Fractional Vegetation Cover (FVC) refers to the percentage of the vertical projected area of vegetation to the total area of the study area. It is an important indicator to measure the effectiveness of ecological protection and ecological restoration. It is widely used in the fields of climate, ecology, soil erosion and so on. FVC is not only an ideal parameter to reflect the productivity of vegetation, but also can play a good role in evaluating topographic differences, climate change and regional ecological environment quality. This research work is mainly to post process two sets of glass FVC data, and give a more reliable vegetation coverage of the circumpolar Arctic Circle (north of 66 ° n) and the Qinghai Tibet Plateau (north of 26 ° n to 39.85 °, east longitude 73.45 ° to 104.65 °) in 2013 and 2018 through data fusion, elimination of outliers and clipping.
YE Aizhong
Project based on Landsat_ Through manual interpretation and machine learning algorithm, tm30m remote sensing data has completed the extraction of spatial pattern distribution information of six types of ecosystems in Qilian Mountains from 1990 to 2015, including forest, farmland, grassland, wetland, settlement city and desert. This set of data can be used to study the evolution law of regional ecosystem macro pattern, ecosystem service function evaluation, major ecological restoration project planning and effect evaluation. The evolution of ecosystem macro pattern is a macro response to the evolution of natural processes driven by climate socio-economic coupling. It is also a direct reflection of land use and land cover changes. It is also an important data basis for the evaluation of the effectiveness of regional sustainable development. The research can provide data basis for the evaluation of green development index in Qilian mountain area.
WU Feng
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.
WU Junjun , LI Yi, ZHONG Bo
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.
WU Junjun , LI Yi, ZHONG Bo
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.
WU Junjun , LI Yi, ZHONG Bo
The data set product contains the aboveground biomass and vegetation coverage data products of the Qinghai-Tibet Plateau every five years from 1990 to 2020 (1990, 1995, 2000, 2005, 2010, 2015 and 2020).The aboveground biomass of the Qinghai-Tibet Plateau is the remote sensing inversion product of above-ground biomass inversion models based on different land cover types including grassland, forest, etc. Vegetation coverage data of the Qinghai-Tibet Plateau is inversed using remote sensing by the dimidiate pixel model. Among them, the aboveground biomass and vegetation coverage data from 2000 to 2020 were estimated based on MODIS data, the spatial resolution was 250 m; the aboveground biomass and vegetation coverage data of 1990 and 1995 were estimated based on NOAA AVHRR data, the spatial resolution after resampling process is 250 m. This dataset can provide basic data for revealing the temporal and spatial pattern of land cover areas and quality on the Qinghai-Tibet Plateau and supporting the assessment of ecosystems, ecological assets and ecological security.
WU Bingfang
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.
ZHU Juntao
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.
WU Junjun , LI Yi, ZHONG Bo
This dataset provides global soil texture data optimized by remote sensing estimation of wilting coefficient, with a spatial resolution of 0.25 degree. The dataset incorporates remote sensing-based (e.g., SMAP satellite) estimation of soil wilting point and uses the SCE-UA algorithm to optimize two prevalently used soil texture datasets (i.e., GSDE (Shangguan et al. 2014) and HWSD (Fischer et al., 2008)). Comparison results with in-situ observations (44 stations in North America) show that, the soil moisture and evaporative fraction simulation from the Noah-MP land surface model by using the optimized soil texture have been significantly improved.
HE Qing , LU Hui, ZHOU Jianhong , YANG Kun, YANG Kun, 阳坤, YANG Kun, SHI Jiancheng
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.
MA Xudong, HUANG Er, YAN Xufeng, LUO Ming, WANG Lu
This dataset includes the normalized difference vegetation index (NDVI), fractional vegetation cover (FVC), vegetation net primary productivity (NPP), grassland biomass, forest stock volume and vegetation parameter remote sensing products of key areas in the Qilian Mountains from May 2021 to October 2021, with a spatial resolution of 8m . This dataset uses remote sensing data sources such as Gaofen-1, Gaofen-6, Sentinel, and Resource-3, combined with basic data such as meteorology and ground monitoring, and uses the band ratio method, mixed pixel decomposition model, CASA model and other vegetation parameters to reflect Algorithms and models are used to generate remote sensing products of monthly vegetation indices in key areas of Qilian Mountains during the growing season. This dataset provides data support for the diagnosis of regional ecological environment problems and dynamic assessment of the ecological environment by constructing a high-resolution satellite-based ecological environment monitoring dataset with high spatial and temporal resolution.
QI Yuan, ZHANG Jinlong, WANG Hongwei, ZHOU Shengming, CAO Yongpan
This dataset includes the normalized difference vegetation index (NDVI), fractional vegetation cover (FVC), vegetation net primary productivity (NPP), grassland biomass, forest stock volume remote sensing products of vegetation parameters in the Heihe River Basin from May 2021 to October 2021, with a spatial resolution of 8m. This dataset uses remote sensing data sources such as Gaofen-1, Gaofen-6, Sentinel, and Resource-3, combined with basic data such as meteorology and ground monitoring, and uses the band ratio method, mixed pixel decomposition model, CASA model and other vegetation parameters to reflect Algorithms and models are used to generate remote sensing products of monthly vegetation indices in key areas of Qilian Mountains during the growing season. This dataset provides data support for the diagnosis of regional ecological and environmental problems and dynamic assessment of the ecological environment by constructing a high-resolution satellite-based ecological environment monitoring data set.
