## 1 Introduction  
Deforestation and forest degradation in tropical regions, particularly in the Amazon rainforest, represent critical environmental challenges with far-reaching implications. The Amazon, the world's largest tropical rainforest, serves as a crucial carbon storage unit and a hotspot for biodiversity, playing an essential role in regulating the planet's climate and providing habitat for numerous species [1]. However, this vital ecosystem faces unprecedented threats driven by complex socio-economic factors and land use changes [2, 3]. Over the past few decades, the Brazilian Amazon has experienced alarming rates of forest loss [4]. This deforestation severely threatens the forest and is primarily driven by agricultural expansion [5], cattle ranching [6], infrastructure development [7], and illegal logging [8]. Forest degradation is equally serious because it frequently precedes total deforestation. Degraded forests are much more susceptible to further deforestation, with typical decreases in canopy height and aboveground biomass of up to 60% and 65%, respectively, according to recent research conducted in tropical moist forests [9]. Understanding the dynamics of land use and land cover (LULC) changes is essential for developing effective conservation strategies and sustainable land management practices. The Brazilian Amazon has experienced varying rates of change across its vast expanse [4, 10]. Thus, this heterogeneity presents both challenges and opportunities for researchers and policymakers seeking to understand and mitigate the impacts of land use changes. The advent of satellite imagery and advanced classification techniques has revolutionized our ability to monitor and analyze LULC changes at various spatial and temporal scales [11]. Remote sensing has emerged as a crucial tool for monitoring deforestation and forest degradation in the Amazon. Satellite imagery provides consistent, large-scale observations that enable researchers to track changes in forest cover over time [12]. The advent of medium and high-resolution satellite imagery has revolutionized our ability to monitor and analyze LULC changes at various spatial and temporal scales [13]. While lower resolution imagery (e.g., 30 m Landsat, 250-1000 m MODIS) offers advantages in terms of broader coverage and long-term historical records, it may overlook fine-scale disturbances. Medium-resolution imagery, such as Sentinel-2 with its 10 m bands, shows a balance between coverage and resolution, allowing for the detection of smaller-scale disturbances that might be missed by coarser-resolution sensors [14]. These advancements in spatial resolution allow for more accurate detection of subtle changes in forest structure and composition, which is critical for understanding degradation processes [15]. To monitor LULC change and deforestation in the Amazon, pixel-based methods have been traditionally the primary approach [16]. For instance, the Brazilian National Institute for Space Research (INPE) uses a pixel-based approach in its PRODES (Monitoring Deforestation in the Brazilian Amazon by Satellite Project) system, which has been the official method for monitoring deforestation in the Brazilian Amazon since 1988 [17]. This monitoring system utilizes Landsat satellite imagery to provide annual assessments of deforestation, offering information with a minimum mapping unit of 6.25 ha. However, recent advancements in remote sensing technology and analysis techniques have opened new possibilities for more accurate and detailed monitoring. Object-based image analysis (OBIA) has proven particularly advantageous for detecting LULC changes. Unlike pixel-based methods, OBIA considers the spatial context of image objects, allowing for a more nuanced classification of complex landscapes [18]. When combined with satellite imagery from Sentinel-2 and machine learning algorithms like Random Forest, OBIA can achieve high classification accuracies and provide more detailed information about forest structure and composition.