The uncontrolled expansion of mining activities has caused severe environmental impacts in semi-arid regions, endangering fragile ecosystems and water resources. This study aimed to propose a decision-making model to identify land use and land cover changes in the semi-arid region of Pernambuco, Brazil, caused by mining through a spatiotemporal analysis using high-resolution images from the PlanetScope satellite constellation. The methodology consisted of monitoring and evaluating environmental impacts using the k-Nearest Neighbors (kNN) algorithm, spectral indices (Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI)), and hydrological data, covering the period from 2018 to 2023. As a result, a 3.28% reduction in vegetated areas and a 6.62% increase in urban areas were identified over five years, suggesting landscape transformation, possibly influenced by the expansion of mining and development activities. The application of kNN yielded an Overall Accuracy (OA) greater than 99% and a Kappa index of 0.98, demonstrating the effectiveness of the adopted methodology. However, challenges were encountered in distinguishing between constructions and bare soil, with the Jeffries–Matusita distance (JMD) analysis indicating a value below 0.34, while the similarity between water and vegetation highlights the need for more comprehensive training data. The results indicated that between 2018 and 2023, there was a marked degradation of vegetation and a significant increase in built-up areas, especially near water bodies. This trend reflects the intense human intervention in the region and reinforces the need for public policies aimed at mitigating these impacts, as well as promoting environmental recovery in the affected areas. This approach proves the potential of remote sensing and machine learning techniques to effectively monitor environmental changes, reinforcing strategies for sustainable management in mining areas.

Riparian forests are forests associated with streams, rivers and lakes that have an influence on the protection of ecosystems from man-made actions. The objective of this study was to identify changes in the Caatinga riparian forest areas, based on the spectral sensitivity of vegetation indices (IVs): GNDVI, NDVI and EVI, considering a temporal space analysis (1985 to 2019) in the surroundings of the Serrinha II Reservoir - PE - Brazil. For this purpose, images from sensors/satellites TM - Landsat 5, OLI - Landsat 8 and MSI - Sentinel 2 were used. The IVs were validated comparing spectral profiles and correlation of the products generated by Landsat 8 and Sentinel 2. Through the binary classification of the indices, changes in vegetation cover were identified between 1996 and 2019, resulting in a loss of 55.40% of vegetation. These events, associated with the effects of the drought were also observed for the reservoir, between 2012 and 2019, with 32.36% reduction in the water mirror. Among the IVs, the GNDVI and NDVI showed greater sensitivity to identify the riparian forest with a Pearson coefficient equal to 0.93 for Landsat 8; 0.94 for Sentinel 2 and an overall accuracy of over 95%. On the other hand, EVI was more sensitive to identify the reservoir area, with overall accuracy of 99.48%. The monitoring of artificial reservoirs and riparian forests of seasonally dry forests such as the Caatinga benefit from the use of IVs where the choice of a given isolated IV should be interpreted with caution according to the characteristics of the region studied.

Urban flooding has intensified due to climate change, particularly in rapidly expanding metropolitan regions. This study presents an integrated remote sensing (RS) approach to monitor flood-prone areas in the Porto Alegre Metropolitan Region (Brazil). The methodology combines predictive topographic modeling using the HAND model with post-flood analysis based on water spectral indices. Flood susceptibility was assessed using HAND derived from SRTM data, while flood extent was estimated with NDWI and WNDWI applied to Landsat imagery and validated against HEC-RAS hydrodynamic simulations for the 2015 and 2024 events. HAND effectively identified low-lying, high-risk areas near watercourses but showed limitations in densely urbanized zones. WNDWI mapped up to 2423 km² flooded in 2024 (22 % of the area), versus 1206 km² by NDWI. Thematic accuracies for WNDWI reached 98-99 %, with Kappa values of 0.75 (2015) and 0.82 (2024), and AUC of 0.55 and 0.74, indicating strong agreement with HEC-RAS. WNDWI outperformed NDWI in detecting floodwaters in highly turbid and heterogeneous areas, though both indices were less sensitive in transition zones. This study's main contribution is the novel integration of modeling, spectral indices, and hydrodynamic validation, enhancing the reliability of RS-based flood mapping and supporting urban planning, risk management, and implementation of resilient strategies in climate-vulnerable metropolitan areas.

Seasonally dry forests like Caatinga are directly influenced by rainfall changes. In this context, the objective of this study was to analyze the behavior of seasonal changes in the vegetation of Caatinga biome, identifying possible changes in the scenario, through the spectral sensitivity of NDVI and Albedo, considering a space-time analysis (2015-2019), in Pajeú River Watershed - PE, Brazil. For this, the data from MODIS sensor MOD13Q1 (NDVI) and MOD09A1 (spectral bands) were used, on board of Aqua and Terra satellites. NDVI and Albedo were evaluated by means of map-images in the rainy and dry periods. The results showed an inversely proportional behavior between albedo and vegetation integrated with precipitation data in both climatic periods. In the dry period the indices presented the best relationships with R² ranging from -0.5 to -0.6, corresponding to the dynamics of precipitation in the basin, however NDVI was sensitive to the dynamics of the microclimate of the basin and albedo was more sensitive to the response of non-vegetated areas. The use of MODIS data for the generation of multitemporal scale cartographic products proved to be an indicator of land use and land cover change in seasonally dry forests such as the Caatinga.

O espaço urbano da cidade do Recife apresenta como uma de suas principais características a ocupação de morros, onde há, frequentemente, habitações construídas em áreas de encostas, as quais estão, por isso, suscetíveis a escorregamentos. Esse cenário constitui um dos principais desafios relacionados à ocupação do solo no contexto da urbanização, que é enfrentado pela gestão municipal. Diante disso, para analisar as condições dessas ocupações, a presente pesquisa foi conduzida na comunidade do Córrego da Telha, situada em uma região de vulnerabilidade ocupacional com uma geomorfologia propícia à ocorrência de deslizamentos, em que foram investigadas evidências atuais desse problema. Para atingir o objetivo, foram realizadas visitas de campo, exploração de dados espaço-temporais, levantamentos bibliográficos e análise de informações disponibilizadas pela Defesa Civil do Recife. Ao longo da pesquisa, constatou-se que no Córrego da Telha grande parte das ocupações está localizada em áreas de risco de deslizamento, com falta de estrutura de sustentação e com sistema de saneamento precário. Embora medidas compensatórias, como aplicação de lonas plásticas e construção de muros de arrimo, tenham sido implementadas, observa-se a ocorrência de problemas como acúmulo de lixo e falta de um sistema de drenagem adequado. Ao final do levantamento, constatou-se que 39% das residências existentes e 45% das residências construídas entre 2015 a 2023 possuem declividade acima de 20º. Além disso, no mínimo 5 vezes por ano, a Defesa Civil é acionada pela população para atuar em situações de deslizamento imediato. Este estudo se mostrou extremamente relevante, trazendo à tona uma reflexão sobre as condições peculiares das ocupações na região, uma vez que se trata de uma área de alta vulnerabilidade social em relação a diferentes aspectos, constituindo-se como uma área de ocupação urbana com desafios complexos.