Leaf area index (LAI) corresponds to one half the total green leaf area per unit horizontal ground surface area. It is one of the essential climate variables identified by the Global Climate Observing System (GCOS) and has been applied in a range of biosphere-atmosphere, biogeochemical, and ecological models as a primary biophysical and ecological parameter. This talk consists of three parts. The first part presents the LAI ground measurement methods. In the field, LAI can be estimated directly or indirectly: the direct methods measure LAI through litterfall or destructive sampling; the indirect methods estimate LAI via relationship with other more easily measurable parameters, such as canopy transmittance. The second part is about the LAI estimation from optical remote sensing technique. Current methods for estimating LAI are classified into three categories through: (1) the empirical relationship between LAI and vegetation indices; (2) inversion of canopy reflectance models; and (3) a hybrid inversion method. Following this, several major global LAI products and their spatial and temporal properties will be introduced. The last part of the presentation focuses on how to validate remote sensing LAI products. There are several validation schemes that develop uncertainty information about the global LAI products, such as the direct comparison method, the bridging method that makes comparison with upscaled high resolution products, and the cross-validation method.

Research is increasingly international, transdisciplinary, and data-enabled, which requires scrupulous data stewardship, full and open access to data. New expectations on researchers from governments and funders to share data fully, openly, and in a timely manner present significant challenges and opportunities to improve quality, efficiency and accountability to society. Researchers should be able to archive and disseminate data, and civil society to scrutinize datasets. Trustworthiness of data services must be verifiable. In addition, the need to integrate large and complex datasets across disciplines and domains with variable levels of maturity calls for better coordination to achieve interoperability and sustainability. WDS promotes long-term stewardship of, and universal and equitable access to, quality-assured scientific data. It coordinates and supports trusted scientific data services for the provision, use, and preservation of datasets to facilitate scientific research. It certifies its members—holders and providers of data—using internationally recognized standards, providing the building blocks of a searchable common infrastructure from which a data system that is both interoperable and distributed can be formed. This presentation will describe more specifically activities developed by its Scientific Committee to:（1）Improve and stimulate basic level Certification for Scientific Data Services, in particular through collaboration with the Data Seal of Approval. （2）Identify and define best practices for Publishing Data and to test implementation by involving the core stakeholders i.e. researchers, institutions, data centres, scholarly publishers, and funders.（3）Establish an open WDS Metadata Catalogue, Knowledge Network, and Global Registry of Trusted Data Services.

Trends and variability in precipitable water (PW) are an important component of climate change assessments for the Tibetan Plateau (TP). PW products from Moderate Resolution Imaging Spectroradiometer (MODIS) are able to provide good spatial coverage of PW over the TP. The trends and variations in TP PW from 2000 to 2010 are analysed using three different methods (including linear fit, non-parametric and non-linear). The trend patterns derived from MODIS and ECMWF are very similar except for the magnitude. Increasing trends are found in the eastern and western TP; with decreasing trends in the middle. To detect the decadal trend in PW over the TP, Bayesian inference theory is used to construct long-term and spatially continuous PW data for the TP based on the station and MODIS observations. The prior information on the monthly-mean PW from MODIS and the 63 stations over the TP for 2000–06 is used to get the posterior probability knowledge that is utilized to build a Bayesian estimation model. This model is then operated to estimate continuous monthly-mean PW for 1970–2011 and its performance is evaluated using the monthly MODIS PW anomalies (2007–11) and annual GPS PW anomalies (1995–2011) to demonstrate that the model estimation can reproduce the PW variability over the TP in both space and time. Annual PW series show a significant increasing trend of 0.19mmdecade21 for the TP during the 42 years. The most significant PW increase of 0.47mmdecade21 occurs for 1986–99 and an insignificant decrease occurs for 2000–11. From the comparison of the PW data from JRA-55, ERA-40, ERA-Interim, MERRA, NCEP-2, and ISCCP, it is found that none of them are able to show the actual long-term trends and variability in PW for the TP as the Bayesian estimation.

This presentation consists of two part: first part will brief the collaboration activities in the Mongolia Plateau, including the establishment of International Joint Research Center on Environment and Sustainable Development of Mongolian Plateau and it major collaboration progress. Second part will introduce the research progress on Socio-economic survey based impact assessment of herders’ livelihood in the grasslands of Mongolia Plateau. The second part will (1) brief the background for the survey, which is mainly due to severe eco-environment problems in the regions, eg, soil erosion, land degradation, limited water availability, extraction of mineral resources, overgrazing, and ecosystem deterioration, trade-offs between grassland ecosystem protection and economic development in the marginal lands, and increasing meat and dairy consumption and demand for land productivity. Actions taken to mitigate those problems are mainly ecosystem conservation programs, which affect herders’ living, this study aims to identify and assess these impact using socio-economic methods and approaches. (2) Designing and implementing the surveys (questionnaire survey, institutional survey): which include purposive/homogeneous sampling, determination of survey sites, sampling methods and conducting survey. (3) livelihood indicators derived from surveys and assessment: land resources, grazing patterns, income structure, consumption patterns, cost for production and grazing, and dependence on local ecosystem services. (4) Evaluation of the survey approaches applied.

