This dataset derives from the article: He, C., Liu, Z., Wu, J., Pan, X., Fang, Z., Li, J., Bryan, B.A. (2021). Future global urban water scarcity and potential solutions. Nature Communications, 12, 4667. https://doi.org/10.1038/s41467-021-25026-3. This dataset includes global urban land information under different shared socioeconomic pathways from 2020 to 2070. The production process of this dataset mainly includes: (1) establishing a linear regression model based on the urban land data (downloaded from https://doi.pangaea.de/10.1594/PANGAEA.892684) and urban population data from 1992 to 2016 to calculate the future urban land demand; (2) using the LUSD-urban model to simulate the spatial distribution of urban land. The dataset can support the assessment of effects of future global urban expansion.
HE Chunyang, LIU Zhifeng, YANG Yanjie
This dataset is derived from the article: Huang, M., Wang, Z.C., Pan, X.H., Gong, B.H., Tu, M.Z., & Liu, Z.F. (2022). Delimiting China's urban growth boundaries under localized shared socioeconomic pathways and various urban expansion modes. Earth's Future, 10, e2021EF002572. The dataset shows the urban expansion and urban growth boundaries of China in 2021-2100 under different socioeconomic scenarios and diverse urban expansion modes. To produce this dataset, the patch-based LUSD-urban model was used to simulate the urban expansion with 11 modes under the localized shared socioeconomic pathways, and the morphology approach was used to delimit urban growth boundaries according to the maximum extent of urban expansion. Using this dataset, the authors quantified the impacts of future urban expansion on ecosystem services under different scenarios and diverse modes, as well as the pressure of urban shrinkage, which is helpful to the Chinese government to demarcate urban development boundaries.
HUANG Miao , WANG Zichen , PAN Xinhao , GONG Binghua , TU Mengzhao , LIU Zhifeng
This dataset derives from the articles: (1) He, C., Liu, Z., Tian, J., & Ma, Q., (2014). Urban expansion dynamics and natural habitat loss in China: a multiscale landscape perspective. Global change biology, 20(9), 2886-2902.（2）Xu, M., He, C., Liu, Z., Dou, Y. (2016). How Did Urban Land Expand in China between 1992 and 2015? A Multi-Scale Landscape Analysis. PLoS ONE 11 (5): e0154839. To produce this dataset, the nighttime light data, vegetation index data, and land surface temperature data were preprocessed to obtain the multi-source remote sensing data in China from 1992 to 2020, and the economic regionalization, selection of samples, support vector machine classification, and inter-annual correction were used to extract the dynamic information of urban built-up area. According to the accuracy assessment based on Landsat TM/ETM+ data, Kappa coefficient is 0.60, overall accuracy is 92.62% This dataset has been used to assess the impacts of urban expansion on natural habitats and cropland, and can provide data support for understanding China’s urban expansion and its effects.
HE Chunyang, LIU Zhifeng, XU Min , LU Wenlu
This data set is a 30m land cover classification product in the Qilian Mountains in 2021. This product is based on the land cover classification product in 2021, based on the Landsat series data and strong geodetic data processing capability of Google Earth engine platform, and is produced by using the ideas and methods of change detection. The overall accuracy is better than 85%. This product is the continuation of land cover classification products from 1985 to 2020. Land cover classification products from 1985 to 2020 can also be downloaded from this website. Among them, the land use products from 1985 to 2015 are five years and one period, and the land use products from 2015 to 2021 are one year and one period.
YANG Aixia, ZHONG Bo, JUE Kunsheng, WU Junjun
This data set includes 30 m cultivated land and construction land distribution products in Qilian Mountain Area in 2021. The product comes from the land cover classification product of 30 m in Qilian Mountain Area in 2021. The overall accuracy of the product is better than 85%.
YANG Aixia, ZHONG Bo
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
Through the investigation of tourist spots, tourist routes and tourist areas at different levels, form photos and video data of tourism resources, tourism services and tourism facilities of scenic spots, scenic spots, corridors and important tourism transportation nodes, tourism villages and tourism towns, record the tourism development status, find problems in tourism development, and form corresponding ideas for the construction of world tourism destinations; The data sources are UAV, tachograph and camera, mobile phone and GPS, and are divided into different folders according to scenic spots and data categories; The data has been checked for many times to ensure its authenticity; This data can provide a traceable basis for the construction of world tourism destinations on the Qinghai Tibet Plateau.
