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 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 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 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
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 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
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 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 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
Ⅰ. Overview This data set is based on Landsat MSS, TM and ETM Remote sensing data by means of satellite remote sensing. Using a hierarchical land cover classification system, the data divides the whole region into six first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅱ. Data processing description The data set is based on Landsat MSS, TM and ETM Remote sensing data as the base map, the data set projection is set as Alberts equal product projection, the scale is set at 1:24,000 for human-computer interactive visual interpretation, and the data set storage form is ESRI coverage format. Ⅲ. Data content description The data set adopts a hierarchical land cover classification system, which is divided into 6 first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅳ. Data use description The data can be mainly used in national land resources survey, climate change, hydrology and ecological research.
XUE Xian, DU Heqiang
Ⅰ. Overview This data set is based on Landsat MSS, TM and ETM Remote sensing data by means of satellite remote sensing. Using a hierarchical land cover classification system, the data divides the whole region into six first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅱ. Data processing description The data set is based on Landsat MSS, TM and ETM Remote sensing data as the base map, the data set projection is set as Alberts equal product projection, the scale is set at 1:24,000 for human-computer interactive visual interpretation, and the data set storage form is ESRI coverage format. Ⅲ. Data content description The data set adopts a hierarchical land cover classification system, which is divided into 6 first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅳ. Data use description The data can be mainly used in national land resources survey, climate change, hydrology and ecological research.
XUE Xian, DU Heqiang
Ⅰ. Overview This data set is based on Landsat MSS, TM and ETM Remote sensing data by means of satellite remote sensing. Using a hierarchical land cover classification system, the data divides the whole region into six first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅱ. Data processing description The data set is based on Landsat MSS, TM and ETM Remote sensing data as the base map, the data set projection is set as Alberts equal product projection, the scale is set at 1:24,000 for human-computer interactive visual interpretation, and the data set storage form is ESRI coverage format. Ⅲ. Data content description The data set adopts a hierarchical land cover classification system, which is divided into 6 first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅳ. Data use description The data can be mainly used in national land resources survey, climate change, hydrology and ecological research.
XUE Xian, DU Heqiang
Ⅰ. Overview This data set is based on Landsat MSS, TM and ETM Remote sensing data by means of satellite remote sensing. Using a hierarchical land cover classification system, the data divides the whole region into six first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅱ. Data processing description The data set is based on Landsat MSS, TM and ETM Remote sensing data as the base map, the data set projection is set as Alberts equal product projection, the scale is set at 1:24,000 for human-computer interactive visual interpretation, and the data set storage form is ESRI coverage format. Ⅲ. Data content description The data set adopts a hierarchical land cover classification system, which is divided into 6 first-class classifications (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining land, residential land and unused land), and 31 second-class classifications. Ⅳ. Data use description The data can be mainly used in national land resources survey, climate change, hydrology and ecological research.
XUE Xian, DU Heqiang
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, WU Shixin, ZHOU Wancun
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, WU Shixin, ZHOU Wancun
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, WU Shixin, ZHOU Wancun
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
Contact Support
Northwest Institute of Eco-Environment and Resources, CAS
0931-4967287
poles@itpcas.ac.cn
Links
National Tibetan Plateau Data CenterFollow Us
A Big Earth Data Platform for Three Poles © 2018-2020 No.05000491 | All Rights Reserved
｜ 
No.11010502040845
Tech Support: westdc.cn