His main research interests are remote sensing in Cryosphere and Data Sharing. He has developed two important databases: Chinese glacier inventory and glacial lake inventory in the Hindu-Kush Himalaya Region. The former has been included by GLIMS database and is also part of Randolph Glacier Inventory(RGI), which is cited by IPCC AR5. He is the key member of “Cold and Arid Regions Science Data Center at Lanzhou” which is the sub center of the earth system science data sharing platform and the regular member of World Data System. Cryosphere data(include snow, glacier and permafrost dataset), desert and desertification data, environmental and ecosystem data in inland basin can be free accessed from this data center.

Collecting Landsat TM/ETM+ images of the whole region in 1990~2000, with the support of the object-oriented platform Ecognition, this research used decision tree and nearest neighbors classifier to extract the information about the land cover pattern of Northeast Asia, as well as its dynamic change. Based on the above datasets, choosing the involved area of China-Russia treaty, the coastwise transect area of Heilongjiang river basin, transect area of the frozen soils and Wusuli river basin of cross-boundary region between China and Russia as priority areas in this study, we discussed characteristics of land cover patterns and spatial differences of changes among different countries. Besides, taking related research about land suitable assessment as reference, we selected the most sensitive, most accurate and most abundant information indices as the assessment factor, according to the principle of comprehensiveness, leading character, rank character and regional rules. Then, we combined AHP (analytic hierarchy process) and expert evaluation method to illustrate the weight of different evaluation factors. After that, we did comprehensive suitable evaluation of reserved cultivated land in Northeast Asia. Finally, this study determined the spatial patterns and quantity of general suitable zone, highly suitable zone, fairly unsuitable zone and unsuitable zone. The result of this study can provide scientific support for sound land use policy makings.

Ecosystem, climate and livelihood are closely related,this presentation will introduce five main topics ：（1）Ecosystem, its Services and Management.（2）Evolution and Ecological Civilization.(3)Ecosystems and Sustainable Development. (4)Climate Change and ecosystems. (5)Ecosystem, Climate and Livelihood, towards a conceptual framework. Finally we will introduce Agenda 2030/SDGs,aimed at an array of issues that included slashing poverty, hunger, disease, gender inequality, and access to water and sanitation.It has 17 SD Goals/169 Targets,and is a conceptual framework of the Ecosystem, Climate and Livelihood, which to end poverty, fight inequality and injustice, and tackle climate change.

Introduce the first 30m resolution global land cover map – Finer Resolution Observation and Monitoring – Global Land Cover (FROM-GLC), its mapping philosophy and procedure based on Landsat and other Earth observation satellite data. Then introduce the follow up data products in the FROM family – FROM-Global Cropland, FROM – Water, FROM – Wetland, Afirca Land Cover, Chilean Land Cover，Major Cities, Regional Settlement. Some of the scientific applications based on these data products will also be presented. Lastly, we propose an international cooperation framework in support of national land cover mapping and change monitoring based on the procedural framework of FROM – GLC in collaboration with national experts from specific countries.

We will introduces the cyberinfrastructure, database and its applications in Qinghai Lake region. We have been building a cyberinfrastructure to support the protection and research in this area since 2006. We introduce the e-Science application practices supporting research on species identification, spatial distribution patterns analysis, and avian influenza virus cross-species infection by presenting their methods, implementation, and results. This is the first attempt in China to construct cyberinfrastructure to support protection and research on wild birds in plateau area. The e-Science application practices are also the first one applied in the national nature reserve in China.

The lecture summarizes the key scientific issues for the sustainable development in Silk Road Economic Belt and analyzes the distribution pattern of resources, ecological environment, society and economy, and holds that it is conducive to Silk Road countries to carry out Omni-directional and multi-level economic cooperation. Then the lecture discusses main modes and paths of sustainable economic development for Silk Road Economic Belt: Resource cooperation mode，Ecological civilization mode, Regional economic integration mode, Transportation economic belt mode, International tourism economic zone mode. Finally, the article puts forward several action projects.

The data set is average temperature interpolation data for China, Russian, Mongolia from 1980 to 2010, is based on the NCDC gsod global temperature data set http://www.ncdc.noaa.gov, and through spatial interpolation. The spatial resolution of the data for 1KM, a single band coverage data format, respectively. The temperature data during 1980-2010 year.

