of standards that ensures permanence and long-term access to the archives of land observation data (hinted at but not specified in DA-06-05; HE-2yr-9 DU-2yr-1). Concluding comments In this final section we summarize some of the cross-cutting issues of the IGOL. The multiplicity and diversity of uses for land observations means that a complete integrated synthesis is not possible, nor wise. Certain observations are unique to particular stake-holders and there is a need to refer to the main document for those. Also requirements often differ in subtle ways. There are many calls for fine resolution optical data: for some applications the needs are for data with spatial resolutions near to 10m whereas for others 50m data will suffice; for some needs observations once every 16 days will provide adequate data sets but other applications require much more frequent imaging. In responding to the needs of the land community such differences must be considered. In most cases higher spatial and temporal resolutions meet most needs can provide the basis for deriving aggregated products, clearly this is not the case in for the coarser resolutions. Remote sensing observations are called for in all parts of the report, to continue existing capabilities, to make incremental additions and in some cases there is a call for new initiatives. Critical observations needing to be continued include fine (10-50m) resolution optical remote sensing. The Team has noted with concern the decline in availability of fine resolution data due to the problems with the Landsat ETM+. However the Team sees the increasing number of countries with systems providing fine resolution data and the opportunity for the development of a distributed global observation capability. In terms of moderate resolution sensors (100m -1km) there appears to be a good chance of achieving continuity but current plans for some sensors such as VIIRS will likely mean some reductions in capabilities relative to current ones. Several radars are now in orbit. The increasing value of their products calls for their continued deployment. Crucial incremental additions. For some purposes fine resolution data are collected with an adequate frequency but for several purposes such as several agricultural applications more frequent, cloud free observations are needed. Increasing the frequency of observations by coordination of existing and future assets is to be encouraged as proposed in the CEOS Virtual Land Surface Imaging (LSI) Constellation. In the future constellations with standard instrumentation designed specifically to provide high temporal imaging are needed. The use of Thermal IR data for energy balance and hydrological products is gaining ground, but we note that there are currently no plans to continue the observational capabilities of ASTER or ETM+. The inclusion of middle and thermal infrared sensors with fire detection capabilities on both fine and moderate resolution systems will help meet the needs of this growing community. The increasing spatial resolution of Geostationary systems makes them suitable for land studies and applications where there is high temporal variability, for example in fire monitoring. Increased awareness of the growing needs of the land community is needed by the geostationary data providers who traditionally serve just the meteorological community. The complementarity of optical and microwave sensors has long been recognized. The report calls for better coordination of optical and microwave acquisition strategies and data fusion tools to enhance their synergistic capabilities. Critical new initiatives. Increasing use of hyperspectral data from experimental missions such as EO-1 has led to a call for more observational assets and related research to improve products in areas as diverse as biodiversity, agriculture and water. The third dimension of vegetation is important for many users. We note the improvements in estimation of biomass by both microwave and optical sensors. The wide use of laser technology to characterize vegetation structure currently from airborne sensors is noted and space agencies are encouraged to develop spaceborne instruments for this purpose, expanding the coverage and repeatability of airborne systems. Combined short- (X- and C- band) and long-wave (L- and P-band) Radar observations in multiple polarizations (including cross-polarized or full-polarized) and in interferometric mode should greatly improve forest mapping in terms of structure, height and biomass as well as improving timely agricultural monitoring. Central role of land cover products. Throughout this document the need for reliable land cover products is repeatedly called for almost all sub-themes. Many such products have been developed for local, national and regional scales and fine tuning them to sub-global needs is appropriate. However many good reasons for global products are identified in this report. At moderate resolutions increasingly refined products have been created and will need to continue to be generated. At fine resolutions no such global products have been made. This is all the more remarkable given