The data source is Copernicus global land service (CGLS), and the download address is:（ https://lcviewer.vito.be/ ）。 This data provides the land cover / land use type of phase I Sichuan Tibet traffic corridor (including G317, G318 and Sichuan Tibet Railway). The land cover data is automatically extracted from remote sensing images. The acquisition time of remote sensing images is from 2015 to 2019. The classification algorithm adopts proba-v, and the classification accuracy is 80%. Land use types include evergreen broad-leaved forest, deciduous broad-leaved forest, evergreen coniferous forest, deciduous coniferous forest, shrub, grassland, landing, entity, construction land, etc. The original spatial resolution of the data is 100m, and the spatial resolution after resampling is 250m. The data geographic coordinate system is wgs1984, and the projection coordinate system is Mercator projection. The data storage format is TIFF file.
Under the funding of the first project (Development of Multi-scale Observation and Data Products of Key Cryosphere Parameters) of the National Key Research and Development Program of China-"The Observation and Inversion of Key Parameters of Cryosphere and Polar Environmental Changes", the research group of Zhang, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, developed the snow depth downscaling product in the Qinghai-Tibet Plateau. The snow depth downscaling data set for the Tibetan Plateau is derived from the fusion of snow cover probability dataset and Long-term snow depth dataset in China. The sub-pixel spatio-temporal downscaling algorithm is developed to downscale the original 0.25° snow depth dataset, and the 0.05° daily snow depth product is obtained. By comparing the accuracy evaluation of the snow depth product before and after downscaling, it is found that the root mean square error of the snow depth downscaling product is 0.61 cm less than the original product. The details of the product information of the Downscaling of Snow Depth Dataset for the Tibetan Plateau (2000-2018) are as follows. The projection is longitude and latitude, the spatial resolution is 0.05° (about 5km), and the time is from September 1, 2000 to September 1, 2018. It is a TIF format file. The naming rule is SD_yyyyddd.tif, where yyyy represents year and DDD represents Julian day (001-365). Snow depth (SD), unit: centimeter (cm). The spatial resolution is 0.05°. The time resolution is day by day.
The seasonal synthetic satellite remote sensing image collection of the Qinghai-Tibet Plateau is obtained by de-clouding processing of the Sentinel-2 TOA time series product. It contains 4 bands (visible and near infrared bands), and has a spatial resolution of about 10 meters. Each year is divided into 4 quarters from January to March, April to June, July to September, and October to December, and the cloud mask of each image is obtained by synthesizing the visible band, cirrus band, aerosol band and NIR band information of Sentinel-2 data. Finally, the cloud-free satellite remote sensing images of the Tibetan Plateau region are obtained by synthesizes all the masked images in a quarter according to the median principle.
The atlas includes three thematic maps of the Distribution Map of Desert Ecosystem Types on the Tibetan Plateau, the Distribution Map of Suitable Areas for Agriculture and Animal Husbandry on the Tibetan Plateau, and the Desertification Development Trend Map of Desert Ecosystem on the Tibetan Plateau. The time of the maps spans from 2010 to 2020. The original climatic data come from the monthly TerraClimate dataset with a spatial resolution of 1/24° (about 4 km). The data were preprocessed to be those have a spatial resolution of 30-m. The well-known desertification assessment system and the desert ecosystem classification standards were integrated to formulate the classification rules of the desert ecosystem, which were calibrated and validated by the remote sensing data and field survey results. In addition, the algorithms such as machine learning, Random Forest (RF) and Support Vector Machine (SVM) were introduced to generate the Distribution Map of Desert Ecosystem Types on the Tibetan Plateau. The Distribution Map of Suitable Areas for Agriculture and Animal Husbandry on the Tibetan Plateau reflects the supply services of agricultural and animal husbandry products. The vegetation productivity of modern desert ecosystem on the Tibetan Plateau was estimated, which showed the spatial distribution of potential forage supply. The grazing red line is set based on the experience of USDA, including: 1) the potential annual mean vegetation biomass less than 225kg ha-1; 2) More than 1.6km away from water source; 3) Slope greater than 65%; 4) High intensity erosion area. Grazing activities will be strictly prohibited from the areas under the standard