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.
A total of 52 sample sites were selected in the desert belts of Qinghai and Tibet for field sampling of aboveground biomass of vegetation during the vegetation growing season in 2019 and 2020. At the same time, the longitude, latitude and altitude of the experimental site were recorded using handheld GPS devices. The field setting method of the quadrate is as follows: select a section with uniform vegetation. When the vegetation is relatively abundant, the quadrate is set as a 10 m x10 m square plot, and when the vegetation is relatively sparse, the quadrate is set as a 30 m x30 m square plot or a 30 m x90 m rectangular plot. 3-5 small sample boxes (1m x 1m) were randomly thrown into the set sample plot to determine the specific location of the sample. Collect plant samples by sample harvesting method: register plant species, number of plants of each species and other information in sample area of 1 square meter. All kinds of plants in the quadrate were planted and mowed on the ground, and the collected herbaceous plant samples were placed in archives and marked with species, sample site name and number, collection time and other information. They were brought back to the laboratory and dried to a constant weight in a constant temperature drying oven at 65 ℃. The dry weight of the plant samples was measured. Finally, the aboveground biomass of the vegetation was calculated. In addition, two kinds of remote sensing net primary productivity (NPP) data of the 52 sample points were extracted by the longitude and latitude of the sampling points. (1) Enhanced Vegetation Index (EVI) from 2000 to 2018, and calculated the annual Integrated Enhanced Vegetation Index (IEVI). IEVI was highly correlated with net primary productivity (NPP). Can be used as a proxy indicator of net primary productivity (He et al. 2021, Science of The Total Environment). (2) Percentage of remote sensing net primary productivity (NPP) and its quality control (QC) in 2001-2020, NPP remote sensing data from MOD17A3HGF Version 6 product (https://lpdaac.usgs.gov/products/mod17a3hgfv006/), the net photosynthetic value (the total primary productivity - keep breathing) is calculated. In the sample sites with low vegetation coverage, there may be null value (NA) of remote sensing net primary productivity.
A monthly data set of potential evapotranspiration based on the Penman-Monteith formula (1980-2018) of 1km on the Zoige Plateau. We collected daily meteorological data from 1980 to 2018 from the Meteorological Data Sharing Center of China Meteorological Administration, calculated daily-scale potential evapotranspiration through the Penman-Monteith equation, and accumulated daily-scale potential evapotranspiration to obtain monthly and annual potential evapotranspiration (PET mm/month) , Through the Anusplin professional meteorological interpolation software, the multi-year average annual temperature (MAT) and annual average precipitation (MAP) calculated by each meteorological station are interpolated to obtain a 1km resolution spatial data set.
The effective energy and material transfer (EEMT) data set (1980-2018) for 1km on the Zoige Plateau. Effective energy and matter transfer (EEMT) is closely related to the structure and function of the earth's key zones. The unit of effective energy and matter transfer (EEMT) is (Jm-2 s-1or W m-2). The heat energy (EPPT) related to the effective rain energy material transfer, the net primary production energy material transfer (EBIO), and the effective energy and material transfer (EEMT) (which is the sum of both EPPT and EBIO) are used as comprehensive climate indicators, The EEMTMODEL model simulation method is used to evaluate these three indicators, and the Anusplin interpolation software is used to obtain a spatial data set with a resolution of EEMT 1km.
This data set contains experimentally measured soil nutrient data collected in typical small watersheds in Sichuan Province, Tibet Autonomous Region and Qinghai Province. The data comes from the survey of grassland, cultivated land, and woodland in Minhe County, Menyuan County and the east area of Qinghai Lake in the second Qinghai-Tibet Plateau scientific expedition, and recorded detailed soil parameters (including organic carbon, ph, soil Cation exchange capacity, water content, etc.) can provide important values for tracing the source of soil water erosion in small watershed areas and understanding the soil environment.