Photosynthetic effective radiation absorption coefficient photosynthetically active radiation component is an important biophysical parameter. It is an important land characteristic parameter of ecosystem function model, crop growth model, net primary productivity model, atmosphere model, biogeochemical model and ecological model, and is an ideal parameter for estimating vegetation biomass. The data set contains the data of photosynthetically active radiation absorption coefficient in Qinghai Tibet Plateau, with spatial resolution of 500m, temporal resolution of 8D, and time coverage of 2000, 2005, 2010 and 2015. The data source is MODIS Lai / FPAR product data mod15a2h (C6) on NASA website. The data are of great significance to the analysis of vegetation ecological environment in the Qinghai Tibet Plateau.
This data set comprises the oxygen isotope and geochemical data of two deep-drilled ice cores drilled in the Puruogangri ice sheet (33°55'N, 89°05'E, altitude: 6070 meters) in the central Tibetan Plateau in 2000. The ice core depths are 118.4 and 214.7 meters, respectively. Source of the data: National Centers for Environmental Information (http://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets/ice-core) . The data set contains 6 tables, which are the average values of 1 oxygen isotope per meter of the Puruogangri ice core, the 10-year average data of 1 oxygen isotope of the Puruogangri ice core, the average values of 2 oxygen isotope and the soluble aerosol concentrations per meter of the Puruogangri ice core, the 5-year average data of 2 oxygen isotope and aerosol concentrations of Puruogangri ice core, 10-year average data of 2 oxygen isotope and aerosol concentrations of the Puruogangri ice core, and the 100-year average values of 2 oxygen isotopic and aerosol concentrations of the Puruogangri ice core. The information on the fields is as follows: Table 1: the average values of 1 oxygen isotope per meter of the Puruogangri ice core Field: Field Name [Dimensions (Unit of Measure)] Field 1: Depth [m] Field 2: δ18° [‰] Table 2: the 10-year average data of 1 oxygen isotope of the Puruogangri ice core Field: Field Name [Dimensions (Unit of Measure)] Field 1: Start time [Dimensionless] Field 2: End time [Dimensionless] Field 3: δ18° [‰] Table 3: the average values of 2 oxygen isotope and soluble aerosol concentration per meter of the Puruogangri ice core Field: Field Name [Dimensions (Unit of Measure)] Field 1: Depth [m] Field 2: Dust (diameter 0.63-20 um) [particles/mL] Field 3: 18° [‰] Field 4: F- [ppb] Field 5: Cl- [ppb] Field 6: SO42- [ppb] Field 7: NO3- [ppb] Field 8: Na+ [ppb] Field 9: NH4+ [ppb] Field 10: K+ [ppb] Field 11: Mg2+ [ppb] Field 12: Ca2+ [ppb] Table 4: the 5-year average data of 2 oxygen isotope and aerosol concentration of the Puruogangri ice core Field: Field Name [Dimensions (Unit of Measure)] Field 1: Start time [Dimensionless] Field 2: End time [Dimensionless] Field 3: δ18° [‰] Field 4: Accumulation [cm/yr] Field 5: Dust (diameter 0.63-20 um) [particles/mL] Field 6: F- [ppb] Field 7: Cl- [ppb] Field 8: SO42- [ppb] Field 9: NO3- [ppb] Field 10: Na+ [ppb] Field 11: NH4+ [ppb] Field 12: K+ [ppb] Field 13: Mg2+ [ppb] Field 14: Ca2+ [ppb] Table 5: the 10-year average data of 2 oxygen isotope and aerosol concentrations of the Puruogangri ice core Field: Field Name [Dimensions (Unit of Measure)] Field 1: Start time [Dimensionless] Field 2: End time [Dimensionless] Field 3: δ18° [‰] Field 4: Dust (diameter 0.63-20 um) [particles/mL] Field 5: F- [ppb] Field 6: Cl- [ppb] Field 7: SO42- [ppb] Field 8: NO3- [ppb] Field 9: Na+ [ppb] Field 10: NH4+ [ppb] Field 11: K+ [ppb] Field 12: Mg2+ [ppb] Field 13: Ca2+ [ppb] Table 6: the 100-year average values of 2 oxygen isotopic and aerosol concentrations of the Puruogangri ice core Field: Field Name [Dimensions (Unit of Measure)] Field 1: The last year of the interval [Dimensionless] Field 2: δ18° [‰] Field 3: Dust (diameter 0.63-20 um) [particles/mL] Field 4: F- [ppb] Field 5: Cl- [ppb] Field 6: SO42- [ppb] Field 7: NO3- [ppb] Field 8: Na+ [ppb] Field 9: NH4+ [ppb] Field 10: K+ [ppb] Field 11: Mg2+ [ppb] Field 12: Ca2+ [ppb]