of the red line. The areas of main crops (highland barley, Lycium chinense and wheat) in and around the Tibetan Plateau over recent five years are excluded. Based on the maximum information entropy analysis of the climate and geological environment of the existing planting areas, the growth adaptability of the three crops in the desert ecological area of the Tibetan Plateau is assessed to develop new agricultural planting areas from the desert ecological area of the Tibetan Plateau. By the comparison between the modern desert ecosystem of the Tibetan Plateau and the historical desertification in the early 21st century, the Desertification Development Trend Map of Desert Ecosystem on the Tibetan Plateau diagnosed the evolution pattern of the desert ecosystem during the past 20 years, and simulated the generation and extinction probability of the desert ecosystem on the Tibetan Plateau under the assumption that the climate change trend will be stable in the next 50 years. The probability distribution will be an important tool for evaluating the suitability of desert ecosystem protection and development in the Tibetan Plateau in the next 50 years. This atlas has reference value for monitoring the desert ecosystem of the Tibetan Plateau and developing and utilizing the service function of the desert ecosystem of the Tibetan Plateau.
The dataset is the normalized difference moisture index (NDMI) products from 1980s to 2019 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the NDMI equation which use the difference ratio between the NIR band and SWIR2 band to quantitatively reflect the water content of vegetation canopy .And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.NDMI is highly correlated with canopy water content and can be used to estimate vegetation water content, and it is also used to analyze the change of land surface temperature because it is strongly correlated with land surface temperature.
The dataset is the Landsat enhanced vegetation index (EVI) products from 1970s to 2020 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the EVI equation which is added backgroud adjusted parameters C1 and C2, and atmospheric adjusted parameter L based on NDVI equation.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow. Compared with NDVI, EVI has stronger ability to resist atmospheric interference and noise,so it is more suitable for weather conditions with high aerosol content and lush vegetation areas.
The dataset is the soil adjusted vegetation index (SAVI) products from 1980s to 2019 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the SAVI equation which is added soil adjusted parameters S based on NDVI equation.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.SAVI is stable in the sparse vegetation area, but is not sensitive in the dense vegetation area .
The dataset is the normalized difference water index (NDWI) products from 1970s to 2020 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the NDWI equation which use the difference ratio between the green band and NIR band to enhance the water information, and then to weaken the information of vegetation, soil, buildings and other targets.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.NDWI is usually used to extract surface water information effectively, therefore it is widely used in water resoureces, hydrology, forestry and agriculture.
Fraction of Absorbed Photosynthetically Active Radiation (FPAR) is a key physiological variable in the study of carbon cycling and is one of the basic variables to describe vegetation ecosystems. The classification results of surface vegetation types in Qinghai-Tibet Plateau region are obtained based on the Landsat reflectance data(30m spatial resolution). According to NDVI of different vegetation types, the remote sensing inversion model is constructed to produce the growing season FPAR products for each vegetation type. This product can be used as one of the parameters to calculate vegetation carbon sequestration and evaluate vegetation ecosystem status.
This dataset contains the monthly/yearly surface shortwave band albedo, fraction of absorbed photosynthetically active radiation (fPAR), leaf area index (LAI), vegetation continuous fields (tree cover and non-tree vegetation cover, VCF), land surface temperature (LST), net radiation (RN), evapotranspiration (ET), aboveground autotrophic respiration (RA-ag), belowground autotrophic respiration (RA-bg), gross primary production (GPP) and net primary production (NPP) in China from 2001 to 2018. The spatial resolution are 0.1 degree. Moreover, the dataset also includes these 11 ecosystem variables under climate-driven scenario (i.e., under no human disturbance). So, it can show the relative influences of climate change and human activities on land ecosystem in China during the 21st century.