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Ecological Vulnerability Classification 1-km Raster Dataset in Qinghai Province of China (2015)


ZHI Zemin1LIU Fenggui*1,2CHEN Qiong1,2ZHOU Qiang1,2
1 School of Geographic Science,Qinghai Normal University,Xining 810008,China2 Academy of plateau Science and Sustainability,Xining 810008,China

DOI:10.3974/geodb.2022.01.04.V1

Published:Jan. 2022

Visitors:2696       Data Files Downloaded:75      
Data Downloaded:12.15 MB      Citations:

Key Words:

Qinghai Province,Ecological Vulnerability,Random Forest,SPR Model

Abstract:

Qinghai Province is located in the alpine region, and its ecosystem is very fragile. The Ecological Vulnerability Classification 1-km Raster Dataset in Qinghai Province of China (2015) was developed based on the data in 2015 and the "SPR" (sensitivity-pressure-resilience) model and stochastic forest model. The data items include slope, elevation, topographic relief, soil erosion sensitivity, land degradation sensitivity, NDVI, average annual temperature, average annual precipitation, population density, etc. Five groups of vulnerability were identified, they were slight, mild, moderate, severe, and extreme. The spatial resolution is 1 km. The dataset is archived in .tif format; it consists of five data files with data size of 1.30 MB (Compressed into one file with 165 KB). The analysis paper was published at the Journal of Qinghai Environment, Vol. 30, No. 4, 2020.Browse

Foundation Item:

Ministry of Science and Technology of P. R. China (2019YFA0606902)

Data Citation:

ZHI Zemin, LIU Fenggui*, CHEN Qiong, ZHOU Qiang.Ecological Vulnerability Classification 1-km Raster Dataset in Qinghai Province of China (2015)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2022. https://doi.org/10.3974/geodb.2022.01.04.V1.

ZHI Zemin, LIU Fenggui, CHEN Qiong, et al. Development of ecological vulnerability classification 1-km raster dataset in Qinghai province of China (2015) [J]. Journal of Global Change Data & Discovery, 2022, 6(3): 409-415.

References:

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     [10] Li, Z. Z., Nawaf r, A., Guang, C. L., et al. Random Forests [J]. Tsinghua Science and Technology, 2018, 23(4): 479-492.
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Data Product:

ID Data Name Data Size Operation
0Datapaper_EcoVul.Qinghai_2015.pdf726.00kbDownLoad
1 EcoVul.Qinghai_2015.rar 165.94KB
Co-Sponsors

Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences

The Geographical Society of China

Parteners

Committee on Data for Science and Technology (CODATA) Task Group on Preservation of and Access to Scientific and Technical Data in/for/with Developing Countries (PASTD)

Jomo Kenyatta University of Agriculture and Technology

Digital Linchao GeoMuseum