Carbon, nitrogen, phosphorus, sulfur and potassium are important basic life elements of ecosystem. It plays an important role in revealing the impact of its regional variation and spatial pattern on human activities and the sustainable development of ecosystem in the future. The Qinghai Tibet Plateau has unique alpine vegetation types and rich vertical zone landforms and surface cover types. The biogeographic pattern of surface elements (carbon, nitrogen, phosphorus, sulfur, potassium) is an important manifestation of the coupling of carbon, nitrogen and water cycle processes and related mechanisms of alpine ecosystems. This dataset focuses on the distribution pattern and spatial variation of surface materials (plant leaf branch stem root and litter) in the complex ecosystem of the Water tower area of Qinghai Tibet Plateau and Himalayan Mountains, in order to provide data support for regional model simulation and ecological management.
LI Mingxu
This data set is a 30m land use / cover classification product in the Sahel region of Africa every five years from 1990 to 2020. The product is based on a collaborative framework of land cover classification integrating machine learning and multiple data fusion, and integrates supervised land cover classification with existing thematic land cover maps by using Google Earth engine (GEE) cloud computing platform. The classification system adopts FROM_ GLC classification system includes 8 categories: cultivated land, forest, grassland, shrub, wetland, water body, impervious surface and bare land. The data set has been verified by a large number of seasonal samples in the Sahel region. The overall accuracy of the data set is about 75%, and the accuracy of change area detection is more than 70%. It is also very similar to FAO and the existing land cover map. The data set can provide data support for the sustainable use of land resources and environmental protection in the Sahel region of Africa.
YU Le
The gridded desertification risk data of The Arabian Peninsula in 2021 was calculated based on the environmentally sensitive area index (ESAI) methodology. The ESAI approach incorporates soil, vegetation, climate and management quality and is one of the most widely used approaches for monitoring desertification risk. Based on the ESAI framework, fourteen indicators were chosen to consider four quality domains. Each quality index was calculated from several indicator parameters. The value of each parameter was categorized into several classes, the thresholds of which were determined according to previous studies. Then, sensitivity scores between 1 (lowest sensitivity) and 2 (highest sensitivity) were assigned to each class based on the importance of the class’ role in land sensitivity to desertification and the relationships of each class to the onset of the desertification process or irreversible degradation. A more comprehensive description of how the indicators are related to desertification risk and scores is provided in the studies of Kosmas (Kosmas et al., 2013; Kosmas et al., 1999). The main indicator datasets were acquired from the Harmonized World Soil Database of the Food and Agriculture Organization, Climate Change Initiative (CCI) land cover of the European Space Agency and NOAA’s Advanced Very High Resolution Radiometer (AVHRR) data. The raster datasets of all parameters were resampled to 500m and temporally assembled to the yearly values. Despite the difficulty of validating a composite index, two indirect validations of desertification risk were conducted according to the spatial and temporal comparison of ESAI values, including a quantitative analysis of the relationship between the ESAI and land use change between sparse vegetation and grasslands and a quantitative analysis of the relationship between the ESAI and net primary production (NPP). The verification results indicated that the desertification risk data is reliable in the Arabian Peninsula in 2021.
XU Wenqiang
1) Data content: species list and distribution data of sand lizard and hemp lizard in the Qaidam Basin, including class, order, family Chinese name, family Latin name, genus Chinese name, genus Latin name, species Latin name, species Chinese name, country, province, city, county, town and township, etc; 2) Data source and processing method: Based on the field investigation of amphibians and reptiles in the arid desert area of the Qaidam Basin from 2007 to 2021, the species composition and distribution range of toad-headed agamas and racerunners in this area are recorded; 3) Data quality description: the investigation, collection and identification personnel of samples are professionals. The collection information of samples is checked to ensure the quality of distribution data; 4) Data application achievements and prospects: comprehensive analysis of species diversity and distribution data of toad-headed agamas and racerunners in the Qaidam Basin can provide important data for biodiversity cataloguing in northwest desert region and arid Central Asia, and provide scientific basis for assessing biodiversity situation and formulating conservation strategies.
