Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud

Predicting forest above-ground biomass using SAR imagery and GEDI data through machine learning in GEE cloud

The estimation of Forest above-ground biomass (AGB) is critical for comprehending forest ecosystems and promoting biodiversity restoration. The study was conducted to develop an effective approach to predict Forest Above-Ground Biomass (AGB) using Machine Learning, Image Classification, and GEE open...

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Bibliographic Details
Main Authors: Chiranjit Singha, Kishore Chandra Swain, Satiprasad Sahoo, Ayad M. Fadhil Al-Quraishi, Joseph Omeiza Alao, Hussein Almohamad, Mohamed Fatahalla Mohamed Ahmed, Hazem Ghassan Abdo
Format: Article
Language:English
Published: Taylor & Francis Group 2025-04-01
Series:Forest Science and Technology
Subjects:
Forest above-ground biomass
biosphere
machine learning
sentinel imagery
environment
Online Access:https://www.tandfonline.com/doi/10.1080/21580103.2025.2481122
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