ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Citation
Articles | Volume X-3-2024
https://doi.org/10.5194/isprs-annals-X-3-2024-193-2024
https://doi.org/10.5194/isprs-annals-X-3-2024-193-2024
04 Nov 2024
 | 04 Nov 2024

XGBOOST and Multitemporal DETER Data for Deforestation Forecasting

Laura E. Cué La Rosa, Felipe Ferrari, Francisco Gilney Silva Bezerra, Rodrigo Antônio de Souza, Raian Vargas Maretto, Ana Paula Dutra de Aguiar, Lubia Vinhas, Patrick Nigri Happ, and Raul Queiroz Feitosa

Keywords: Deforestation Forecast, Amazon Rainforest, XGBOOST, Multitemporal Analysis

Abstract. This paper reports research that is part of a project to combat deforestation in the Brazilian Amazon rainforest by developing an online system designed to forecast deforestation risk over the short term, spanning 2 to 4 weeks. This online platform aims to empower stakeholders with timely data, facilitating proactive conservation and intervention strategies to safeguard the Amazon rainforest. We built a multitemporal database that compiles weekly deforestation alerts from the DETER project, forming our analysis’s backbone. Utilizing the XGBOOST regression algorithm, we have crafted a predictive model that identifies areas within the Amazon at imminent risk of more intensive deforestation. Preliminary results reveal an RMSE of 0.29 for predicting areas under deforestation risk, as validated against early alert data from 2020 to 2023. Our work advances environmental monitoring by focusing on a spatial resolution of 25 km × 25 km, providing accessible, near real-time information on deforestation risks.