Forest fires threaten the population’s health, biomass, and biodiversity, intensifying the desertification processes and causing temporary damage to conservation areas. Remote sensing has been used to detect, map, and monitor areas that are affected by forest fires due to the fact that the different areas burned by a fire have similar spectral characteristics. This study analyzes the performance of the k-Nearest Neighbor (kNN) and Random Forest (RF) classifiers for the classification of an area that is affected by fires in central Portugal. For that, image data from Landsat-8, Sentinel-2, and Terra satellites and the peculiarities of each of these platforms with the support of Jeffries–Matusita (JM) separability statistics were analyzed. The event under study was a 93.40 km² fire that occurred on 20 July 2019 and was located in the districts of Santarém and Castelo Branco. The results showed that the problems of spectral mixing, registration date, and those associated with the spatial resolution of the sensors were the main factors that led to commission errors with variation between 1% and 15.7% and omission errors between 8.8% and 20%. The classifiers, which performed well, were assessed using the receiver operating characteristic (ROC) curve method, generating maps that were compared based on the areas under the curves (AUC). All of the AUC were greater than 0.88 and the Overall Accuracy (OA) ranged from 89 to 93%. The classification methods that were based on the kNN and RF algorithms showed satisfactory results.

Fire is one of the natural agents with the greatest impact on the terrestrial ecosystem and plays an important ecological role in a large part of the terrestrial surface. Remote sensing is an important technique applied in mapping and monitoring changes in forest landscapes affected by fires. This study presents a spectral separability analysis for the detection of burned areas using Landsat-8 OLI/TIRS images in the context of fires that occurred in different biomes of Brazil (dry ecosystem) and Portugal (temperate forest). The research is based on a fusion of spectral indices and automatic classification algorithms scientifically proven to be effective with as little human interaction as possible. The separability index (M) and the Reed–Xiaoli automatic anomaly detection classifier (RXD) allowed the evaluation of the spectral separability and the thematic accuracy of the burned areas for the different spectral indices tested (Burn Area Index (BAI), Normalized Burn Ratio (NBR), Mid-Infrared Burn Index (MIRBI), Normalized Burn Ratio 2 (NBR2), Normalized Burned Index (NBI), and Normalized Burn Ratio Thermal (NBRT)). The analysis parameters were based on spatial dispersion with validation data, commission error (CE), omission error (OE), and the Sørensen–Dice coefficient (DC). The results indicated that the indices based exclusively on the SWIR1 and SWIR2 bands showed a high degree of separability and were more suitable for detecting burned areas, although it was observed that the characteristics of the soil affected the performance of the indices. The classification method based on bitemporal anomalous changes using the RXD anomaly proved to be effective in increasing the burned area in terms of temporal alteration and performing unsupervised detection without relying on the ground truth. On the other hand, the main limitations of RXD were observed in non-abrupt changes, which is very common in fires with low spectral signal, especially in the context of using Landsat-8 images with a 16-day revisit period. The results obtained in this work were able to provide critical information for fire mapping algorithms and for an accurate post-fire spatial estimation in dry ecosystems and temperate forests. The study presents a new comparative approach to classify burned areas in dry ecosystems and temperate forests with the least possible human interference, thus helping investigations when there is little available data on fires in addition to favoring a reduction in fieldwork and gross errors in the classification of burned areas.

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.

O fogo é um fator importante na perturbação e perda de florestas secas tropicais globais. Os incêndios florestais exercem um papel ecológico relevante, pois afetam a biodiversidade local, as propriedades do solo e o suprimento de água. O bioma Caatinga apresenta um alto nível de degradação de atividades antrópicas e naturais, sendo extremamente afetado por incêndios originados predominantemente por atividades humanas. O sensoriamento remoto orbital, por apresentar características espaciais, espectrais e temporais específicas, é uma alternativa tecnológica imprescindível no monitoramento de áreas afetadas pelo fogo na superfície terrestre. Este trabalho teve como objetivo analisar, no âmbito espacial, espectral e temporal, o comportamento de um incêndio em ambiente de Caatinga a partir de Imagens Landsat-8, Índice de Vegetação Realçado e Análise por Componentes Principais. A quantificação de características da vegetação derivada do índice espectral fornece uma melhor avaliação da condição física da superfície terrestre sob efeitos do fogo. Técnicas de sensoriamento remoto e estatística multivariada foram utilizadas para avaliar comportamento espectral da vegetação nativa exposta a eventos de incêndio do bioma Caatinga. Os resultados do Teste de Normalidade Kolmogorov-Smirnov apresentaram um nível de significância de 5 %. A integração dos métodos estatísticos de Regressão Linear Simples e Análise por Componentes Principais possibilitaram diagnósticos importantes nas estimativas e/ou relacionamentos entre as variáveis aleatórias. A técnica multivariada permitiu avaliar 94% da variação de dados. Os mapas resultantes da metodologia testada representam um aprimoramento importante no mapeamento da distribuição da vegetação. Este estudo gera indicativos para futuras pesquisas científicas vinculadas ao gerenciamento do espaço relacionado à vulnerabilidade e recuperação de paisagens de vegetação do clima semiárido sob situações de fogo geradas por incêndios.

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.

Forest fires are considered one of the major dangers and environmental issues across the world. In the Cerrado biome (Brazilian savannas), forest fires have several consequences, including increased temperature, decreased rainfall, genetic depletion of natural species, and increased risk of respiratory diseases. This study presents a methodology that uses data from the Sea and Land Surface Temperature Radiometer (SLSTR) sensor of the Sentinel-3B satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) of the Terra satellite to analyze the thematic accuracy of burned area maps and their sensitivity under different spectral resolutions in a large area of 32,000 km² in the Cerrado biome from 2019 to 2021. The methodology used training and the Support Vector Machine (SVM) classifier. To analyze the spectral peculiarities of each orbital platform, the Transformed Divergence (TD) index separability statistic was used. The results showed that for both sensors, the near-infrared (NIR) band has an essential role in the detection of the burned areas, presenting high separability. Overall, it was possible to observe that the spectral mixing problems, registration date, and the spatial resolution of 500 m were the main factors that led to commission errors ranging between 15% and 72% and omission errors between 51% and 86% for both sensors. This study showed the importance of multispectral sensors for monitoring forest fires. It was found, however, that the spectral resolution and burning date may gradually interfere with the detection process.