The data include the night light data of Tibetan Plateau with a spatial resolution of 1km*1km, a temporal resolution of 5 years and a time coverage of 2000, 2005 and 2010.The data came from Version 4 dmsp-ols products. DMSP/OLS sensors took a unique approach to collect radiation signals generated by night lights and firelight.DMSP/OLS sensors, working at night, can detect low-intensity lights emitted by urban lights, even small-scale residential areas and traffic flows, and distinguish them from dark rural backgrounds.Therefore, DMSP/OLS nighttime light images can be used as a representation of human activities and become a good data source for human activity monitoring and research.
Data set of key elements of desertification in Central and Western Asia (Amu river Basin), includes three parts: lakes distribution data, vegetation coverage data and NPP data. The spatial resolution of lakes distribution data and vegetation coverage data is 30 m, and includes three periods: 1990, 2000 and 2010. It is based on the interpretation and calculation of TM/ETM data; The spatial resolution of NPP data is 500 m, and includes two periods: 2000 and 2010 (Since no MODIS data in 1990, there is no 1990 data). It is based on the calculation of MODIS data. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.
Data Set of Key Elements of Desertification in Typical Watershed of Central and Western Asia includes four parts: distribution and change of agricultural land of Amu River Basin, distribution and change of grassland of Amu River Basin, distribution and change of shrub land of Amu River Basin, distribution and change of forests of Amu River Basin. the spatial resolution of data is 30 m. All the data is based on Landsat TM/ETM image data in 1990, 2000 and 2010. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.
This data set is based on the evaluation of existing land cover data and the evidence theory，including a 1:100,000 land use map for the year 20 2000、a 1:1,000,000 vegetation map、a 1:1,000,000 swamp-wetland map, a glacier map and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001) were merged，Finally, the decision is made based on the principle of maximum trust, and a new 1KM land cover data of China in 2000 with IGBP classification system is produced. The new land cover data not only maintain the overall accuracy of China's land use data, but also supplement the information of vegetation types and vegetation seasons in China's vegetation map, update China's wetland map, add the latest information of China's glacier map, and make the classification system more general.
The past frozen soil map of the Tibetan Plateau was based on a small number of temperature station observations and used a classification system based on continuity. This data set used the geographically weighted regression model (GWR) to synthesize MODIS surface temperature, leaf area index, snow cover ratio and multimodel soil moisture forecast products of the National Meteorological Information Center through spatiotemporal reconstruction. In addition, precipitation observations of more than 40 meteorological stations, the precipitation products of FY2 satellite observations and the multiyear average temperature observation data of 152 meteorological stations from 2000 to 2010 were integrated to simulate the average temperature data of the Tibetan Plateau, and the permafrost thermal condition classification system was used to classify permafrost into several types: Very cold, Cold, Cool, Warm, Very warm, and Likely thawing. The map shows that, after deducting lakes and glaciers, the total area of permafrost on the Tibetan Plateau is approximately 1,071,900 square kilometers. Verification shows that this map has higher accuracy. It can provide support for future planning and design of frozen soil projects and environmental management.