GUO Xianguang
This data set includes two infrared cameras and environmental parameter data sets of three terrestrial vertebrates deployed in Qilian Mountain reserve. The equipment is deployed near Sidalong in Qilian Mountain reserve, with a time span of (2020.8-2021.10). Due to equipment maintenance and insufficient illumination, some data are discontinuous, but the data of the two equipment can complement each other and reconstruct all the information of observation points in Qilian Mountain reserve from August 2020 to October 2021. One of the two devices is equipped with an infrared camera, which collects 4994 photos, which can be matched with the above sensor photos, or the ecological factor information before and after taking photos. 1. Wild animals and temperature, humidity, light, pressure and network signal strength information in Qilian Mountain reserve. The acquisition interval is once every half an hour 2. Data source: "development of terrestrial vertebrate monitoring equipment", 2016yfc0500104, completed by: Institute of zoology, Chinese Academy of Sciences, raw data, unprocessed 3. The sensor data acquisition interval is every half an hour. The temperature accuracy is plus or minus 0.1 degrees and the humidity accuracy is plus or minus 0.5%. The photo data is divided into trigger and timing. The trigger data is generally triggered by wild animals in the field of vision of the infrared camera; the timing photo data is dynamically adjusted according to the battery power, and the acquisition interval is between 1-12 hours. 4. This data can be used to record the ambient temperature in the reserve. Combined with the infrared camera data, it can be used to analyze the activity rhythm of wild animals, coexistence analysis and distribution limiting factors.
QIAO Huijie
Leaf area index is an important structural parameter of ecosystem, which is used to reflect the number of plant leaves, changes in canopy structure, life vitality of plant community and its environmental effects, provide structured quantitative information for the description of material and energy exchange on the surface of plant canopy, and balance the energy of carbon accumulation, vegetation productivity and interaction between soil, plant and atmosphere in ecosystem, Vegetation remote sensing plays an important role. The data comes from the distributed leaf area index instrument independently developed by the project (based on hemispheric image), which takes hemispheric images of forest canopy at fixed time, fixed point and from bottom to top, and uploads them through wireless network. This data acquisition is the original hemispherical image, which needs further processing to calculate the leaf area index, which can be processed by hemiview and other software.
SU Hongxin
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.
PENG Yan
The dataset is the modified soil adjusted vegetation index (MSAVI) products from 1980s to 2019 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the MSAVI equation which modifies the problem that SAVI is not sensitive in the dense vegetation area.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.MSAVI is stable in the dense vegetation area, but is not sensitive in the sparse vegetation area .
PENG Yan
The dataset is the normalized burnt ratio (NBR) products from 1980s to 2019 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the NBR equation which use the difference ratio between the NIR band and SWIR1 band to enhance the feature of the burned area.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.NBR is usually used to extract burned area information effectively, and to monitor the vegetation restoration in burned area .
PENG Yan
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.
PENG Yan
The dataset is the Landsat normalized difference vegetation index (NDVI) products from 1980s to 2019 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the NDVI equation which defined the difference between NIR band and red band.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow. The NDVI can indicate the health of vegetation and the growth of vegetation,it is thusly widely used in agriculture, forestry, ecological environment and other fields. It is also an important input parameter for the inversion of ecological physical parameters, and is one of the most widely used vegetation indexes.
PENG Yan
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.
PENG Yan
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 .
PENG Yan
The dataset is the salinity index (SI) products from 1980s to 2019 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the SI equation which is based on the method that the red band and blue band can well reflect the soil salinity.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.SI is usually used to quantitatively evaluate the salinized soil .
PENG Yan
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.
LIU Huichan
Data content: The data set products include impervious surface products with a resolution of 10 meters in the Qinghai-Tibet Plateau, which can be used as a key parameter for related research on the Qinghai-Tibet Plateau ecosystem. Data source and processing method: Product inversion is mainly based on Sentinel series data, considering joint features, combining depth spatial features, long-time NDVI and other exponential features, and topographic features, and using random forest model to achieve impervious surface information extraction. Data quality: The overall accuracy is high. Data application results and prospects: The data set will be continuously updated and can be used to further clarify the impact of human activities on the ecosystem of the Qinghai-Tibet Plateau.
