Leveraging SAR and Optical Remote Sensing for Enhanced Biomass Estimation in the Amazon with Random Forest and XGBoost Models

Rodrigo Antunes*, Luiz Junior*, Gilson Costa*, Raul Feitosa*, Edilson de Souza Bias*, Abimael Cereda Junior*, Catherine Almeida*, Laura E. Cué La Rosa*, Patrick Happ*, Leonardo Chiamulera*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

This study addresses the challenge of estimating above-ground biomass (AGB) in the Amazon rainforest by developing a reference geographical database, which provides the ground truth, and comparing the relative importance of using Synthetic Aperture Radar (SAR) and optical remote sensing data to automatically infer AGB. In the experiments reported in this article, we assessed how those two remote sensing data sources impact the accuracy of AGB estimates produced by regression models built with Random Forest (RF) and Extreme Gradient Boosting (XGBoost). The research involved compiling a comprehensive database from many available forest inventories, integrating parcel- and tree-level data to enable precise biomass estimation. The methodology included setting up a spatial data analysis environment, standardizing data, and implementing an experimental protocol with feature selection and leave-one-out cross-validation. The results demonstrate that both kinds of data, i.e., SAR and optical, and their combination can be used for estimating AGB, providing valuable insights for forest management and climate change mitigation efforts. The reference database is available upon request to the corresponding authors.

Original languageEnglish
Pages (from-to)21-27
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume10
Issue number3
DOIs
Publication statusPublished - 4 Nov 2024
Event2024 Symposium on Beyond the Canopy: Technologies and Applications of Remote Sensing - Belem, Brazil
Duration: 4 Nov 20248 Nov 2024

Keywords

  • Aboveground Biomass
  • Forest Inventory
  • Geodatabase
  • Machine Learning
  • Remote Sensing

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