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.
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.
This data set is mainly the SRTM terrain data obtained by International Center for Tropical Agriculture （CIAT）with the new interpolation algorithm, which better fills the data void of SRTM 90. The interpolation algorithm was adpoted from Reuter et al. (2007). SRTM's data organization method is as follows: divide a file into 24 rows (-60 to 60 degrees) and 72 columns (-180 to 180 degrees) in every 5 degrees of latitude and longitude grid, and the data resolution is 90 meters. Data usage: SRTM data are expressed as elevation values with 16-bit values (-/+/32767 m), maximum positive elevation of 9000m, and negative elevation (12000m below sea level). For null data use the -32767 standard.
Data content: the data set product contains the 30-meter resolution product of suspended solids concentration in the water body of the Qinghai-Tibet Plateau, which can be used as the key parameters for ecosystem-related research in Qinghai-Tibet Plateau. Data sources and processing methods: Product inversion is mainly based on the Landsat series data, by extracting the effective aquatic reflectance, to obtain the water composition information. This product is the preliminary result of extracting the concentration information of suspended solids in water using the empirical / semi-empirical method. Data quality: the overall accuracy is high, and the product will be further optimized in combination with the measured data of scientific research. Results and prospects of data application: the data set will be continuously updated and can be used for the study and analysis of ecosystem change in the Qinghai-Tibet Plateau.
The data set is based on NDVI 3G calculated by GIMMS AVHRR sensor data, which represents the greenness of vegetation. The source data range is global, and the Qinghai Tibet plateau region is selected in this data set. This data integrates the original semi monthly scale data into the monthly data. The processing method is to take the maximum value of two NDVI of a month to achieve the effect of noise removal as far as possible. This data set is one of the most widely used NDVI data, and is often used to evaluate the temporal and spatial patterns of vegetation greenness, which has practical significance and theoretical value.
This dataset contains land cover products in Qilian Mountain Area in 2020. The dataset was produced based on the product in 2019 using change monitoring method on the Google Earth Engine platform using Landsat series data. The overall accuracy of this product is above 85%. This is a continuation of the products from 1985-2019.
This data set includes 30 m cultivated land and construction land distribution products in Qilian Mountain Area in 2020. The product comes from the land cover classification product of 30 m in Qilian Mountain Area in 2020. The land cover classification products of 30m in 2020 areproduced using change detection method based on the land cover classification product of 2019 in Google Earth engine platform with the Landsat series data . The overall accuracy of the product is better than 85%. This product is a continuation of the human activity parameter product from 1985 to 2019，which also can be downloaded from this website.
Reasonable carrying capacity, also known as theoretical carrying capacity, refers to the maximum number of domestic animals that can be carried by a certain grassland area in a certain period of time under the premise of moderate grazing (or mowing) and maintaining sustainable production of grassland to meet the needs of normal growth, reproduction and production of livestock. Based on the MODIS inversion data of forage yield (fresh weight, kg / hm2), the reasonable carrying capacity of grassland (sheep unit, mu / km2) was evaluated according to the code for calculation of grassland carrying capacity and grass livestock balance (DB 51 / t1480-2012) and calculation of reasonable carrying capacity of natural grassland (NY / T 635-2015), The time series is 2000-2019, and the spatial resolution is 250m. This data set can analyze the temporal and spatial variation characteristics of the theoretical carrying capacity under the condition of rational utilization of grassland in the Qinghai Tibet Plateau, evaluate the carrying capacity characteristics of grassland in the Qinghai Tibet Plateau, and extract the overgrazing areas, which has important application value for ecological protection, monitoring and early warning of the Qinghai Tibet Plateau.
Grassland yield is an important ecological parameter of grassland, which is an important basis for monitoring grassland productivity, Estimating Grassland reasonable carrying capacity and evaluating grassland carrying status. Based on the grassland data collected in July and August, MODIS NDVI, precipitation and terrain parameters, multivariate statistical equations were established to invert the total grass yield (kg / hm2) and edible grass yield (kg / hm2). The time series is 2000-2019, and the spatial resolution is 250 meters. Based on the data of 50 quadrats distributed in Sichuan, Tibet, Qinghai, Gansu and other regions, the results show that the average absolute error of total grass yield is 734.75kg/hm2, and the average relative error is 24.85%. The average absolute error of edible grass yield is 715.81kg/hm2, and the average relative error is 30.52%. Due to the complexity of grassland types, high spatial heterogeneity and scale mismatch between the measured grassland quadrats and MODIS image pixels, this accuracy can meet the requirements of remote sensing monitoring of grassland in large areas. This data set can analyze the spatiotemporal variation characteristics of grassland productivity in the Qinghai Tibet Plateau, evaluate the carrying capacity characteristics of grassland in the Qinghai Tibet Plateau, and extract the overgrazing areas, which has important application value for ecological protection, monitoring and early warning of the Qinghai Tibet Plateau.