WANG Guizhou
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.
YE Jiansheng
In this paper, we review evidence for a major biotic turnover across the Oligocene/Miocene in the Tibetan Plateau region. Based on the recent study of six well-preserved fossil sites from the Cenozoic Lunpola and Nima basins in the central Tibetan Plateau, we report a regional changeover from tropical/subtropical ecosystems in the Late Oligocene ecosystem (26–24 Ma) to a cooler, alpine biota of the Early Miocene (23–18 Ma). The Late Oligocene fossil biota, comprising of fish (climbing perch), insects and plants (palms), shows that the hinterland of the Tibetan Plateau was a warm lowland influenced by tropical humidity from the Indian Ocean. In the Early Miocene, the regional biota became transformed, with the evolution and diversification of the endemic primitive snow carp. Early Miocene vegetation was dominated by temperate broad-leaved forest with abundant conifers and herbs under a cool climate, and mammals included the hornless rhinoceros, Plesiaceratherium, a warm temperate taxon. This dramatic ecosystem change is due to a cooling linked to the uplift of Tibetan region, from a Late Oligocene paleo-elevation of no greater than 2300 m a.s.l. in the sedimentary basin to a paleo-elevation of about 3000 m a.s.l. Another factor was the Cenozoic global climatic deterioration toward to an ice-house world.
DENG Tao
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Dunhuang Station from January 1 to December 31, 2020. The site (93.708° E, 40.348° N) was located on a wetland in the Dunhuang west lake, Gansu Province. The elevation is 990 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4m and 8 m, towards north), wind speed and direction profile (windsonic; 4m and 8 m, towards north), air pressure (1 m), rain gauge (4 m), infrared temperature sensors (4 m, towards south, vertically downward), soil heat flux (-0.05 and -0.1m ), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation in the south of tower, -0.05 and -0.2 m), photosynthetically active radiation (4 m, towards south), four-component radiometer (4 m, towards south), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m, Ta_8 m; RH_2 m, RH_4 m, RH_8 m) (℃ and %, respectively), wind speed (Ws_4 m, Ws_8 m) (m/s), wind direction (WD_4 m, WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_0.05m, Gs_0.1m) (W/m^2), soil temperature (Ts_0.05m, Ts_0.2m) (℃), soil moisture (Ms_0.05m, Ms_0.2m) (%, volumetric water content), soil conductivity (Ec_0.05m, Ec_0.2m)(μs/cm), sun time(h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day and missing records were denoted by -6999.. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column.
ZHAO Changming, ZHANG Renyi
The data includes: zooplankton species list; zooplankton density; microscopy; high-throughput sequencing; complete data; constructing an original data set for lakes on the Qinghai-Tibet Plateau. Zooplankton is an indispensable link in lake water ecological investigation, and it is a link between the system The location of the food web is an important carrier for the material circulation and energy flow of the food web. The systematic investigation and study of the composition and biodiversity of the zooplankton in the lakes on the Qinghai-Tibet Plateau is particularly important for understanding the stability and resilience of the lake ecosystem on the Qinghai-Tibet Plateau. In addition, Zooplankton are very sensitive to environmental changes, and changes in their structure and functional groups can indicate the intensity and magnitude of environmental pressure.
LI Yun
The dataset is the 30 meter resolution leaf area index (LAI) product from 2010 to 2019 over the Tibetan Plateau. The LAI product was retrieved using Landsat time series data and physically based radiative transfer model, and it is the annual maximum synthetic leaf area index product. When validated with the simulation data set, the root-mean-square error (RMSE) was 1.16. Leaf area index highly integrates the horizontal coverage and vertical structure of vegetation, and is an important structural parameter of the vegetation canopy, which can provide data product support for the research and applications in land surface process simulation, resources survey, ecological environment monitoring, global change research and other fields.
