The data set records the current situation of land use in Qinghai Province. The data is divided by cultivated land, garden land, woodland, grassland, residential land, industrial and mining land, transportation land, water conservancy facilities land and unused land. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 8 data tables Land use status 2002.xls Land use status in 2003.xls Land use status 2004.xls Land use status 2006.xls Land use status 2007.xls Land use status in 2008.xls Land use status in 2009.xls The structure of 2012. XLS data table is the same. For example, there are four fields in the data table of land use status in 2002 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
The data set records the statistical data of land use status in Huangnan Prefecture of Qinghai Province from 2003 to 2012, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Land use status in Huangnan Prefecture, 2003.xls Land use status of Huangnan Prefecture, 2006.xls Land use status in Huangnan Prefecture, 2008 1.xls Land use status in Huangnan Prefecture, 2008 2.xls Land use status of Huangnan Prefecture, 2012.xls Land use status in Huangnan Prefecture, 2004.xls Land use status of Huangnan Prefecture, 2006.xls Land use status of Huangnan Prefecture 2007.xls Current situation of land use in Huangnan Prefecture The data table structure is the same. For example, there are four fields in the data table of land use status in Huangnan Prefecture in 2003 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
The data set records the statistical data of land use status in Haixi Prefecture of Qinghai Province from 2003 to 2007, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 8 data tables Land use status of Haixi Prefecture, 2003.xls Land use status of Haixi Prefecture 2007.xls Land use status of Haixi Prefecture, 2008.xls Land use status of Haixi Prefecture in 2008 Land use status of Haixi Prefecture, 2012.xls Land use status of Haixi Prefecture, 2006.xls Land use status of Haixi Prefecture 2007.xls Land use status of Haixi Prefecture, 2004.xls The data table structure is the same. For example, there are four fields in the data table of land use status in Haixi Prefecture in 2003 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
This data set records the statistical data of land use status in Hainan prefecture of Qinghai Province from 2003 to 2007, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Land use status in Hainan Province, 2003.xls Land use status in Hainan Province, 2006-2007.xls Land use status in Hainan Province, 2008 1.xls Land use status in Hainan Province, 2008 2.xls Land use status in Hainan, 2012.xls Land use status in Hainan Province, 2004.xls Land use status in Hainan, 2006.xls Land use status in Hainan, 2007.xls Land use status in Hainan Province The data table structure is the same. For example, there are four fields in the data table of land use status in Hainan in 2003 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
This data set records the statistical data of land use status in Haidong area of Qinghai Province from 2003 to 2012, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Land use status in Haidong region, 2003.xls Land use status in Haidong area 2007.xls Land use status in Haidong region, 2008 1.xls Land use status in Haidong region, 2008 2.xls Land use status in Haidong region, 2004.xls Land use status in Haidong region, 2006.xls Land use status in Haidong area 2007.xls Land use status in Haidong region, 2008 3.xls Land use status of Haidong City, 2012.xls The data table structure is the same. For example, there are four fields in the 2003 data table of land use status in Haidong region Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
This data set records the statistical data of land use status in Haibei Prefecture of Qinghai Province from 2003 to 2007, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Land use status of Haibei Prefecture, 2003.xls Land use status of Haibei Prefecture 2006 2007.xls Land use status of Haibei Prefecture, 2008.xls Land use status of Haibei Prefecture 2008.xls Land use status of Haibei Prefecture, 2012.xls Land use status of Haibei Prefecture, 2004.xls Land use status of Haibei Prefecture, 2006.xls Land use status of Haibei Prefecture 2007.xls Land use status of Haibei Prefecture, 2008.xls The data table structure is the same. For example, there are four fields in the data table of land use status in Haibei Prefecture in 2003 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
This data set records the statistical data of land use status in Guoluo Prefecture of Qinghai Province from 2003 to 2007, which is divided by industry, region, affiliation and registration type. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set consists of 9 data tables Present situation of land use in Guoluo Prefecture, 2003.xls Current situation of land use in Guoluo Prefecture, 2006-2007.xls Current situation of land use in Guoluo Prefecture, 2008.xls Present situation of land use in Guoluo Prefecture, 2008.xls Current situation of land use in Guoluo Prefecture, 2012.xls Present situation of land use in Guoluo Prefecture, 2004.xls Current situation of land use in Guoluo Prefecture, 2006.xls Present situation of land use in Guoluo Prefecture, 2007.xls Current situation of land use in Guoluo Prefecture, 2008.xls The data table structure is the same. For example, there are four fields in the data table of land use status in Luozhou in 2003 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