the availability of observations suitable for this purpose since 1972. The computational capacity to develop global products at fine resolutions is now available and the data acquisition strategy and support for development of such products are strongly recommended. Socio-economic products. The importance of socio-economic variables is apparent in many of the sub-themes including but not limited to land use, biodiversity and conservation, agriculture and human dimensions? We recognize that considerable effort and new initiatives will be needed to improve the availability of spatially explicit socio-economic data in a non-aggregated form for large parts of the World. In situ observations have been dealt with less comprehensively than remote sensing observations. Most of the latter are collected with national or local needs and standardization of collection procedures is often not adhered to, nor is there often a tradition of freely exchanging data. The need for improved in situ data collection occurs throughout the report and notably for biodiversity, agriculture, soils and fires. In the report there are several calls for improved standardization of in situ data such that national fire data collection and reporting and the adoption of international standards for much in situ data relevant to agriculture. Continuing efforts will be needed by the various terrestrial communities to make data available. For example there is a call for improved data exchange standards for several biophysical variables to improve their availability. In some fields such as forestry and agriculture, there are significant benefits to be gained from a greater integration of in situ and satellite observations. Importance of validation. One key place where in situ data and remote sensing come together is in product validation. The report stresses once again the importance of validating remote sensing-derived products, so that their value can be objectively tested and errors are reliably estimated. Unvalidated products should not be distributed by agencies. Validation results should be made openly available and the associated validation data should be easily accessible. Key role of improved classification schemes. To turn observations into useful products requires agreement on the characteristics of such products. We note the call in several parts of the report for improvements in internationally agreed classification schemes for ecosystem hierarchies and for those associated with land use and specifically for the built environment and associated infrastructure. Delivering observations and products. Users are obliged to obtain their data from a multiplicity of sources: from space agencies, from government departments, universities, research organizations and at times from commercial organizations. While we recognize the desirability and strength of distributed data systems, we believe that providing more coordination in accessing these diverse sources such as portals linking users to multiple related data sets would greatly improve the take-up of data. Similarly increased attention by data providers to metadata standards would greatly improve data inter-use. Data policies. A detailed discussion of the many different data policies for land data is beyond the scope of this report. We do stress the abundant empirical evidence that making data openly and freely available greatly increases its take-up and use. In particular it reduces the financial obstacle which prevents poorer countries and institutions from using the data for resource management and decision support. One key data set is currently in existence but is unavailable to most users. Topographic data from the SRTM mission are generally available only at 30m except for the United States itself. Elsewhere the spacing is 90m. Release of the finer resolution data would have a multiplicity of benefits for the land community. Capacity building Disparity in the understanding of what data are available, how to access, process and utilize them remains a major obstacle to broad uptake of the data, particularly in developing countries. The demand for capacity building in the use of land observations is high and comes primarily from the resource management community. Over the years a number of centers have been established offering training related to the observing systems and online tutorials have been developed. Procedures need to be put in place for updating these capabilities as new observing systems come on line. In the developing world there is wide variability in the capability to utilize the existing observing systems and the limited access to the full internet is often an obstacle. Lateral transfer of technologies between countries within a region offers an opportunity to focus on appropriate technologies, suited to a particular environment or set of resource problems. The observing systems are encouraged to increase emphasis on capacity building to help realize the full potential of their data. There are a number of new applications areas where land observations are starting to be used more extensively e.g. carbon accounting, biodiversity assessments, human