This dataset includes the maximum normalized vegetation index (NDVI) data from 1982 to 2015, the maximum enhanced vegetation index (EVI) data from 2000 to 2020, and the land cover change (LUCC) data from 2001 to 2019 in the China-Mongolia-Russia Economic Corridor (CMREC). Among these, NDVI data was extracted from GIMMS satellite data with a resolution of 8 km; EVI and LUCC data were extracted from MODIS satellite data (MOD13A3 and MCD12C1) with a resolution of 1 km and 5 km, respectively. The dataset filters the outliers or missing values in the original data, which is of higher quality than the source data. Meanwhile, we adopted the maximum value composite (MVC) method to process NDVI and EVI data to obtain the annual maximum NDVI and EVI, which can better reflect the vegetation distribution and change in CMREC over the past several decades. The spatio-temporal changes of vegetation and land use extracted from satellite remote sensing data will provide scientifical guidance for the risk control and prevention of the ecological environment change in CMREC.
ZHANG Xueqin
This data is the land cover data at 30m resolution of Southeast Asia in 2015. The data format of the data is NetCDF, and the variable name is "land cover type". The data was obtained by mosaicing and extracting the From-GLC data. Several land cover types, such as snow and ice that do not exist in Southeast Asia were eliminated.The legend were reintegrated to match the new data. The data provide information of 8 land cover types: cropland, forest, grassland, shrub, wetland, water, city and bare land. The overall accuracy of the data is 71% (Gong et al., 2019). The data can provide the land cover information of Southeast Asia for hydrological models and regional climate models.
LIU Junguo
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