QI Yuan, ZHANG Jinlong, WANG Hongwei, ZHOU Shengming, CAO Yongpan
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.
LIN Zhipeng, WANG Chengshan , HAN Zhongpeng, BAI Yalige, WANG Xinhang, ZHANG Jian, MA Xinduo, HU Taiyu, ZHANG Chenjin
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.
QIU Haijun
This dataset contains daily land surface evapotranspiration products of 2021 in Qilian Mountain area. It has 0.01 degree spatial resolution. The dataset was produced based on Gaussian Process Regression (GPR) method by fusing six satellite-derived evapotranspiration products including RS-PM (Mu et al., 2011), SW (Shuttleworth and Wallace., 1985), PT-JPL (Fisher et al., 2008), MS-PT (Yao et al., 2013), SEMI-PM (Wang et al., 2010a) and SIM (Wang et al.2008). The input variables for the evapotranspiration products include MODIS products, and MERRA meteorological data.
YAO Yunjun, LIU Shaomin, SHANG Ke
The data is 1:250000 topographic data of the Himalayan mountains where the Himalayan mountain basin is located. It is extracted by strm90m elevation data entity according to the Himalayan mountain boundary mask in ArcGIS software. It is 90m grid resolution. Because DEM describes the ground elevation information, it is widely used in the fields of Surveying and mapping, hydrology, meteorology, geomorphology, geology, soil, engineering construction, communication, military and other national economy and national defense construction, as well as humanities and natural sciences. In terms of flood control and disaster reduction, DEM is the basis for hydrological analysis, such as catchment area analysis, water system network analysis, rainfall analysis, flood storage calculation, inundation analysis, etc.
WANG Zhonggen
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.
WANG Xiaofeng
ChinaSA is raster data with a geospatial extent of 72 - 142E, 16 - 56N, using an equal latitude and longitude projection and a spatial resolution of 0.005°. The dataset covers the period from 1 January 2000 to 31 December 2020 with a temporal resolution of 1 day. The data contains six elements: black sky albedo (Black_Sky_Albedo), white sky albedo (White_Sky_Albedo), solar zenith angle (Solar_Zenith_Angle), pixel-level cloud label (Cloud_Mask), pixel-level forest pixel (Forest_Mask) and pixel-level retrieval label (Abnormal_Mask). Black_Sky_Albedo records the black sky albedo calculated by retrieved, with as a calculation factor of 0.0001 and a data range of 0-10000. White_Sky_Albedo records the white sky albedo calculated by retrieved, with as a calculation factor of 0.0001 and a data range of 0-10000. Cloud_Mask records whether the pixel is cloud type, with a value of 0 indicating non-cloud and 1 indicating cloud. Forest_Mask records whether the pixel has been corrected as a forest type, with a value of 0 indicating that it has not been corrected and 1 indicating that it has been corrected. Abnormal_Mask records whether the retrieval of the black sky albedo and white sky albedo of the pixel is an anomaly of less than 0 or greater than 10000, with a value of 0 indicating a non-anomaly and 1 indicating an anomaly. ChinaSA was retrieved based on the MODIS land surface reflectance product MOD09GA, the snow cover product MOD10A1/MYD10A1 and the global digital elevation model SRTM. The snow albedo retrieval model was developed based on the ART model and produced using the GEE and local side interactions. To assess the retrieval quality of ChinaSA, the accuracy of the snow albedo product was verified using observations from in-situ meteorological stations and the sample observation validation method, and compared with the accuracy of four commonly used albedo products (GLASS, GlobAlbedo, MCD43A3 and SAD). The validation results show that ChinaSA outperforms the other products in all validations, with a root mean square error (RMSE) of less than 0.12, and can achieve a RMSE of 0.021 in forest areas.
XIAO Pengfeng , HU Rui , ZHANG Zheng , QIN Shen
The resilience of vegetation cover in countries along the Belt and Road reflects the level of resilience of vegetation cover in the countries along the Belt and Road, and the higher the value of the data, the stronger the resilience of vegetation cover in the countries along the Belt and Road. The vegetation cover status resilience data products were prepared with reference to the MODIS MOD13A3 dataset from 2000 to 2020, with a spatial resolution of 1 KM and a temporal resolution of 1 year. Using the year-by-year NDVI data of the countries along the Belt and Road from 2000 to 2020, based on the consideration of year-by-year changes, and through comprehensive diagnosis based on sensitivity and adaptability analysis, the data were prepared The resilience products of vegetation cover status were generated. The data set on the resilience of vegetation cover in countries along the Belt and Road is an important reference for analysing and comparing the current resilience of vegetation cover in each country.
XU Xinliang
Ecosystem productivity resilience along the Belt and Road reflects the level of ecosystem productivity resilience in the countries along the Belt and Road, with higher values indicating stronger ecosystem productivity resilience. The ecosystem productivity resilience data products were prepared with reference to the global medium-resolution vegetation gross primary productivity dataset from 2000 to 2015, with a spatial resolution of 0.05° and a temporal resolution of 1 year, using the year-by-year data of gross primary productivity of vegetation from 2000 to 2015 for the countries along the Belt and Road. The data set was used to generate ecosystem productivity resilience products based on sensitivity and adaptation analyses, using year-by-year data on total primary productivity of vegetation in the countries along the Belt and Road from 2000 to 2015, and a comprehensive diagnosis based on year-by-year changes. "The ecosystem productivity resilience dataset for the countries along the Belt and Road is an important reference for analysing and comparing the current state of ecosystem productivity resilience in each country.
XU Xinliang
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