The data set records the land use status of Yushu prefecture in Qinghai Province from 2003 to 2012, and the data is divided by year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains eight data tables, each of which has the same structure. For example, there are four fields in the data table from 1978 to 2004 Field 1: area at the beginning of the year Field 2: area reduced during the year Field 3: area increased during the year Field 4: year end area
Qinghai Provincial Bureau of Statistics
The data set records the land use status of Xining City in Qinghai Province from 2003 to 2012, and the data is divided by year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains 9 data tables with the same structure. For example, the data table in 2003 has four fields: Field 1: area at the beginning of the year Field 2: area reduced during the year Add field area within 3 years Field 4: year end area
Qinghai Provincial Bureau of Statistics
According to the distribution of cultivated land in 18 districts and counties in the "One River and Two Tributaries" region of Tibet Autonomous Region, a 5km × 5km grid was adopted, covering all cultivated land and greenhouse land. A total of 1092 5km × 5km grids were set up, and each grid contains a number. Data processing method: the fishnet tool in ArcGIS 10.3 is used to generate the grid covering the administrative boundaries of 18 districts and counties in the "one river, two rivers" region of Tibet Autonomous Region, and then the intersect tool is used to generate the grid covering cultivated land. The data can be used to collect soil samples of cultivated land in "One River and Two Tributaries" area of Tibet Autonomous Region.
WANG Zhaofeng, GONG Dianqing
The data of greenhouse land is based on Google Earth image interpretation in Lhasa city, 2018, with a spatial resolution of 0.52 meters. Most of the greenhouses in Lhasa are regular rectangles with high reflectivity, which is easy to identify. In the process of interpretation, the open fields with an area of more than 0.10 hectares and roads with a width of more than 7 meters in the greenhouse area of protected agriculture, as well as the greenhouse covered with black textile were removed, while the small empty fields and ridges between the farmland of protected agriculture were not removed. The accuracy of interpretation is 98%. The data well reflects the spatial pattern characteristics of greenhouse land in Lhasa city.
WANG Zhaofeng, GONG Dianqing
This dataset contains land cover products in Qilian Mountain Area from 1985 to 2017 every 5 years. The dataset was produced by two steps. Firstly, land cover product in 2015 is produced using time series Landsat-8/OLI data. In view of the different NDVI time series curves of various land features with time variation, the knowledge of different land features is summarized, the extraction rules of different land features are set, and the land cover classification map in 2015 is obtained. The classification system refers to IGBP and FROM_LC classification system. It is divided into 10 categories: cultivated land, woodland, grassland, shrub, wetland, water body, impermeable surface, bare land, glacier and snow cover. According to the accuracy evaluation of Google Earth high-definition image and field survey data, the overall accuracy of land cover classification products in 2015 is as high as 92.19%. Secondly, taking the land cover classification products in 2015 as the base map, a large number of samples are selected according to the proportion of different types. Based on the Landsat series data and powerful data processing ability of Google Earth Engine platform, the random forest classifier is selected to train the band information and NDVI, MNDWI, NDBI and other indices by using the idea of in-depth learning. The land of each five-year period from 1985 to 2017 is produced. By comparing two classified products in 2015, it is concluded that the land cover classified products based on Google Earth Engine platform have good consistency with those based on time series method. In conclusion, the land cover data set in the core area of Qilian Mountains has high overall accuracy , and the method based on sample training of Google Earth Engine platform can expand the existing classification products in time and space, and the frequency of every five years can reflect more land cover type change information in long time series.
YANG Aixia, ZHONG Bo, JUE Kunsheng
This data set is the land use data of the key areas of Qilian mountain in 2018, spatial resolution 2m. This data set is based on the data of climate, altitude, topography, and land cover type of the Qilian mountain. Through the high-resolution remote sensing images to interprets the surface cover types. For the land types that cannot be reflected by the images, collect relevant data in the field, check and correct the land use types. At the same time, the maps and attribute information are uniformly entered and edited to form land use data in the Qilian Mountain area in 2018.