health and the expanding urban environment. As these fields develop through increased research and development, there is a need to develop a process whereby new requirements can be integrated into the design of future systems. This will mean operational agencies expanding their user base beyond the traditional weather agencies. There exists a number of land observing systems which are currently managed independently and are operating in an uncoordinated way. There would be considerable advantage in them being integrated into a small number of system of systems, which would increase data availability and use, ensure data continuity and reduce gaps caused by shortcomings in national programs. Such systems come from an increasingly large number of countries. In this regard GEOSS holds much promise but continued involvement of the community of practice that has been developed during the IGOL requirements setting process will be needed. Acknowledgments The support of the following sponsors is gratefully acknowledged: European Space Agency, the UN Food and Agricultural Organization, the National Remote Sensing Center of China, the United Nations Environment Program and the United States Geological Survey. . References Amaral, S., Camara, G., Monteiro, A.M.V., Quintanilha, J.A. and Elvidge, C.D., 2005. Estimating population and energy consumption in Brazilian Amazonia using DMSP night-time satellite data. Computer, Environment and Urban Systems, v. 29: 179-195. Anderson, M.C. Kustasa, W.P. and Norman, J.M. 2007 Upscaling Flux Observations from Local to Continental Scales Using Thermal Remote Sensing, Agron J 99:240-254 Askne, J., Santoro, M., Smith, G., Fransson, J. E. S 2003, "Multitemporal repeat-pass SAR interferometry of boreal forests," IEEE Transactions on Geoscience and Remote Sensing, vol. 41, 7, pp. 1540-1550,. Arino O., Trebossen H, Achard F, Leroy M, Brockman C, Defourny P, Witt R, Latham J, Schmullius C, Plummer S, Laur H, Goryl P, Houghton N (2005) The Globcover Initiative, Proceedings of the MERIS (A)ATSR Workshop 2005, ESA SP-597 Arino O., Plummer S, Defrenne D (2005) Fire Disturbance: The Ten Years Time Series Of The ATSR World Fire Atlas, Proceedings of the MERIS (A)ATSR Workshop 2005, ESA SP-597  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-17731?func=lateral-link&doc_number=005602925&line_number=0006" Bartalev S.A.,  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-17732?func=lateral-link&doc_number=005602925&line_number=0021" Belward A.S.,  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-17733?func=lateral-link&doc_number=005602925&line_number=0022" Erchov D.V.,  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-17734?func=lateral-link&doc_number=005602925&line_number=0023" Isaev A.S., (2003),  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-17730?func=lateral-link&doc_number=005602925&line_number=0007" A new SPOT4-VEGETATION derived land cover map of Northern Eurasia, International Journal of Remote Sensing24, no.9 p. 1977-1982 Bartholome, E. M. and Belward A. S., 2005, GLC2000; a new approach to global land cover mapping from Earth Observation data, International Journal of Remote Sensing, 26, 1959 - 1977 Balmford, A., Bennun, L., Brink, B.T., Cooper D., Ct, I.M., Crane, P., Dobson, A., Dudley, N., Dutton, I., Green, R.E., Gregory, R.D., Harrison, J., Kennedy, E.T., Kremen, C., Leader-Williams, N., Lovejoy, T.E., Mace, G., May, R., Mayaux, P., Morling, P., Phillips, J., Redford, K., Ricketts, T.H., Rodriguez, J.P., Sanjayan, M., Schei, P.J., van Jaarsveld, A.S. and Walther, B.A., 2005, The Convention on Biological Diversitys 2010 Target, Science, 307, 212-213.  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-69442?func=lateral-link&doc_number=005414419&line_number=0003" Boschetti, M.,  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-69443?func=lateral-link&doc_number=005414419&line_number=0017" Bocchi, S., and  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-69444?func=lateral-link&doc_number=005414419&line_number=0018" Brivio, P. (.2007. A.,  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-69441?func=lateral-link&doc_number=005414419&line_number=0004" Assessment of pasture production in the Italian Alps using spectrometric and remote sensing information, Agriculture Ecosystems & Environment118 (1-4): 267-272 Jan 2007 Carmona-Moreno, C., Belward, A., Malingreau, J-P, Hartley, A., Garcia-Alegre, M., Antonovskiy, M., Buchshtaber, V, and Pivovarov, V., 2005, Characterising interannual variations in global fire calendar using data from Earth observing satellites, Global Change Biology, 11, 1537 1555 CNES 2000 To be completed. Di Gregorio, A., and Jansen, L.J.M., 2000, Land Cover Classification System, concepts and user manual, GCP/RAF/287/ITA Africover (Food and Agriculture Organization of the United Nations Publishing Service, Viale delle Terme di Caracalla, 00100, Rome, Italy) 179 pp Diner, D. J., B. H. Braswell, R. Davies, N. Gobron, J. Hu, Y. Jin, R. A. Kahn, Y. Knyazikhin, N. Loeb, J.-P. Muller, A. W. Nolin, B. Pinty, C. B. Schaaf, G. Seiz, and J. Stroeve 2005. 