This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This data set includes 30 m farmland and construction land distribution products in Qilian Mountains from 1985 to 2017. The product is based on Landsat series data production. In the production of farmland products, the Normalized Difference Vegetation Index (NDVI) is used, and the production rules are set by using the prior knowledge of crop phenology and planting types. In the production of buildingland products, the normalized NDVI is used by using the Landsat series data synthesized in summer. other indexs like NDBI, MNDWI and other data like DEM, lighting data are used together to set product production rules. Taking the products of 2015 as an example, the accuracy of the products of farmland andbuildingland in 2015 is 89.43% and 91.89% respectively, based on the high-definition image of Google Earth and field survey data.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This data set includes 30 m farmland and buildingland distribution products in Qilian Mountains from 1985 to 2017. The product is based on Landsat series data production. In the production of farmland products, the Normalized Difference Vegetation Index (NDVI) is used, and the production rules are set by using the prior knowledge of crop phenology and planting types. In the production of buildingland products, the normalized NDVI is used by using the Landsat series data synthesized in summer. other indexs like NDBI, MNDWI and other data like DEM, lighting data are used together to set product production rules. Taking the products of 2015 as an example, the accuracy of the products of farmland andbuildingland in 2015 is 89.43% and 91.89% respectively, based on the high-definition image of Google Earth and field survey data.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This dataset contains cultivated land and impermeable surface products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset came from land cover products in Qilian Mountain Area.
ZHONG Bo, JUE Kunsheng
This data set includes 30 m farmland and bulidingland distribution products in Qilian Mountains in 2018. The product is based on Landsat-8/OLI data. In the production of farmland products, the Normalized Difference Vegetation Index (NDVI) is used, and the production rules are set by using the prior knowledge of crop phenology and planting types. In the production of buildingland products, the normalized NDVI is used by using the Landsat series data synthesized in summer. other indexs like NDBI, MNDWI and other data like DEM, lighting data are used together to set product production rules. The accuracy of the products of farmland andbuildingland in 2018 is 90.05% and 90.97% respectively, based on the high-definition image of Google Earth and field survey data.
ZHONG Bo, JUE Kunsheng
This dataset is the data of human activities in the key areas of Qilian Mountain in 2018, spatial resolution 2m. This dataset focuses on mine mining, urban expansion, cultivated land development, hydropower construction, and tourism development in the key areas of Qilian Mountain.Through high-resolution remote sensing images, compare the changes before and after the statistics. For the maps of the landforms in the Qilian Mountains, check and verify them one by one; re-interpret the plots that are suspicious of the map; collect the relevant data in the field that cannot be reflected by the images, check and correct the location. At the same time, unified input and editing of map attribute information. Generating a data set of human activities in the key areas of the Qilian Mountains in 2018.
QI Yuan, ZHANG Jinlong, JIA Yongjuan, ZHOU Shengming, WANG Hongwei
The data include the night light data of Tibetan Plateau with a spatial resolution of 1km*1km, a temporal resolution of 5 years and a time coverage of 2000, 2005 and 2010.The data came from Version 4 dmsp-ols products. DMSP/OLS sensors took a unique approach to collect radiation signals generated by night lights and firelight.DMSP/OLS sensors, working at night, can detect low-intensity lights emitted by urban lights, even small-scale residential areas and traffic flows, and distinguish them from dark rural backgrounds.Therefore, DMSP/OLS nighttime light images can be used as a representation of human activities and become a good data source for human activity monitoring and research.
FANG Huajun
China's land cover data set includes 5 products: 1) glc2000_lucc_1km_China.asc, a Chinese subset of global land cover data based on SPOT4 remote sensing data developed by the GLC2000 project. The data name is GLC2000.GLC2000 China's regional land cover data is directly cropped from global cover data. For data description, please refer to http : //www-gvm.jrc.it/glc2000/defaultGLC2000.htm 2) igbp_lucc_1km_China.asc, a Chinese subset of global land cover data based on AVHRR remote sensing data supported by IGBP-DIS, the data name is IGBPDIS; IGBPDIS data was prepared using the USGS method, using April 1992 to March 1992 The AVHRR data developed global land cover data with a resolution of 1km. The classification system adopts a classification system developed by IGBP, which divides the world into 17 categories. Its development is based on continents. Applying AVHRR for 12 months to maximize synthetic NDVI data, 3) modis_lucc_1km_China_2001.asc, a subset of MODIS land cover data products in China, the data name is MODIS; MODIS China's regional land cover data is directly cropped from global cover data, and its data description please refer to http://edcdaac.usgs.gov/ modis / mod12q1v4.asp. 4. umd_lucc_1km_China.asc, a Chinese subset of global land cover data based on AVHRR data produced by the University of Maryland, the data name is UMd; the five bands of UMd based on AVHRR data and NDVI data are recombined to suggest a data matrix, using Methodology carried out global land cover classification. The goal is to create data that is more accurate than past data. The classification system largely adopts the classification scheme of IGBP. 5) westdc_lucc_1km_China.asc, China ’s 2000: 100,000 land cover data organized and implemented by the Chinese Academy of Sciences, combined with Yazashi conversion (the largest area method), and finally obtained a land use data product of 1km across the country, data name WESTDC. WESTDC China's regional land cover data is based on the results of a 1: 100,000 county-level land resource survey conducted by the Chinese Academy of Sciences. The land use data were merged and converted into a vector (the largest area method). The Chinese Academy of Sciences resource and environment classification system is adopted. 2: Data format: ArcView GIS ASCII 3: Mesh parameters: ncols 4857 nrows 4045 xllcorner -2650000 yllcorner 1876946 cellsize 1000 NODATA_value -9999 4: Projection parameters: Projection ALBERS Units METERS Spheroid Krasovsky Parameters: 25 00 0.000 / * 1st standard parallel 47 00 0.000 / * 2nd standard parallel 105 00 0.000 / * central meridian 0 0 0.000 / * latitude of projection's origin 0.00000 / * false easting (meters) 0.00000 / * false northing (meters)
RAN Youhua
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