This data set is the average annual precipitation interpolation data of China, Russia and Mongolia 1980-2010 years, according to the NCDC gsod global precipitation data set http://www.ncdc.noaa.gov, and the spatial interpolation.

This data set in 1981 -1999 NDVI-PathFinder and 2000 -2012 MODIS data set based on vegetation index data set, two pixel model and vegetation index based on the data were retrieved from the data of Mongolia Plateau vegetation coverage in 1981 -2012 set. This data set provides the surface vegetation cover of 1981-2012 year in Mongolia plateau from 4-10 to 1981-2012. The overall accuracy of the data set is 75%.

This data set using the sinusoidal projection and 16 days of synthetic data, the spatial resolution of 250m, improve the NDVI observation of low saturation point, calculated on the basis of BRDF data, and a variety of mask processing (water, cloud, aerosol, cloud and shadow), helps to carry out the data analysis of the long-term dynamic assimilation and vegetation, and also can be used for detecting vegetation status, biological chemistry, water cycle and global and regional climate research.

This data set using the sinusoidal projection and 16 days of synthetic data, extraction of EVI, but also the full consideration of the atmospheric correction problems, the calculation is based on BRDF correction data, and a variety of mask processing. Compared to the NDVI EVI time series seasonal time series is more obvious, can better reflect the seasonal variation characteristics of high vegetation covered area, and there is little drop phenomenon, time series curve was smooth.

The data set provides the Mongolia plateau SPOT 10 days synthetic NDVI raster data with a resolution of 1000 meters, the coordinates of type WGS-84, projection using Albers projection.

The original data set is the MODIS Collection 5 data set produced by the Boston University. The data set, projection system and data format conversion of MCD12Q1 products using the MODIS reprojection tool MRT, and land_cover_type_1 data extraction layer, data classification system transformation, using resampling techniques covering raster data resolution are obtained for 500m resolution of the 2009 Mongolia plateau land. The overall accuracy of the data is about 75%, and the accuracy of different land cover types is different.

The original data set is the MODIS Collection 5 data set produced by the Boston University. The data set, projection system and data format conversion of MCD12Q1 products using the MODIS reprojection tool MRT, and land_cover_type_1 data extraction layer, data classification system transformation, using resampling techniques covering raster data resolution are obtained for 500m resolution of the 2005 Mongolia plateau land. The overall accuracy of the data is about 75%, and the accuracy of different land cover types is different.

The original data set is the MODIS Collection 5 data set produced by the Boston University. The data set, projection system and data format conversion of MCD12Q1 products using the MODIS reprojection tool MRT, and land_cover_type_1 data extraction layer, data classification system transformation, using resampling techniques covering raster data resolution are obtained for 500m resolution of the 2001 Mongolia plateau land. The overall accuracy of the data is about 75%, and the accuracy of different land cover types is different.

Including Nepal based National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Including Thailand based National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Including Kazakhstan based National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Including Uzbekistan based National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Including Turkmenistan based National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Including Tajikistan based National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Including India based National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Including Bangladesh based National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Including Bhutan based National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Including Burma based National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Including the basic situation of the Lao National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Including the basic national geographic data of Vietnam. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Including Kampuchea based National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Including Philippines based National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Including Pakistan based National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Geographic data containing the Russian national conditions. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Use Poyang Lake area from 2009 to 2012 spring, summer, autumn and winter seasons continuously observed suspended solids concentration and the same period of MODIS Image product mod09a1 multi band linear regression analysis to obtain the inversion model of four seasons; based on these models, acquired Poyang Lake of suspended solids concentration from 2009 to 2012 by season . Furthermore, analyzes the Poyang Lake sexton concentrations of space-time distribution pattern and variation characteristics.

The data set use 20 county (city, district) in the Dongting Lake area as the study area, extract lake polder 2013 space distribution, and generate Lake polder spatial variation data in three different periods (1949-1998 years, from 1998 to 2008, 2008-2013)

This set of data made field spectral information collection and analysis, construct the spectral index, combining with the measured data of chlorophyll a concentration, using the least squares regression analysis method to get the sensitive band interval; the semi empirical method, the experience stage has been in Poyang Lake 2009-2012 chlorophyll estimation model for a concentration using MODIS data obtained in Poyang Lake from 2009 to January 2012, April, July and October the distributions of chlorophyll a concentration data.

Including Kyrgyzstan based National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.

Including Mongolia based National Geographic Data. It includes three categories: basic national conditions, natural resources and political and economic data. Detailed classification: geographical location, administrative division, topography, soil, climate, rivers, lakes and the environment, land resources, water resources, forest resources, animal resources, plant resources, energy resources, mineral resources, non-metallic mineral resources, tourism resources, language, nationality, religion, festivals, political diplomacy, economy, science and technology, education, sports ,hospital bed density, etc.