'The value of multiangle measurements for retrieving structurally and radiatively consistent properties of clouds, aerosols, and surfaces', Remote Sensing of Environment, 97, 495-518 Dudley et al. 1999 To be completed Dugelby and Libby 1998 To be completed Dull C.W. and Lee B., 2001. Satellite earth observation information requirements of the fire management community. In Global and Regional Fire Monitoring from Space: planning a coordinated international cooperation. Eds. F.J. Ahern, J. Goldammer and C.O. Justice, pp.19-33. SPB Publishing, The Hague. Ebener, S., Murray, C., Tandon, A., Elvidge, C. 2005 From wealth to health: modeling the distribution of income per capita at the sub-national level using nighttime lights imagery. International Journal of Health Geographics, 4:5. EEA 1995 To be completed ESA 2004 To be completed FAO 2003 To be completed FAO 2004 To be completed FAO 2007. Gridded livestock of the world. , Rome.  HYPERLINK "http://www.fao.org/ag/againfo /resources" http://www.fao.org/ag/againfo /resources /en/glw/default.html). Fransson, J. E. S., Walter, F., Ulander, L. M. H., 2000. "Estimation of forest parameters using VHF SAR data," IEEE Transactions on Geoscience and Remote Sensing, vol. 38, 1, pp. 720-727,  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-15582?func=lateral-link&doc_number=005552569&line_number=0006" Friedl M.A., et al., 2002,  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-15581?func=lateral-link&doc_number=005552569&line_number=0007" Global land cover mapping from MODIS: Algorithms and early results, Remote Sensing of Environment83, no.1-2 (2002) p. 287-302 George, H and Nachtergaele F.O. (2002) Land Use data, in Global Environmental Databases: Present Situation; Future Directions (Volume 2) Edited by R. Tateishi and D. Hastings, pp 53-65. Giglio, L., Descloitres, J., Justice, C. O. and Kaufman, Y., 2003, An enhanced contextual fire detection algorithm for MODIS. Remote Sensing of Environment, 87, 273-282. GLP 2005. Science Plan and Implementation Strategy. IGBP Report No. 53/IHDP Report No. 19. IGBP Secretariat, Stockholm. 64pp. Gobron, N., B. Pinty, M. Taberner, F. Mlin, M. M. Verstraete, and J.-L. Widlowski 2005. 'Monitoring the Photosynthetic Activity of Vegetation from Remote Sensing Data', Advances in Space Research, doi:10.1016/j.asr.2003.07.079. Gobron, N., B. Pinty, O. Aussedat, J. Chen, W. B. Cohen, R. Fensholt, V. Gond, K. F. Hummerich, T. Lavergne, F. Mlin, J. L. Privette, I. Sandholt, M. Taberner, D. P. Turner, M. M. Verstraete and J.-L. Widlowski 2006. 'Evaluation of FAPAR products for different canopy radiation transfer regimes: Methodology and results using JRC products derived from SeaWiFS against ground-based estimations', Journal of Geophysical Research, 111, D13110, DOI 10.1029/2005JD006511 GOFC-GOLD1999 To be completed Hay, S.I., Noor, A.M., Nelson, A. and Tatem, A.J. 2005, The Accuracy of Human Population Maps for Public Health Applications, Tropical Medicine and International Health, 10(10), 1073-1086. Herold, M., Woodcock, C., Di Gregorio, A., Mayaux, P., Belward A., Latham, J. & C. C. Schmullius 2006a.. A joint initiative for harmonization and validation of land cover datasets, IEEE Transactions on Geoscience and Remote Sensing, 44, 7, 1719-1727. Herold, M., Brady, M., Sambale, J. and C. Schmullius (.2006b. Report of the Land Cover Implementation Team Meeting, Jena, Germany, 2324th March 2006, GOFC-GOLD report series,  HYPERLINK "http://www.fao.org/gtos/gofc-gold/series.html" http://www.fao.org/gtos/gofc-gold/series.html. IGOL 2006. Agricultural Monitoring Meeting Convened For The Integrated Global Observations For Land (IGOL) Theme, Rome, Italy, 8-11 March 2006 IPCC (2001) IPCC Third Assessment Report: Climate Change 2001,  HYPERLINK "http://www.ipcc.ch/pub/reports.htm" http://www.ipcc.ch/pub/reports.htm IPCC 2003 Good Practice Guidance for Land Use, Land-Use Change and Forestry. Justice, C., Belward, A., Morisette, J., Lewis, P., Privette, J., & Baret, F. (2000). Developments in the validation of satellite sensor products for the study of land surface. International Journal of Remote Sensing, 21(17), 3383 3390. A. C. Janetos, and J. Townshend, 2005 Special meeting on observational priorities for conservation and biodiversity. Integrated Global Observations of the Land (IGOL) an IGOS-P Theme 3rd-4th November 2005, H. John Heinz III Center for Science, Economics, and the Environment, Washington DC Jansen, L.J.M. 2005 Harmonisation of land-use class sets to facilitate compatibility and comparability of data across space and time, 12th CEReS International Symposium, 13-14 December 2005, Chiba, Japan Justice et al., 1998 To be completed  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-03835?func=lateral-link&doc_number=005450901&line_number=0006" Korontzi S.,  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-03836?func=lateral-link&doc_number=005450901&line_number=0025" Roy D.P.,  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-03837?func=lateral-link&doc_number=005450901&line_number=0026" Justice C.O.,  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-03838?func=lateral-link&doc_number=005450901&line_number=0027" Ward D.E., 2004. ,  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-03834?func=lateral-link&doc_number=005450901&line_number=0007" Modeling and sensitivity analysis of fire emissions in southern Africa during SAFARI 2000. Remote Sensing of Environment92, no.3 (30 AUG 2004) p. 376-396 Lee B.S., Alexander M.E. Hawkes B.C., Lynham T.J., Stocks B.J., Englefield P., 2002. Information systems in support of wildland fire management decision making in Canada. Computers and Electronics in Agriculture 37 (2002) 185-98 Loveland, T. R., Reed, B. C., Brown, J. F., Ohlen, D. O., Zhu, Z., Yang, L., & Merchant, J. W. 2000. Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data. International Journal of Remote Sensing, 21(6/7), 1303 1330. Mayaux P., Strahler A., Eva H., Herold M., Shefali A., Naumov S., Dorado A., Di Bella C., Johansson D., Ordoyne C., Kopin I., Boschetti L. and Belward A. 2006. Validation of the Global Land Cover 2000 Map, IEEE Transactions on Geoscience and Remote Sensing, 44, 7, 1728-1739. Mollicone, D., Eva, H. D. and Achard, F., 2006 Human role in Russian wild fires, Nature 440:436-437,doi:10.1038/440436a, 23 March 2006 Plummer S, Arino O, Fierens F, Borstlap G, Chen J, Dedieu G, Ranera F, Simon M (2005) The Globcarbon Initiative: Multi-Sensor Estimation Of Global Biophysical Products For Global Terrestrial Carbon Studies, Proceedings of the MERIS (A)ATSR Workshop 2005, ESA SP-597 Prins, E. M., J. Schmetz, L. P. Flynn, D. W. Hillger, and J. M. Feltz. 2001. An overview of diurnal active fire monitoring using a suite of international geostationary satellites. In F. J. Ahern, J. G. Goldammer, and C. O. Justice, editors, Global and Regional Vegetation Monitoring from Space: Planning a Coordinated International Effort, pages 145170. SPB Academic Publishing, The Hague.  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Roy+D.P.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=9:0:next=html/records.html:bad=error/badsearch.html" Roy D.P.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Justice+C.O.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=9:0:next=html/records.html:bad=error/badsearch.html" Justice C.O.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Jin+Y.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=9:0:next=html/records.html:bad=error/badsearch.html" Jin Y.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Lewis+P.E.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=9:0:next=html/records.html:bad=error/badsearch.html" Lewis P.E. 2005. Prototyping a global algorithm for systematic fire-affected area mapping using MODIS time series data. Remote Sensing of Environment 97, no.2 (30 JUL 2005) p. 137-162  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Roy+D.P.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Roy D.P.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Landmann+T.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Landmann T.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Le+Roux+J.L.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Le Roux J.L.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Gumbo+K.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Gumbo K.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Makungwa+S.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Makungwa S.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Dunham+K.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Dunham K.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Du+Toit+R.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Du Toit R.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Mhwandagara+K.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Mhwandagara K.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Zacarias+A.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Zacarias A.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Tacheba+B.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Tacheba B.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Dube+O.P.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Dube O.P.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Pereira+J.M.C.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Pereira J.M.C.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Mushove+P.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Mushove P.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Morisette+J.T.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Morisette J.T.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Santhana+Vannan+S.K.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Santhana Vannan S.K.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Davies+D.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Davies D.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Frost+P.G.H.