Normalized Difference Vegetation Index (NDVI) is the sum of the reflectance values of the NIR band and the red band by the Difference ratio of the reflectance values of the NIR band and the red band. Vegetation index synthesis refers to the selection of the best representative of vegetation index within the appropriate synthesis cycle, and the synthesis of a vegetation index grid image with minimal influence on spatial resolution, atmospheric conditions, cloud conditions, observation geometry, and geometric accuracy and so on. This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly NDVI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
Net Primary Productivity (NPP) refers to the total amount of organic matter produced by photosynthesis in green plants per unit time and area. As the basis of water cycle, nutrient cycle and biodiversity change in terrestrial ecosystems, NPP is an important ecological indicator for estimating earth support capacity and evaluating sustainable development of terrestrial ecosystems. This data set includes the monthly synthesis of 30m*30m surface LAI products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly NPP products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
Fractional Vegetation Coverage (FVC) is defined as the proportion of the vertical projection area of Vegetation canopy or leaf surface to the total Vegetation area, which is an important indicator to measure the status of Vegetation on the surface. In this dataset, vegetation coverage is an evaluation index reflecting vegetation coverage. 0% means that there is no vegetation in the surface pixel, that is, bare land. The higher the value, the greater the vegetation coverage in the region. This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly FVC products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
Leaf Area Index (LAI) is defined as half of the total Leaf Area within the unit projected surface Area, and is one of the core parameters used to describe vegetation. LAI controls many biological and physical processes of vegetation, such as photosynthesis, respiration, transpiration, carbon cycle and precipitation interception, and meanwhile provides quantitative information for the initial energy exchange on the surface of vegetation canopy. LAI is a very important parameter to study the structure and function of vegetation ecosystem. This data set includes the monthly synthesis of 30m LAI products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly LAI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
This data uses a large number of MODIS remote sensing images to analyze and calculate the surface vegetation coverage of the Qinghai Tibet Plateau from 2000 to 2018 based on the Google Earth engine platform. Vegetation index (NDVI) is an important index for monitoring ground vegetation. The 6th edition data of Terra moderate resolution imaging spectrometer (MODIS) vegetation index level 3 product (mod13q1) are generated every 16 days with a spatial resolution of 250 meters. The annual average NDVI index calculated based on GEE platform can reflect the long-term change trend of vegetation coverage from 2000 to 2018. Meanwhile, the multi-year average NDVI index from 2000 to 2018 reflects the spatial distribution of the Qinghai Tibet Plateau. The spatial-temporal change monitoring of vegetation index (NDVI) is an indispensable basic information and key parameter for environmental change research and sustainable development planning, which is helpful to understand the changes and impacts of some ecological factors (temperature, precipitation) under the background of climate change.
As the basis of ecosystem material and energy cycle, net primary productivity (NPP) of vegetation can reflect the carbon sequestration capacity of vegetation at regional and global scales. It is an important indicator to evaluate the quality of terrestrial ecosystem. Aiming at the production of net primary productivity products of vegetation, based on the principle of light energy utilization model and coupling remote sensing, meteorological, vegetation and soil type data, the modeling of ecosystem productivity in national barrier area was studied. In terms of parameter selection, the photosynthetic effective radiation (APAR) is calculated from the spot/veg etation NDVI satellite remote sensing data, China's vegetation map, total solar radiation and temperature data; Compared with the soil water molecular model, the regional evapotranspiration model can simplify the parameters and enhance the operability of the model. Taking photosynthetic effective radiation and actual light energy utilization as input variables of CASA (Carnegie Ames Stanford approach) model, the net primary productivity of land vegetation on the Qinghai Tibet Plateau with a resolution of 1km from 2000 to 2018 was estimated based on the parametric model.
The Normalized Difference Vegetation Index (LST) dataset is original from MODIS products and preprocessed by format conversion, projection and resampling. The existing format is TIFF and projection is Krasovsky_1940_Albers. The data set has a spatial resolution of 1000 meters and provides one image per year during the period from 2001 to 2020. NDVI products are calculated by reflectance of red and near-infrared bands, which can be used to detect vegetation growth state and vegetation coverage. NDVI is ranged from -1 to 1, and the negative value means the land is covered by snow, water, etc. By contrast, positive value means vegetation coverage, and the coverage increases with the increase of NDVI.
The considerable amount of solid clastic material in the Yarlung Tsangpo River Basin (YTRB)) is one of the important components in recording the uplift and denudation history of the Tibet Plateau. Different types of unconsolidated sediments directly reflect the differential transport of solid clastic material. Revealing its spatial distribution and total accumulation plays an important value in the uplift and denudation process of the Tibet Plateau. The dataset includes three subsets: the type and spatial distribution of unconsolidated sediments in theYTRB, the thickness spatial distribution, and the quantification of total deposition. Taking remote sensing interpretation and geological mapping as the main technical method, the classification and spatial distribution characteristics of unconsolidated sediments in the whole YTRB (16 composite sub-basins) were comprehensively clarified for the first time. Based on the field measurement of sediment thickness, the total accumulation was preliminarily estimated. A massive amount of sediment is an important material source of landslide, debris flow and flood disasters in the basin. Finding out its spatial distribution and total amount accumulation not only has theoretical significance for revealing the key information recorded in the process of sediment source to sink, such as surface environmental change, regional tectonic movement, climate change and biogeochemical cycle, but also has important application value for plateau ecological environment monitoring and protection, flooding disaster warning and prevention, major basic engineering construction, and soil and water conservation.
This dataset is a high-frequency observation data of soil temperature and humidity in the active layer of seasonal frozen soil observed in the alpine meadow of Qianhuli Small watershed of Qinghai Lake, with a time resolution of half an hour. The data set can provide data support for the rate-dependent soil hydrothermal model and dynamic characterization of soil active layer.
The data are DEM and orthographic image data along the Nyangqu River of Yarlung Zangbo River. The camera carried by DJI UAV was used to take photos of the sampling section of Nyangqu River according to the set flight path. The overlap of adjacent photos was not less than 70%. The photos were utilized by Agisoft Metashape software to generate orthography image and DEM. Orthography image contains three bands: red, green and blue. The sampling river reaches of Nyangqu River basin contained four locations of main channel and two locations of tributaries. The resolution of the digital elevation model was less than 1.0m and the coordinate system was WGC1984. The data set can provide data support for the accurate simulation of flood disaster in the Nyangqu River, and further serve the prevention and control of flood disaster and risk assessment, which has important scientific and engineering value.