SHI Shanshan
This dataset was captured during the field investigation of the Qinghai-Tibet Plateau in June 2021 using uav aerial photography. The data volume is 3.4 GB and includes more than 330 aerial photographs. The shooting locations mainly include roads, residential areas and their surrounding areas in Lhasa Nyingchi of Tibet, Dali and Nujiang of Yunnan province, Ganzi, Aba and Liangshan of Sichuan Province. These aerial photographs mainly reflect local land use/cover type, the distribution of facility agriculture land, vegetation coverage. Aerial photographs have spatial location information such as longitude, latitude and altitude, which can not only provide basic verification information for land use classification, but also provide reference for remote sensing image inversion of large-scale regional vegetation coverage by calculating vegetation coverage.
LV Changhe, ZHANG Zemin
The data set of land desertification distribution in Sanjiangyuan area is derived from the desertification pattern and change data of Qinghai Tibet Plateau. This data is obtained based on the integration of remote sensing images, auxiliary data and other multi-source data. The main data used and referred to include: 1) remote sensing image data: Landsat was selected to extract the images from June to September as the main data source for land desertification monitoring on the Qinghai Tibet Plateau, and five images were selected to monitor land desertification in 1980, 1990, 2000, 2010 and 2015. 2) auxiliary data: terrain data, soil type data, vegetation type data Land use data, Google Earth image and other auxiliary data are important data in the interpretation of desertification land; 3) The indicators of desertification are wind erosion rate, percentage of quicksand area and vegetation coverage; 4) The area of the source area of the three rivers is 382312 km2. The data set is cut out from the land desertification distribution data of the Qinghai Tibet Plateau, so as to carry out the research and analysis of the source area of the three rivers separately; 5) This data format is ShapeFile format. It is recommended to use ArcMap to open data.
NAN Weige
This data is digitized from the "Yinchuan Land Use Status Map" of the drawing, which is a key scientific and technological research project in the "Seventh Five-Year Plan" of the country: "Three North" Shelter Forest Remote Sensing Comprehensive Survey, one of the series maps of Ganqingning Type Area, with the following information: * Chief Editor: Wang Yimou * Deputy Editors: Feng Yushun, You Xianxiang, Shen Yuancun * Editors: Wang Xian, Wang Jingquan, Qiu Mingxin, Quan Zhijie, Mou Xindai, Qu Chunning, Yao Fafen, Qian Tianjiu, Huang Autonomy, Mei Chengrui, Han Xichun, Li Yujiu, Hu Shuangxi * Responsible Editor: Huang Meihua * Editorial: Feng Yushun and Yao Fafen * Compilation: Yao Fafen, Li Zhenshan, Wang Xizhang, Zhu Che, Ma Bin, Yang Ping * Editors: Feng Yushun and Wang Yimou * Qing Hua: Wang Jianhua, Yao Fafen, Ma Bin, Li Zhenshan * Cartographic unit: compiled by Desert Research Office of Chinese Academy of Sciences * Publishing House: Xi 'an Map Publishing House * Scale: 1: 500000 * Publication time: not yet available 2. File Format and Naming Data is stored in ESRI Shapefile format, including the following layers: Desertification type map (desert), Yinchuan landuse map (landuse), railway, residential _ poly, residential, River, Road, Water_poly 3. Data Fields and Attributes Type number land_type Desert shape Paddy field Paddy field 12 Irrigated field 131 Plain non-irrigated field Valley non-irrigate field Slope non-irrigated field, 133 slope dryland 134 dryland Terrace non-irrigat field 14 Vegetable plot vegetable plot 15 Abandoned farmland Orchard orchard 31 Woodland ......... Specific attribute contents refer to data documents 2. Projection information: Angular Unit: Degree (0.017453292519943295) Prime Meridian: Greenwich (0.000000000000000000) Datum: D_Beijing_1954 Spheroid: Krasovsky_1940 Semimajor Axis: 6378245.000000000000000000 Semiminor Axis: 6356863.018773047300000000 Inverse Flattening: 298.300000000000010000