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Frost P.G.H.;  HYPERLINK "http://newfirstsearch.oclc.org/WebZ/FSQUERY?searchtype=hotauthors:format=BI:numrecs=10:dbname=GEOBASE::termh1=Justice+C.O.:indexh1=au%3D:sessionid=fsapp5-59173-f159tzoz-2u8611:entitypagenum=6:0:next=html/records.html:bad=error/badsearch.html" Justice C.O.2005. The Southern Africa Fire Network (SAFNet) regional burned-area product-validation protocol. International Journal of Remote Sensing 26, no.19 (10 OCT 2005) p. 4265-4292 Roy, D.P., Trigg, S.N., Bhima, R., Brockett, B., Dube, O., Frost, P., Govender, N., Landmann, T., Le Roux, J., Lepono, T., Macuacua, J., Mbow, C., Mhwandangara, K., Mosepele, B., Mutanga, O., Neo-Mahupeleng, G., Norman, M., Virgilo, S. The utility of satellite fire product accuracy information - perspectives and recommendations from the southern Africa fire network. IEEE Transactions on Geoscience and Remote Sensing, Land Product Validation Special Issue, Santoro, M., Askne, J., Smith, G., Fransson, J. E. S., 2002"Stem Volume Retrieval in Boreal Forests with ERS-1/2 Interferometry," Remote Sensing of Environment, vol. 81, 1, pp. 19-35,. Santoro, M., Eriksson, L., Askne, J., Schmullius, C 2006., "Assessment of stand-wise stem volume retrieval in boreal forest from JERS-1 L-band SAR backscatter," International Journal of Remote Sensing, vol. 27, 16, pp. 3425-3454,. Strahler, A., Boschetti, L., Foody, G. M., Fiedl, M. A., Hansen, M. C., Herold, M., Mayaux, P., Morisette, J. T., Stehman, S. V., Woodcock, C. 2006. Global Land Cover Validation: CEOS-WGCV/GOFC-GOLD report, JRC report series, URL: http://landval.gsfc.nasa.gov/pdf/GlobalLandCoverValidation.pdf Sutton, P. C., Cova, T.J., Elvidge, C.D., 2006, Mapping exurbia in the conterminous United States using nighttime satellite imagery. Geocarto International, v. 21, no. 2, 39-45. Tansey, K., Grgoire, J-M., Binaghi, E., Boschetti, L., Brivio, P.A., Ershov, D., Flasse, S., Fraser, R., Graetz, D., Maggi, M., Peduzzi, P., Pereira, J.M.C., Silva, J.M.N., Sousa, A. and Stroppiana, D. 2005, A Global Inventory of Burned Areas at 1km Resolution for the Year 2000 Derived from SPOT VEGETATION Data, Climatic Change, 67(2), 345-377 Terborgh and van Schaik 2002 To be completed Toenges-Schuller, N., Stein, O., Rohrer, F., Wahner, A., Richter, A., Burrows, J.P., Beirle, S., Wagner, T., Platt, U. and Elvidge, C.D., 2006. Global distribution pattern of anthropogenic nitrogen oxide emissions: Correlation analysis of satellite measurements and model calculations. Journal of Geophysical Research v. 111, D05312. Townshend J.R.and Brady, M.A. 2006 A Revised Strategy for GOFC-GOLD, GOFC-GOLD Report No. 24.  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-10525?func=lateral-link&doc_number=005527839&line_number=0006" Townshend J.R.G. and Justice C. O., 2002  HYPERLINK "http://researchport.umd.edu/V/N4X6SXQR7XKL9B6IF25NJQBX4VTPTN365IJNYKFC4QCSBCNK9P-10524?func=lateral-link&doc_number=005527839&line_number=0007" Towards operational monitoring of terrestrial systems by moderate-resolution remote sensing. Remote Sensing of Environment83, no.1-2 (2002) p. 351-359 UN FAO/GIEWS 2005,  HYPERLINK "http://www.fao.org/docrep/008/J5649e/J5649e00.htm" http://www.fao.org/docrep/008/J5649e/J5649e00.htm UNEP, Millenium Ecosystem Assessment, 2005,  HYPERLINK "http://www.maweb.org/en/index.aspx" http://www.maweb.org/en/index.aspx UNEP 2005 To be completed USGS-EDC, 2003 To be completed WCPA 2002 To be completed Wooster, M.J., Roberts, G., Perry, G. and Kaufman, Y.J. 2005 Retrieval of biomass combustion rates and totals from fire radiative power observations: Calibration relationships between biomass consumption and fire radiative energy release, Journal of Geophysical Research, , 110, D21111, doi: 10.1029/2005JD006318. Appendix 1 List of acronyms AcronymFull nameALOSAdvanced Land Observing Satellite ALOS-PALSARPhased Array type L-band Synthetic Aperture Radar of the Advanced Land Observing SatelliteAQUASTATFAO's Information System on Water and AgricultureATSRAlong Track Scanning RadiometerAVHRR GACAdvanced Very High Resolution Radiometer - Global Area CoverageCBDConvention on BiodiversityCBERSChina-Brazil Earth Resource SatelliteCEOSCommittee on Earth Observation Satellites CEOS LPV HYPERLINK "http://www.ceos.org/" Committee on Earth Observation Satellites, Land Product Validation working groupCEOS WGISSCommittee on Earth Observation Satellites, Working Group on Information Systems and Services CIESINThe Center for International Earth Science Information Network CNESCentre National d'Etudes spatialesCOOSCoastal Ocean Observing SystemsDEMDigital Elevation ModelDLRDeutsches Zentrums fr Luft- und RaumfahrtDMC SatellitesDisaster Monitoring Constellation DMSP OLSDefense Meteorological Satellite Program Operational Linescan System DSSDecision Support SystemEEA HYPERLINK "http://www.eea.europa.eu/" European Environment AgencyENVISAT-ASARAdvanced Synthetic Aperture Radar of ENVISATENVISAT MERISMEdium Resolution Imaging Spectrometer of EnvisatEO1 HYPERLINK "http://eo1.gsfc.nasa.gov/" Earth Observing Mission 1ERS HYPERLINK "http://en.wikipedia.org/wiki/Satellite" \o "Satellite" European Remote-Sensing satelliteESSP HYPERLINK "http://www.essp.org/" Earth System Science Partnership ETM+Enhanced Thematic MapperEUMETSAT European Organisation for the Exploitation of Meteorological SatellitesFEWSFamine Early Warning SystemFAOFood and Agriculture OrganizationFAO/GIEWSGlobal Information and Early Warning System at FAOfAPAR