ZHANG Zhaoming
The dataset is the fractional vegetation cover (FVC) products from 1980s to 2019 over the Tibetan Plateau。The dataset is producted based on Landsat surface reflectance dataset. It is calculated by the FVC equation which is based on dimidiate pixel model of NDVI.And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.FVC is an important ecological parameter, which is widely used in ecological environmnet monitoring .
ZHANG Zhaoming
This is a photo collection of birds in Qinghai-Tibet Plateau produced during the fieldtrip in December 2020 to January 2021. The geographical area mainly covers the middle-down stream of the Yarlung Zangbo River valley, including Lhasa, Qushui, bird species pheasants, buzzards, laughingthrushs rosefinches and accendors. The species were identified by Song Gang, Xing Jiahua, Qiao Huijie from IOZ, Yang Le, Zhou Shengling from Institute of Plateau Biology, Tibet Autonomous Region, and Yixi Duojie from Museum of Natural Science of Tibet
SONG Gang
This database includes the occurrence records of birds in Qinghai-Tibet Plateau produced during the fieldtrip in December 2020 to January 2021. The geographical area mainly covers the middle-down stream of the Yarlung Zangbo River and eastern coast of Namtso lake, covering mang vallies, villiages and wetlands of Lhasa, Linzhi, Shannan, Rikaze. The information of each record is composed of species name, coordinates, date of field observation and observers.
SONG Gang
From July to August 2019, take Hongyuan County on the Qinghai-Tibet Plateau as the scientific research site, select typical land use types of grassland and typical slopes to set up transects. After plant sample surveys, the soil profiles of grassland, shrubs, and wetland ecosystems (0-10 cm, 10-20 cm, 20-40 cm, 40-60 cm and 60-100 cm) soil samples were collected, 3 replicates for each soil layer, 104 soil samples were collected, and the soil was measured The bulk density and water content.Through the sampling of various lines to form the surface sampling points and spatial data sets of the Baihe River Basin, it simulates the spatiotemporal pattern of typical water and soil ecosystem services such as ecosystem production, carbon fixation, hydrological regulation and soil conservation, and reveals the spatiotemporal changes of water and soil ecosystem services at the basin scale Pattern, combined with factors such as climate change, socio-economic data, implementation of ecological and environmental protection policies, and land use change.
HU Jian
From July to August 2019, taking Hongyuan County on the eastern edge of the Zoige Plateau as the scientific research site, select alpine grasslands with typical land use types and typical slopes to set up transects, and set up transects every 50m from the top to the foot of the mountain. The characteristics of the plant community were investigated. The plot size was 50cm×50cm, with 3 replicates, a total of 63 plant plots were investigated, and the number of plant species, quantity, above-ground biomass, diversity index, etc. were obtained. Provide reliable data for the study of plant productivity and community changes in different altitude gradients and different grassland types.Accurately quantifying the effects of alpine grass and shrub vegetation changes on plant communities and vegetation evolution will help optimize multi-objective management of grassland ecosystems on the Qinghai-Tibet Plateau.
HU Jian
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.
HU Jian
Collect daily meteorological data from 1980 to 2018 from the Meteorological Data Sharing Center of China Meteorological Administration. Humidity Index (HI) is calculated by the ratio of annual precipitation to potential evapotranspiration. Anusplin interpolation software is used to obtain a spatial dataset of HI 1km resolution.Through spatial data collection, model simulation of the spatiotemporal pattern of typical water and soil ecosystem services such as ecosystem production, carbon fixation, hydrological regulation, and soil conservation, revealing the spatiotemporal change pattern of water and soil ecosystem services in the watershed, combining climate change, socioeconomic data and ecological environmental protection policies Implementation, land use conversion and other factors, combined with trade-off analysis and structural equation modeling to quantify the trade-offs and synergies of these water and soil ecosystem services and their main driving forces, to provide more effective and scientific ecological protection and multi-purpose land use for Ruoergai Wetland Optimal management provides theoretical support.
HU Jian
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.
HU Jian
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.
