Monitoring annual Landsat-based deforestation using LSMM and MODIS-burned area product in Rondônia, Brazilian Amazon

Deforestation and fire play a critical role in shaping the Brazilian Amazon, yet accurate automated long-term monitoring remains a challenge. This article presents a method for monitoring deforestation (1980–2022) and burned areas (2001–2022) in the state of Rondônia, a deforestation hotspot region in the Brazilian Amazon. We applied the Linear Spectral Mixing Model (LSMM) to Landsat (MSS, TM, and OLI) datasets to derive vegetation, soil, and shade fraction images, effectively reducing data volume while highlighting relevant target features. A threshold-based classification method was then applied to produce annual classification maps, showing forest, non-forest, deforestation, and water bodies. Burned areas were obtained from the MODIS MCD64A1 product. Our deforestation estimates (2008–2022) showed strong agreement with PRODES (Project for Monitoring Deforestation in the Legal Amazon) and GFC (Global Forest Change), achieving an overall accuracy of 89% compared to PRODES. The results reveal a significant decline in forest cover: from 86% in 1986 to 70% in 2000, and just 52% in 2022. Considering the use of fire during the deforestation process, fire activity was also extensive, with approximately 4.5 million hectares burned during the study period—2.9 million hectares within forested areas, of which only 894 thousand hectares remained as forest after burning. These findings provide critical insights into land-use dynamics and fire-related forest loss in the Amazon, offering valuable support for conservation policies and climate change mitigation strategies.