Global land projection based on plant functional types with a 1-km resolution under socio-climatic scenarios

Guangzhao Chen; Xia Li; Xiaoping Liu

doi:10.1038/s41597-022-01208-6

Scientific Data (Mar 2022)

Global land projection based on plant functional types with a 1-km resolution under socio-climatic scenarios

Guangzhao Chen,
Xia Li,
Xiaoping Liu

Affiliations

Guangzhao Chen: Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University
Xia Li: Key Lab of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University
Xiaoping Liu: Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University

DOI: https://doi.org/10.1038/s41597-022-01208-6
Journal volume & issue: Vol. 9, no. 1
pp. 1 – 18

Abstract

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Measurement(s) future land cover Technology Type(s) cellular automata • supervised machine learning Factor Type(s) spatial driving factors - socioeconomic (GDP, population, urban centre and road) • spatial driving factors - physical (temperature, precipitation, topography and soil quality)

Published in Scientific Data

ISSN: 2052-4463 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/sdata/

About the journal