SU Zhengan
The data set records the output information of main crops in Qinghai Province from 1978 to 2016, mainly including grain, oil, fruit, meat and eggs and main industrial products, aluminum, crude oil, steel, cement and power generation. The data set contains three data tables (1. The data table of main industrial and agricultural products per capita has 17 fields; 2. The data table of crop production by counties has 13 fields; 3. The data table of main industrial and agricultural products per capita and main agricultural products. There are 6 fields in total). The data comes from: "Qinghai Social and Economic Statistical Yearbook" and "Qinghai Statistical Yearbook", with the same precision as the statistical yearbook extracted from the data. This data set is of great value for studying food security and agricultural production in Qinghai Province.
SU Zhengan
The data set is based on the field observation and survey along the roads in Sichuan, Qinghai and Tibet. 100 * 100m sample plots are selected along the roads, and 1m * 1m or 2m * 2m sample plots are selected according to the vegetation distribution. The survey content involves the weather, geographical location, geomorphic characteristics, slope direction, slope position, soil type, vegetation type, plant community name, surface characteristics, human activity mode and vegetation status in the sample plot. For the investigation of basic information and vegetation status of the sample plot, the methods of artificial observation and tool measurement are adopted. In the vegetation status, the vegetation name refers to "herb species in Qinghai Province", mainly investigating its height, coverage, life form and other information. The summary of the survey results of the data set can be used as a reference to supplement the herb diversity of the Qinghai Tibet Plateau. The data set is the vegetation survey content of the actual sample plot, one file per day, and the file naming method is: year + day. For example, 20200712 represents the questionnaire content on July 12, 2020, and 202007023 represents the questionnaire content on July 23, 2020.
LI Jingji
Data content: The data set products include impervious surface products with a resolution of 30 meters in the Qinghai-Tibet Plateau, which can be used as a key parameter for related research on the Qinghai-Tibet Plateau ecosystem. Data source and processing method: Product inversion is mainly based on Landsat series data, considering joint features, combining depth spatial features, long-time NDVI and other exponential features, and topographic features, and using random forest model to achieve impervious surface information extraction. Data quality: The overall accuracy is high, better than 80% in most areas. Data application results and prospects: The data set will be continuously updated and can be used to further clarify the impact of human activities on the ecosystem of the Qinghai-Tibet Plateau.
WANG Guizhou
The dataset is the 30m resolution burned area product from 1980s to 2019 over the Tibetan Plateau. The dataset is produced using Landsat time series land surface reflectance and machine learning algorithm, and the overall accuracy is over 90%. It can provide data product support for the research and applications in fire monitoring, carbon emission studies, ecological environment monitoring, global change research and other fields.
ZHANG Zhaoming
Aboveground biomass (AGB) is an important indicator for measuring ecosystem productivity.This dataset provides the forest aboveground biomass with a resolution of 30m in the Qinghai-Tibet Plateau from 1970s-2022. The biomass data is estimated using Landsat series data, based on actual ground data and some literature data, tree height data, and forest types including coniferous forest, broad-leaved forest and mixed forest.Through data disclosure and free download services, it provides basic data support for related research on the dynamic changes of forest ecosystems on the Qinghai-Tibet Plateau, and also provides a scientific basis for sustainable forest management in this region.
ZHANG Xiaomei
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.
PENG Dailiang
The dataset is the land surface temperature (LST) product from 1980s to 2019 over the Tibetan Plateau. The dataset is retrieved based on Landsat images and a practical single-channel (PSC) algorithm. When validated with the simulation data set, the root-mean-square error (RMSE) of the PSC algorithm was 1.23 K. The corresponding quality assessment (QA) product is also generated to identify cloud, cloud shadow, ice and snow. LST is a commonly used land surface parameter, which can provide data product support for the research and applications in resources survey, ecological environment monitoring, global change research and other fields.