WANG Hongwei, QI Yuan, ZHANG Jinlong, YAN Changzhen, DUAN Hanchen, JIA Yongjuan
The dataset is the land cover of Qing-Tibet Plateau in 2014. The data format is a TIFF file, spatial resolution is 300 meters, including crop land, grassland, forest land, urban land, and so on. The dataset offers a geographic fundation for studying the interaction between urbanization and ecological reservation of Qing-Tibet Plateau. This land cover data is a product of CCI-LC project conducted by European Space Agency. The coordinate reference system of the dataset is a geographic coordinate system based on the World Geodetic System 84 reference ellipsoid. There are 22 major classes of land covers. The data were generated using multiple satellite data sources, including MERIS FR/RR, AVHRR, SPOT-VGT, PROBA-V. Validation analysis shows the overall accuracy of the dataset is more than 70%, but it varies with locations and land cover types.
DU Yunyan
This dataset is the spatial distribution map of the marshes in the source region of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30 m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.
WANG Guangjun
Based on 2015 ESA global land cover data (ESA GlobCover, 300 m grid), combined with the Tsinghua university global land cover data (FROM GLC, 30 m grid), NASA MODIS global land cover data (MCD12Q1, 300 m grid), the United States Geological Survey (USGS global land data (GFSAD30, 30 m), Japanese global forest data (PALSAR/PALSAR - 2, 25 m), we build the LUCC classification system in the Belt and Road's region and the rest of the data transformation rules of the classification system. We also build the land cover classification confidence function and the rules of fusing land classification to finish the integration and modification of land cover products and finally completed the land use data in the Belt and Road's region V1.0 (64 + 1 countries, 2015, 1 km x 1 km grid, the first level classification).
XU Erqi
Land cover dataset of MODIS is a product that describes the types of land cover based on the data obtained from Terra and Aqua observations for one year. The land cover dataset contains 17 major land cover types, including 11 natural vegetation types, 3 land development and mosaic types and 3 non-vegetation land types according to the International Geosphere Biosphere Project (IGBP). MCD12Q1 adopts five different land cover classification schemes. The main technology of information extraction is supervised decision tree classification. Based on MCD12Q1 data from 2001 to 2016, MatLab was used to tailor the masks of 18 key nodes in Southeast Asia and middle East. Finally, the annual land cover dataset of 18 key nodes in Southeast Asia from 2001 to 2016 was obtained.
GE Yong, LING Feng, ZHANG Yihang
The MODIS Terra MOD09A1 Version 6 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 m reflectance bands is a quality layer and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used.Based on MCD12Q1 data from 2001 to 2016, MatLab was used to tailor the masks of 18 key nodes in Southeast Asia and middle East. Finally. This dataset is based on the data of MOD09A1 V6 synthesized in 8 days from 2001 to 2016 downloaded by the National Aeronautics and Space Administration (NASA). The spatial resolution is 500 meters, and MatLab is used to mask cut the data in the research area, and Finally, the land cover data of 18 key nodes from 2001 to 2016 were obtained.. The 18 key regions covered by the data mainly include: Bangkok, Port of Myanmar, Chittagong, Colombo, Dhaka, Gwadar, Hambantot, Huangjing and Malacca, Kwantan, Maldives, Mandalay, Sihanouk, Vientiane, Yangon, etc.).
LI Xinyan
This data comes from "China's 1:100000 land use data". China's 1:100000 land use data is constructed in three years based on LANDSAT MSS, TM and ETM Remote sensing data by means of satellite remote sensing, organized by 19 research institutes affiliated to the Chinese Academy of Sciences under the national macro survey and dynamic research on remote sensing of resources and environment, a major application project of the eighth five year plan of the Chinese Academy of Sciences. Using a hierarchical land cover classification system, this data divides the whole country into six first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class categories. This is the most accurate land use data product in China, which has played an important role in the national land resource survey, hydrological and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, ZHOU Wancun, WU Shixin
The data set contains land cover data sets from the Yellow River Source, the Yangtze River Source, and the Lancang River from 1992 to 2015. A total of 22 land cover classifications based on the UN Land Cover Classification System were included. NOAA AVHRR, SPOT, ENVISAT, PROBA-V and other vegetation classification products were integrated. In China, (1) first, combined with the 1:100,000 vegetation classification (2007) of China, quality correction and control were performed, and (2) the vegetation classification of China emphasized the combination with climate zones, when correcting CCI-LC, climate divisions and the corresponding vegetation types were combined, and the data label was comprehensively revised.
WEI Yanqiang, WEI Yanqiang
