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Data Details

Grid Dataset of Global Land Degradation Trend at 500 m (2000-2018)


GAO Zhihai1LI Zengyuan1SUN Bin1DING Xiangyuan1LI Yifu1GAO Ting1QIN Pengyao1WANG Bengyu1YAN Ziyu1
1 Institute of Forest Resource Information Techniques，Chinese Academy of Forestry，Beijing 100101，China

DOI:10.3974/geodb.2019.06.01.V1

Published:Nov. 2019

Visitors：9044       Data Files Downloaded：450      
Data Downloaded：44581.95 MB      Citations：

Key Words：

global,land degradation,trend,500m,GEOARC

Abstract：

The grid dataset of global land degradation trend (500 m) was developed based on integrating the annual net primary productivity (GLASS-NPP), the Moisture-Responded NPP (MNPP) and the Sen and Mann-Mendall (Sen MK) model. The procedure of the data development is that firstly, the Sen and Mann-Mendall (Sen MK) method was used to analyze the trend of NPP change from 2000 to 2018 (a = 0.05); secondly, correlation analysis between MNPP and moisture index (MI) during the same period was conducted (a = 0.05). Finally, the spatial extent of global land degrading and improving areas since 2000 was identified, and the grid dataset of global land degrading and improving areas at 500 m was developed. In the dataset, -1 represents land degrading areas, 1 represents land improving areas, and 0 represents no change. The dataset covers the global terrestrial area excluding the Antarctica, and it is divided into 16 group .tif files (112 data files in total), each file spans 45 longitudes from east to west and 65 latitudes from north to south. The data size of the dataset is 127 MB (Compressed to one single file with 99.0 MB). The dataset was used for the 2019 report on Global Ecosystem and Environment Observation Analysis Research Cooperation (GEOARC) – Trend in Global Land Degradation since 2000.

Foundation Item：

Data Citation：

GAO Zhihai, LI Zengyuan, SUN Bin, DING Xiangyuan, LI Yifu, GAO Ting, QIN Pengyao, WANG Bengyu, YAN Ziyu. Grid Dataset of Global Land Degradation Trend at 500 m (2000-2018)[J/DB/OL]. Digital Journal of Global Change Data Repository, 2019. https://doi.org/10.3974/geodb.2019.06.01.V1.

Data Product:

ID Data Name Data Size Operation
1 GlobalLandDegradTrend500m.rar 101448.69KB
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