ZHANG Zhaoming
The dataset is the Landsat surface reflectance products from 1980s to 2019 over the Tibetan Plateau, it is the key input parameter of many surface geophysical parameters (such as leaf area index, chlorophyll and biomass). The dataset is retrieved based on Landsat level 4 products from China satellite remote sensing ground station, and it is retrived by using the atmospheric correction based on 6S model and BRDF correction model based on C-factor .The RMSE of geometric correction is less than 12m and the RMSD of surface reflectance is less than 5%. And the corresponding production of quality identification documents (QA) is also generated to identify the cloud, ice and snow.The Landsat surface reflectance play an important role in forest, water resources, climate change.
PENG Yan
The dataset includes FPAR, GPP, NPP, evapotranspiration product and LAI product. FPAR product is and LAI product are obtained from the MODIS Terra MOD15A2H dataset, GPP and NPP product are obtained from the MOD15A2H dataset and evapotranspiration product is obtained from MODIS Terra MOD16A2 dataset from 2000 to 2019 over the Tibetan Plateau,which is downloaded from USGS, and their formats are converted from .hdf to .tif by GDAL.The quality assessment files are also included for aboved products,and they are stored in an efficient bit-encoded manner.The MODIS products play an important role in forest, agriculture, ecology.
GONG Chengjuan
The dataset is the MODIS Terra surface reflectance products from 2000 to 2019 over the Tibetan Plateau,each period of data contains 13 files: 7 surface reflectence files, 3 observation angle files, 2 quality control files and 1 time description file. The dataset is download from USGS and its format is converted from .hdf to .tif by GDAL.The sur_refl_qc_500m and sur_refl_state_500m layers are the quality identification documents,which are stored in an efficient bit-encoded manner.The MODIS surface reflectance play an important role in forest, water resources, climate change.
GONG Chengjuan
This data set contains the biological property data of soil samples from several scientific research routes in the Qinghai Tibet Plateau from 2019 to 2021, including the information of the collector, collection time, collection location, longitude and latitude, altitude, vegetation type, sampling depth, phosphatase activity, microbial respiration, nitrogen transformation characteristics, functional gene abundance, fungi, bacteria, protobiotic diversity, etc. The analysis of various soil properties refers to the requirements of "technical specification for soil environmental quality monitoring", and the first-hand data obtained through laboratory analysis. The data quality is controlled by determining blank samples, duplicate samples and standard samples. The data set can be used to evaluate soil quality and function under the influence of climate change and human activities.
ZHANG Limei
Soil profiles in this dataset were surveyed in the western and central Qinghai-Tibet Plateau in July 2019, including Ali, Xigaze and Naqu of the Tibet and Kashgar and Hotan of the Xinjiang. Information on the profile ID, longitude, latitude, soil types was provided. Soil types were referenced according to the Chinese Soil Taxonomy. The Chinese Soil Taxonomy is a hierarchical system, in which 6 categories were defined: Order, Suborder, Group, Subgroup, Family and Series. The sampling location was recorded by a handheld GPS receiver. Especially, these soil types were initially determined based on the diagnostic horizons and diagnostic properties identified in field. Due to the effect of epidemic, physicochemical properties of some soil samples have not been achieved and thus some soil types need to be updated in the following months.
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.
WANG Xunming
The composition, lamellate structure, diversity and biomass of main desert plant communities in Gansu were investigated, and the density, frequency, dominance and element content of the building plants in the desert plant communities were determined, which provided basic data for the stable maintenance and protection of desert ecosystem in Gansu.This data includes species name, quantity, life form, coverage, density, height and biomass of main desert plant communities in Gansu Province.Community characteristics of 124 sites were investigated, which were divided into herb and shrub parts.At the same time, with latitude and longitude and habitat information.All data were obtained from field surveys by ecological professionals and were original data.The data quality is reliable and can be reused.
WANG Shaokun
Ground-penetrating radar method is a narrow pulse broadband high-frequency electromagnetic wave signal detection of underground media distribution technology method, with fast, non-destructive, continuous detection and real-time display characteristics. The use of detection instrument is GR-IV type geological radar, and set up different power antenna, in August 2019 and August 2020 to carry out 2 years of coverage of Namucuo Niyaqu typical wetland area within the range of ground-penetrating radar observation, obtained the 2019-2020 Namucuo typical wetland ground-penetrating radar data set, and data format is raw.