Fraction of Absorbed Photosynthetically Active RadiationFPAR HYPERLINK "http://ccrs.nrcan.gc.ca/optic/veg/veg2_e.php" Fraction of Photosynthetically Active Radiation FRP Fire Radiative PowerGBIFThe Global Biodiversity Information FacilityGCOS HYPERLINK "http://en.wikipedia.org/wiki/Global_Climate_Observing_System" \o "Global Climate Observing System" Global Climate Observing SystemGECAFS HYPERLINK "http://www.gecafs.org/" Global Environmental Change and Food SystemsGEOSS HYPERLINK "http://www.epa.gov/geoss/" Global Earth Observation System of SystemsGHGGreenhouse GasGLC2000global landcover classification for the year 2000GLOBSCAR ATSR Global Burned Forest MappingGLPGlobal Land ProjectGMES HYPERLINK "http://ec.europa.eu/space/gmes/index_en.htm" Global Monitoring for Environment and SecurityGMFS HYPERLINK "http://www.gmfs.info/" Global Monitoring for Food SecurityGOES HYPERLINK "http://www.oso.noaa.gov/goes/" Geostationary Operational Environmental SatellitesGOFC/GOLDGlobal Observation of Forest and Land Cover Dynamics GPPgross primary production GPSGlobal Positioning SystemGSDIGLOBAL SPATIAL DATA INFRASTRUCTUREGSEGMES Service ElementGTN-LGridded map of the areas of lakesGTOSGlobal Terrestrial Observing SystemIGBP HYPERLINK "http://www.igbp.net/" International Geosphere-Biosphere ProgrammeIGBP-STARTIGBP System for Analysis, Research, and TrainingIGOLINTEGRATED GLOBAL OBSERVATION OF LANDIGOSThe Integrated Global Observing StrategyIHDP HYPERLINK "http://www.ihdp.uni-bonn.de/html/publications/publications.html" International Human Dimensions ProgrammeILTER HYPERLINK "http://www.ilternet.edu/" The International Long Term Ecological Research NetworkInSARInterferometric Synthetic Aperture RadarIPCC HYPERLINK "http://www.ipcc.ch/" Intergovernmental Panel on Climate ChangeIRS HYPERLINK "http://www.fas.org/spp/guide/india/earth/irs.htm" Indian Remote Sensing SatelliteISRO HYPERLINK "http://www.isro.org/" Indian Space Research OrganisationIT IS HYPERLINK "http://www.itis.gov/" Integrated Taxonomic Information SystemIUSS HYPERLINK "http://www.iuss.org/" International Union of Soil SciencesJAXA HYPERLINK "http://www.jaxa.jp/index_e.html" Japan Aerospace Exploration Agency JRCThe Joint Research Centre of the European ComissionLAILeaf Area IndexLCCSLand Cover Classification SystemLDCM HYPERLINK "http://ldcm.nasa.gov/" LANDSAT DATA CONTINUITY MISSIONLIDARLIght Detection and RangingLTAPLong Term Acquisition Plan for Landsat 7LUCCLand-Use and Land-Cover ChangeIUCN HYPERLINK "http://www.iucn.org/" The World Conservation UnionLULUCF land use, land-use change and forestry MAMillenium Ecosystem AssessmentMETOPpolar orbiting meteorological satellitesMISRMulti-angle Imaging SpectroRadiometerMODISModerate-resolution Imaging SpectroradiometerMRLC2001Multi-Resolution Land Characteristics 2001 NASANational Aeronautics and Space AdministrationNEONThe National Ecological Observatory NetworkNOAAThe National Oceanic and Atmospheric AdministrationNPOESS The National Polar-orbiting Operational Environmental Satellite SystemNPOESS-VIIRSThe Visible Infrared Imager Radiometer Suite of NPOESSOGCThe Open Geospatial ConsortiumPRISMthe Panchromatic Remote-sensing Instrument for Stereo Mapping of JAXASAR HYPERLINK "http://en.wikipedia.org/wiki/Synthetic_aperture_radar" \o "Synthetic aperture radar" Synthetic Aperture RadarSOTERThe Soil and Terrain Digital DatabaseSPOT-HRVSatellite Pour l'Observation de la Terre - High Resolution VisibleSRTMShuttle Radar Topographic MissionSWIRShort wavelength infraredTEMSthe Terrestrial Ecosystem Monitoring Sites databaseTIN HYPERLINK "http://en.wikipedia.org/wiki/Triangulated_irregular_network" \o "Triangulated irregular network" Triangulated irregular networkTIRThermal InfraredTMThematic MapperUNCBDUnited Nations Convention on Biological DiversityUNCEDUnited Nations Conference on Environment and DevelopmentUNDPUnited Nations Development ProgrammeUNECEUnited Nations Economic Commission for EuropeUNEPUnited Nations Environment Program(me)UNESCOUnited Nations Educational, Scientific and Cultural OrganizationUNFCCC United Nations Framework Convention on Climate ChangeURBEXUrban Expansion program of ESAUSAIDUnited States Agency for International DevelopmentUSDAUnited States Department of AgricultureUSGS-EDCThe Earth Resources Observation Systems (EROS) Data Center (EDC) of the US Geological Survey's WCPAWorld Commission on Protected Areas (IUCN)WCRP World Climate Research ProgrammeWHOWorld Health OrganizationWHYCOSWorld Hydrological Cycle Observing SystemWMO World Meteorological OrganizationWSSDWorld Summit on Sustainable Development (UN) Appendix 2 Participants in the IGOL Theme Members of the IGOL Team Co-Chair: John TownshendChair, GOFC-GOLD, GTOSCo-Chair: John LathamGTOS Programme DirectorDennis Ojima IGBPAlan Belward GCOSChristiana SchmulliusGOFC/GOLDJeff Tschirley FAOOlivier ArinoESAChris JusticeGOFC/GOLDTony Janetos Heinz CenterAke Rosenqvist JAXAAshbindu SinghUNEPRoberta Balstad MillerCIESENJay Feuquay USGSJiyuan LiuCAS Attendees 1st IGOL Theme Team Meeting Olivier ArinoESAChris ElvidgeNOAAWang HongNRSCCChris JusticeUniversity of MarylandJiyuan LiuIGSNRRRoberta Balstad