Machine Learning and Multi-source Remote Sensing in Forest Aboveground Biomass Estimation: A Review
Year: 2024
Type: preprint
Study design: Systematic Review
Abstract: Quantifying forest aboveground biomass (AGB) is crucial for informing decisions and policies that will protect the planet. Machine learning (ML) and remote sensing (RS) techniques have been used to do this task more effectively, yet there lacks a systematic review on the most recent working combinations of ML methods and multiple RS sources, especially with the consideration of the forests' ecological characteristics. This study systematically analyzed 25 papers that met strict inclusion criteria from over 80 related studies, identifying all ML methods and combinations of RS data used. Random Forest had the most frequent appearance (88\% of studies), while Extreme Gradient Boosting showed superior performance in 75\% of the studies in which it was compared with other methods. Sentinel-1 emerged as the most utilized remote sensing source, with multi-sensor approaches (e.g., Sentinel-1, Sentinel-2, and LiDAR) proving especially effective. Our findings provide grounds for recommending which sensing sources, variables, and methods to consider using when integrating ML and RS for forest AGB estimation.
Source: arXiv (Cornell University)
Authors: Nguyen, Autumn, Saha, Sulagna
Institution: Mount Holyoke College
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Life on land
Open Access status: green
OpenAlex ID: W4404988987