WANG Jianhua, WANG Yimou, YOU Xianxiang, SHEN Yuancun, FENG Yusun, WANG Xian, YAO Fafen, SHEN Yuancun, FENG Yusun, YAO Fafen
This data is the dunhuang land use status map digitized from the drawings. This map is one of the key scientific and technological research projects of the seventh five-year plan of China: comprehensive remote sensing survey of shelterbelt in the third north, and one of the series maps of the type area of gan qingning. The information is as follows: * chief editor: wang yimou, * deputy chief editor: feng yusun, you xianxiang, shenyuan village *, qing painting: wang jianhua, yao fafen, Yang ping * drawing: feng yu-sun, yao fa-fen, wang jianhua, zhao yanhua, li weimin * cartographic unit: desert laboratory, Chinese academy of sciences * publishing house: xi 'an map publishing house 2. File format and naming The data is stored in ESRI Shapefile format, including the following layers: Dunhuang land use status map, rivers, roads, lakes, railways, residential land, reservoirs, desertification 3. Data fields and properties Type code land resource class (Land_type) 12. Irrigated field 31 Woodland 311 Woodland 312 Joe irrigation mixed forest land (tree-shurb mixed) 321 Shrub land (Shrub) Sparse shrub 33 Sparse woods In winter and spring of 4111 Meadow grassland, Meadow grassland in the spring and winter) 4112 winter and spring of salinization meadow grassland, Saline meadow grassland in the spring and winter) 4112 winter and spring of salinization meadow grassland, Saline meadow grassland in the spring and winter) In winter and spring of 4113 salt meadow grassland (Salty soil meadow grassland in the spring and winter) 4122 gritty desert grassland autumn grass (Gravely desert - steppe grassland in autumn and winter) 4124 mountain desert grassland winter and spring pastures (Mountainous desert - steppe grassland in winter and spring) 4134 four seasons mountain desert grassland, Mountainous desert steppe in four seasons) Sandy desert steppe in autumn and winter Gravely desert steppe in autumn and winter Earthy desert steppe in four seasons Alpine steppe in four seasons 51 Urban and town land 52 Village land 73 Reservoir and pond 74 Reed marshes Tidal flat 81 Desert land 82 Saline-alkali land 83 Marshes 84 Sandy land Sandy flat and dry valley 86 Bare land 87 Gobi Gobi 88 Exposed rock Flat sandy land Compound dunes Undulatory sand-overlying land Dunes and barchan chain The sand ridge (Longitudinal dune) Check dune
WANG Jianhua, WANG Yimou, FENG Yusun, YAO Fafen, YOU Xianxiang, SHEN Yuancun, FENG Yusun, WANG Xian, YAO Fafen, SHEN Yuancun
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
WANG Jianhua, LIU Jiyuan, ZHUANG Dafang, ZHOU Wancun, WU Shixin
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
WANG Jianhua, LIU Jiyuan, ZHUANG Dafang, ZHOU Wancun, WU Shixin
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
WANG Jianhua, LIU Jiyuan, ZHUANG Dafang, ZHOU Wancun, WU Shixin
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, ZHOU Wancun, WU Shixin
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, ZHOU Wancun, WU Shixin
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, ZHOU Wancun, WU Shixin
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, ZHOU Wancun, WU Shixin
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
LIU Jiyuan, ZHUANG Dafang, WANG Jianhua, ZHOU Wancun, WU Shixin
The land cover classification product is the second phase product of the ESA Climate Change Initiative (CCI), with a spatial resolution of 300 meters and a temporal coverage of 1992-2015. The spatial coverage is latitude -90-90 degrees, longitude -180-180 degrees, and the coordinate system is the geographic coordinate WGS84. The classification of the surface coverage is based on the Land Cover Classification System (LCCS) of the Food and Agriculture Organization of the United Nations. When the data are used for scientific research purposes, the ESA CCI Land Cover project should be acknowledged. In addition, the published article should be send to contact@esalandcover-cci.org.
XU Xiyan
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