Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution

Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution

The article presents results of using remote sensing images and machine learning to map and assess land potential based on time-series of potential Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) composites. Land potential here refers to the potential vegetation productivity in the...

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Bibliographic Details
Main Authors: Julia Hackländer, Leandro Parente, Yu-Feng Ho, Tomislav Hengl, Rolf Simoes, Davide Consoli, Murat Şahin, Xuemeng Tian, Martin Jung, Martin Herold, Gregory Duveiller, Melanie Weynants, Ichsani Wheeler
Format: Article
Language:English
Published: PeerJ Inc. 2024-03-01
Series:PeerJ
Subjects:
Machine learning
Spatiotemporal modeling
Land potential
FAPAR
Natural vegetation
Land degradation
Online Access:https://peerj.com/articles/16972.pdf

Internet

https://peerj.com/articles/16972.pdf

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