The Amazon is home to a vast biodiversity and plays a crucial role in the maintenance and conservation of the planet. The progress of anthropogenic activities in the region is one of the drivers of land cover change. Studying these changes through remote sensing enables the monitoring of degradation and deforestation in the Amazon rainforest. Using bibliometric tools this study analyses the different remote sensing approaches for land use and land cover monitoring in the Amazon. The methodology includes 1) data search and collection, 2) tool selection and data processing, and 3) data and trend analysis. The results indicate that studies on forest loss and biodiversity are trending in the field. Emerging topics focus on new tools for environmental impact assessment and evaluating new computational technologies for satellite data management.

Landslides are a persistent and destructive hazard in Angra dos Reis, located in the highlands of Rio de Janeiro State, southeastern Brazil, where steep slopes, intense orographic rainfall, and unregulated urban expansion converge to trigger recurrent mass movements. In this study, we applied Multiscale Geographically Weighted Regression (MGWR) to examine the spatially varying relationships between landslide occurrence and topographic, hydrological, geological, and anthropogenic factors. A detailed inventory of 319 landslides was compiled using high-resolution PlanetScope imagery after the December 2023 rainfall event. Following multicollinearity testing and variable selection, thirteen predictors were retained, including slope, rainfall, lithology, NDVI, forest loss, and distance to roads. The MGWR achieved strong performance (R² = 0.94; AICc = 134.99; AUC = 0.99) and demonstrated that each factor operates at a distinct spatial scale. Slope, rainfall, and lithology exerted broad-scale controls, while road proximity had a consistent global effect. In contrast, forest loss and land use showed localized significance. These findings indicate that landslide susceptibility in Angra dos Reis is primarily driven by the interaction of orographic rainfall, steep terrain, and geological substrate, intensified by human disturbances such as road infrastructure and vegetation removal. The study underscores the need for targeted adaptation strategies, including slope stabilization, restrictions on road expansion, and vegetation conservation in steep, rainfall-prone sectors.

Coastal cities are increasingly exposed to complex hydroclimatic pressures shaped by changes in both the intensity and the temporal structure of rainfall. This study examines long-term precipitation variability in Caraguatatuba, southeastern Brazil, using daily CHIRPS data (1981–2024) and ETCCDI indices. Results indicate a significant increase in consecutive dry days (CDD), rising from 29 days in 1981 to 39 days in 2017 ( p = 0.0064), reflecting growing drought-related stress. In contrast, the Rx1day index shows a significant decline ( p = 0.0039), with annual maxima decreasing from 141 mm to 84 mm, and the contribution of very wet days (R95p and R99p) also diminishes ( p = 0.0159 and 0.0271). Spatial analyses reveal strong heterogeneity: urbanized eastern districts exhibit declining extreme-rainfall intensities, whereas forested southwestern sectors continue to experience high-magnitude events modulated by orographic forcing. These patterns suggest that vulnerability may increase not through intensifying extremes, but through longer dry spells and greater rainfall irregularity, which can reduce soil moisture and infiltration capacity and heighten susceptibility to runoff and slope instability. This evolving hydroclimatic regime poses emerging challenges for water management and disaster preparedness in rapidly developing coastal environments.

Forest fires in the Amazon, particularly in protected areas (PAs), pose significant challenges related to CO2 emissions and deforestation. This study aimed to evaluate fire foci and their driving factors in the Jamanxim National Forest (FLONA Jamanxim), located in the southeastern Amazon, using Geographic Information Systems (GIS) to inform the decision-making process for environmental management. The methodology involved two phases: (I) processing data on fire foci, land-use and land-cover changes (LULCCs), and climatic variables in the pivotal years 2004, 2007, 2020, and 2022; (II) data analysis using the Mann–Kendall test and Principal Component Analysis (PCA) to understand the trends and relationships between fire and LULC and climate variables. A total of 5,012 fire foci were recorded, with the highest numbers in 2004 and 2007 during neutral and warm phase of the El Niño-Southern Oscillation (ENSO), respectively. Throughout the study period, the months of July to September (dry season months) showed the greatest concentration of fires. LULCC analysis indicated pasture expansion towards FLONA’s center. Spatial fire foci patterns were further analyzed through hotspot mapping, revealing concentration zones near pasture expansion areas. The Mann–Kendall test revealed a significant trend in fire foci over pasture (p = 0.04). PCA showed that fire foci were linked to maximum temperatures, wind speed, and vapor pressure deficit and inversely correlated with soil moisture and evapotranspiration. These findings, derived from geospatial and statistical analyses, highlight that human activities are at the origin of fire foci, underscoring the need for improved management and monitoring of FLONA Jamanxim.

This study aims to evaluate an automated approach for mapping areas affected by land use and land cover change using Sentinel-1 SAR imagery time series and machine learning models trained through a novel pseudo-labeling framework. The proposed framework automatically identifies reliable pseudo-labeled samples for training classifiers and represents an evolution of a previously proposed strategy developed for the same purpose. Different algorithms and attribute configurations were tested to automate the distinction between stable areas and regions subject to spatiotemporal changes. Two case studies were conducted in regions with distinct characteristics, located in Novo Progresso (Brazil) and Istanbul (Turkey), allowing a broad evaluation of the proposed method. The results indicate that the choice of classifier, the configuration of attributes, and regional characteristics influence the mapping performance. In Novo Progresso, the best results were obtained with SVM-Linear (kappa: 0.749; F1-Score: 0.842), followed by RF and RBFNet, while AdaBoost performed the worst. In Istanbul, RBFNet demonstrated higher accuracy (kappa: 0.759; F1-score: 0.852), confirming the effectiveness of the approach in detecting changes across different contexts.