Vegetation functional type (PFT) is a combination of large plant species according to the ecosystem function and resource utilization mode of plant species. Each planting functional type shares similar plant attributes, which simplifies the diversity of plant species into the diversity of plant function and structure.The concept of vegetation-functional has been advocated by ecologists especially ecosystem modelers.The basic assumption is that globally important ecosystem dynamics can be expressed and simulated through limited vegetative functional types.At present, vegetation-functional model has been widely used in biogeographic model, biogeochemical model, land surface process model and global dynamic vegetation model. For example, the land surface process model of the national center for atmospheric research (NCAR) in the United States has changed the original land cover information into the applied vegetation-functional map (Bonan et al., 2002).Functional vegetation has been used in the dynamic global vegetation model (DGVM) to predict the changes of ecosystem structure and function under the global change scenario. 1. Functional classification system of vegetation 1 Needleleaf evergreen tree, temperate 2 Needleleaf evergreen tree, boreal 3 Needleleaf deciduous tree 4 Broadleaf evergreen tree, tropical 5 Broadleaf evergreen tree, temperate 6 Broadleaf deciduous tree, tropical 7 Broadleaf deciduous tree, temperate 8 Broadleaf deciduous tree, boreal 9 Broadleaf evergreen shrub, temperate 10 Broadleaf deciduous shrub, temperate 11 Broadleaf deciduous shrub, boreal 12 C3 grass, arctic 13 C3 grass 14 C4 grass 15 Crop 16 Permanent wetlands 17 Urban and built-up lands 18 Snow and ice 19 Barren or sparsely vegetated lands 20 Bodies of water 2. Drawing method China's 1km vegetation function map is based on the climate rules of land cover and vegetation function conversion proposed by Bonan et al. (Bonan et al., 2002).Ran et al., 2012).MICLCover land cover map is a blend of 1:100000 data of land use in China in 2000, the Chinese atlas (1:10 00000) the type of vegetation, China 1:100000 glacier map, China 1:10 00000 marshes and MODIS land cover 2001 products (MOD12Q1) released the latest land cover data, using IGBP land cover classification system.The evaluation shows that it may be the most accurate land cover map on the scale of 1km in China.Climate data is China's atmospheric driven data with spatial resolution of 0.1 and temporal resolution of 3 hours from 1981 to 2008 developed by he jie et al. (2010).The data incorporates Princeton land-surface model driven data (Sheffield et al., 2006), gewex-srb radiation data (Pinker et al., 2003), TRMM 3B42 and APHRODITE precipitation data, and observations from 740 meteorological stations and stations under the China meteorological administration.According to the evaluation results of RanYouhua et al. (2010), GLC2000 has a relatively high accuracy in the current global land cover data set, and there is no mixed forest in its classification system. Therefore, the mixed forest in the MICLCover land cover diagram USES GLC2000 (Bartholome and Belward, 2005).The information in xu wenting et al., 2005) was replaced.The data can be used in land surface process model and other related researches.
The data include NDVI data of Tibetan Plateau region, with spatial resolution 1000m, time resolution 16d, and time coverage in 2000, 2005, 2010 and 2015.The data source is MOD13A2(C6).NDVI is a kind of vegetation index formed by combining visible light and near-infrared bands of satellites according to the spectral characteristics of vegetation.NDVI is a simple, effective and empirical measure of surface vegetation.The data is of great significance for analyzing the ecological environment of Tibetan Plateau.
The dataset includes vector map of the lakes larger than 1k㎡ on Tibetan Plateau in 1970s, 1990, 2000, 2010. The lake boundry data was extracted from remote sensing image like Landsat MSS, TM, ETM+, by means of visual interpretation. The data type is vector data, and it's attribute class includes Area (km²). The Projected Coordinate System is Albers Conical Equal Area. It is mainly used in the study of changes in lakes, hydrological and meteorological on the Tibetan Plateau.