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Characterizing land use/land cover change dynamics by an enhanced random forest machine learning model: a Google Earth Engine implementation

Abstract Land use and land cover (LULC) analysis is crucial for understanding societal development and assessing changes during the Anthropocene era. Conventional LULC mapping faces challenges in capturing changes under cloud cover and limited ground truth data. To enhance the accuracy and comprehen...

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Autori principali: Chaitanya Baliram Pande, Aman Srivastava, Kanak N. Moharir, Neyara Radwan, Lariyah Mohd Sidek, Fahad Alshehri, Subodh Chandra Pal, Abebe Debele Tolche, Mohamed Zhran
Natura: Artigo
Lingua:Inglês
Pubblicazione: SpringerOpen 2024-04-01
Serie:Environmental Sciences Europe
Soggetti:
LULC
Change detection
GEE
Random forest
Remote sensing
Energy
Accesso online:https://doi.org/10.1186/s12302-024-00901-0
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