DU Jianqing
The data set mainly includes the species, quantity, distribution characteristics, production performance data, photos and sample collection information of livestock breeds in typical counties of Qinghai Tibet Plateau. The data set provides basic data for livestock related research in Qinghai Tibet Plateau.The data mainly came from field survey and literature collection, and Excel was used for collation and analysis.To check the data, to eliminate differences, and complement the lack of data, make a good representative data.After the completion of data collection, the genetic diversity of livestock was analyzed and evaluated to provide effective basic research data for animal husbandry research in Qinghai Tibet Plateau.
WANG Fei
The data set mainly includes the species, quantity, distribution characteristics and related characteristics of cultivated herbage, wild herbage and poisonous herbage in typical counties on the Qinghai-Tibet Plateau, as well as the survey data, photos and plant specimen collection information of natural grassland sample plots. The data set can be used to establish the relevant database of herbage in this region, which can be used to analyze the distribution and development of herbage resources in this region, and put forward the utilization and protection countermeasures. Data came from field investigation and literature collection, and Excel was used for sorting and analysis. The obtained data were checked, the data with large differences were eliminated, and the missing data were supplemented to make the data have better representativeness. The data set can be used to study the rational allocation of natural grassland and artificial grassland in Qinghai-Tibet Plateau, and to plan the rational distribution of artificial grassland.
WANG Fei
The data set is the vegetation sample survey photos of Qilian County in Qinghai Province during the second comprehensive scientific expedition to the Qinghai-Tibet Plateau. Iincluding the survey photos of 28 sample points in Qilian County in 2020. The photos of each sample point are placed in a separate In a folder, the folder is named the sample number. The photos of each sample point include photos of the landscape around the sample point, photos of the plant community in the sample square and close-up photos of the dominant species. The vegetation in the sample can be observed The status of the community, the status of the soil surface and the slope of the surrounding sampling area, human interference, etc. The photos are all original images, in jpg format, taken with a mobile phone or camera, and the specific latitude and longitude information of the sampling points are in a separate Excel table in the compressed package In, you can compare and view.
SHANG Zhanhuan
The data contains the survey information of the sample community species of 5 samples at each sampling site, including species name, coverage, height (5 plants) and number of plants. A total of 28 samples (28 Excel forms) are named from QZKK- 03-001 to QZKK-03-028, in a compressed file package, the other three files are the aboveground biomass, underground biomass and the longitude and latitude information of the sampling point of the species, which need to be compared with each other (species height, coverage , Number of plants, aboveground/underground biomass collection and survey plot area is 1m*1m, each sample point has 10 plots, of which 2, 4, 6, 8, 10 and other plots are collected for different species. P2, P4, P6, P8, P10 are expressed, the coverage is expressed as a percentage, the height is cmcm, and the number of plants is expressed by the number of species. The root biomass of each plot is divided into 3 layers, each layer 10cm, each item The units of the indicators are marked in the title). The data are all collected and measured on the spot, the height is measured by a tape measure, and the coverage is obtained by the estimation method. The data is of good quality and can be used to calculate biodiversity and species distribution.
SHANG Zhanhuan
The data file is in 7z compressed package format, which can be unzipped and opened with 7-Zip software. The internal is divided into 2 compressed packages, including 47 samples in Lhasa and Nagqu in 2019, and 49 samples in Ali and Nagqu in 2020. Survey photos. The photos of each sample point are placed in a separate folder. The folder is named the sample number. The photos of each sample point include photos of the landscape around the sample point, photos of the plant community in the sample square, and Close-up photos of the dominant species, you can observe the status of the vegetation community in the plot, the condition of the soil surface, the slope of the surrounding sampling area, human interference, etc. The photos are all original images, jpg format, taken with mobile phones or cameras, sampling The specific longitude and latitude information of the points can be viewed in a separate Excel table in the compressed package.
TIAN Dashuan
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