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
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 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
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
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