MillarCIESIN, Columbia UniversityDoug MuchoneyUSGSDennis OjimaNREL - Colorado State UniversityFrancesco PalazzoESAChristina SchmulliusFriedrich-Schiller-Universitt (FSU)Ashbindu SinghUNEPErgin AtamanFAO-SDRNJuan FajardoFAO-AGPSHubert GeorgeFAO-AGLLKailash GovilFAO-FORMJohn LathamFAO-SDRNFreddy NachtergaeleFAO-AGLLMohamed SaketFAO-FORMReuben SessaFAO-SDRNJeff TschirleyFAO-SDRN Attendees 2nd IGOL Theme Team Meeting, Reston Virginia USA 20-22 July 2005 Co-Chair: John TownshendGOFC-GOLDCo-Chair:John LathamGTOSOlivier ArinoESARoberta BalstadCIESENRichard ConantColorado StateChris ElvidgeNOAAJay FeuquayUSGSDriss El HadaniCRTSAngas HopkinsFAOChris JusticeGOFC/GOLDTom LovelandUSGSMartha MaidenNASADoug MuchoneyUSGSDennis OjimaIGBPFrancesco PalazzoESAChristina SchmulliusGOFC/GOLDAsbindu SinghUNEPHirokazu YamamotoJAXA Attendees 3rd IGOL Theme Team Meeting, Beijing, China, 27 February 1 March 2006 Co-chair: John TownshendGOFC-GOLD, GTOSCo-chair: John LathamGTOSOlivier ArinoESACao ChunxiangIRSA-CASRichard ConantColorado StateChris ElvidgeNOAAGuo JianningCRSDAChris JusticeGOFC/GOLDDu KepingBNUMengxue LiNRSCCAi LikunCASWang LiminCAASJiyuan LiuCASShao LiqinNRSCCTom LovelandUSGSDennis OjimaIGBPFrancesco PalazzoESAChristina SchmulliusGOFC/GOLDAsbindu SinghUNEPDeng Xiangzheng NRRCSHirokazu YamamotoJAXAZhao YongchaoIRSA-CASZhongli ZhuPeking UniversityChen ZhongxinCAAS Attendees: Chinese Special IGOL Meeting: To Obtain Chinese input on the development of the IGOL Theme Report, Beijing, 3rd September 2005. NameAffiliationMingkui CaoInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesJin ChenCollege of Resources Science & Technology, Beijing Normal UniversityZhongxin ChenInstitute of Agricultural Resources & Regional Planning, Chinese Academy of Agricultural SciencesJianning GuoChinese center for Earth Resource Satellite Data ApplicationGuifei JingNational Remote Sensing Center of ChinaMengxue LiNational Remote Sensing Center of ChinaZengyuan LiInstitute of Forest resources Information Technique, Chinese Academy of ForestryJiyuan LiuInstitute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesJohnR.G. TownshendDepartment of Geography, Institute of Advanced Computing Studies, University of MarylandBingfang WuInstitute of Remote Sensing Applications, Chinese Academy of SciencesChuanqing WuInformation Center, State Environment Protection AdministrationSongcai YouData Center for Resources and Environmental Sciences, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of SciencesJiashen ZhangNational Satellite Meteorological Center, CMADafang ZhuangData Center for Resources and Environmental Sciences, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences Participants: Agricultural Monitoring Meeting Convened For The Integrated Global Observations For Land (IGOL) Theme Rome, Italy 8-11 March 2006 Co-chairs: Chris JusticeUniv. of MDCo-chair: Henri JosserandFAO-ESCGAssaf AnyambaNASADominic BallayanFAO, ESSASergey BartalevIKIInbal Becker-ReshefUniv. of MDPatrice BicheronCNESLieven ByderkerkeGMFSElisabetta CarfagnaUniv. of BolognaZhongxin ChenCAASRichard ConantCO State Univ.Gerard DedieuCESBIOPierre DefournyUniv. LouvainPaola De SalvoWFPBradley DoornUSDA/FASHugh EvaGEMGary EilertsUSAIDThomas GabriellFAO-FSAUHubert GeorgeFAO-AGLLFrancesco HoleczSarmapJiyuan LiuCASKennedy MasamvuFANRSADCMichel MassartMARS-FOODEvaristo Eduardo de MirandaEMBRAPADoug MuchoneyUSGS/GEOSSThierry NegreMARS-FOODFrancesco PalazzoESAPrabhu PingaliFAO-ESADCurt ReynoldsUSDA/FASGabriel SenayEROSReuben SessaFAOSRDNEdwin SheffnerNASAMark SmuldersFAO-ESDGJim VerdinUSGS Special meeting on observational priorities for conservation and biodiversity 3rd-4th November 2005, Washington DC, USA John Townshend University of MarylandRich Conant Colorado State University Lera Miles UNEP WCMC Robert Hoft CBD Secretariat Fred Stolle WRI Doug Muchoney USGS Roger Sayre USGS Dennis Grossman NatureServe Roberta Balstad Columbia University Karl Didier WCS John Musinsky CI Mark Steininger CI William Settle FAO Benjamin White University of MarylandMichael Case WWF Bob Scholes CSIR Angas Hopkins FAO Tony Janetos Heinz Center Woody Turner NASA   http://www.ipcc.ch/pub/un/syreng/spm.pdf  http://www.fao.org/docrep/008/J5649e/J5649e00.htm  This section is a condensed version of the full IGOL report on Agriculture (IGOL 2006)  To be inserted  To be inserted     PAGE  PAGE 5 INTEGRATED GLOBAL OBSERVATION OF LAND -Draft  ACD\]hijlstνꞚqhqNqhq2jh| h| >*B*UmHnHphuh| mHnHuhKh| 0JmHnHu$jhKh| 0JUmHnHujhj'mUhj'mh}rh"I5CJ(aJ(mHnHuhh385CJ(aJ( jhh385CJ(UaJ(hh385hh385CJ,aJ,jh38Uh38jh38UmHnHuhS|_  01ABCjklG  $  $ $`a$gd}r$a$gd8$a$gdgdC< $`a$gd8$a$gdS|_--1.2.4.6.      % & ' A B C D E F G H I e f g h k l w x y ¹««¹«f«2jh| h| >*B*UmHnHphu j,h| UmHnHu2jh| h| >*B*UmHnHphuhKh| 0JmHnHuh| mHnHu$jhKh| 0JUmHnHu j2h| UmHnHujh| UmHnHuh| mHnHu'       9 ¹««Հ¹«f«2jh| h| >*B*UmHnHphu j h| UmHnHu2jh| h| >*B*UmHnHphuhKh| 0JmHnHuh| mHnHu$jhKh| 0JUmHnHuh| mHnHujh| UmHnHu j&h| UmHnHu$9 : ; < = > ? @ A ] ^ _ ` c d ¹««Հ¹«f«2jh| h| >*B*UmHnHphu jh| UmHnHu2jh| h| >*B*UmHnHphuhKh| 0JmHnHuh| mHnHu$jhKh| 0JUmHnHuh| mHnHujh| UmHnHu jh| UmHnHu$ ?  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