The great majority of the world population resides nowadays in urban areas. Understanding their physical and social structure, and especially their urban land use pattern dynamics throughout time, becomes crucial for successful, effective management of such areas. This study is committed to simulate and predict urban land use change in a pilot city belonging to the São Paulo Metropolitan Region, southeast of Brazil, by means of a cellular automata model associated with the Markov chain. This model is driven by data derived from orbital and airborne remotely sensed images and is parameterized by the Bayesian weights of evidence method. Several layers related to infrastructure and biophysical aspects of the pilot city, São Caetano do Sul, were used as evidence in the simulation process. Alternative non-stationary scenarios were generated for the short-run, and the results obtained from past simulations were statistically validated using a multiresolution “goodness-of-fit” metric relying on fuzzy logic. The best simulations reached fuzzy similarity indices around 0.25–0.58 for small neighborhood windows when an exponential decay approach was employed for the analysis, and approximately 0.65–0.95 when a constant decay and larger windows were considered. The adopted Bayesian inference method proved to be a good parameterization approach for simulating processes of urban land use change involving multiple classes and transitions.

Mining is an important economic activity in the world. Mining activities are known for their high environmental impacts. Considering the high magnitude of some operations and amounts of tailings produced, the mining activity has represented a risk for several regions and populations, which can be affected by accidents and inefficient disposal and monitoring systems. Remote sensing has been frequently used to map areas that are large or difficult to access; it enables the obtaining of information on objects or phenomena on the Earth's surface without requiring physical contact, making it an important source of data collection that can be used for several purposes. Images used in remote sensing provide a synoptic and multitemporal view of large areas and are an alternative for environmental studies. The objective of this work was to evaluate the extension of the area affected by the collapse of the tailings dam of the Vale S.A. company in the Corrego do Feijao stream mine, in the municipality of Brumadinho, state of Minas Gerais, Brazil, which occurred on January 25, 2019, causing an environmental impact that will last for many years. The dimension of this environmental impact was analyzed using imagery of the Planet's satellite constellation with spatial resolution of 3.0 meters. The methodology used was based on digital image processing, using the PCI Geomatics and ENVI 5.3 software, which made possible the quantification of the affected area. Two different methods were used and the results were compared to those provided by the State Secretariat for Environment and Sustainable Development of Minas Gerais, Brazil. The results showed an affected area of 2.964096 km(2), from the Brumadinho dam to the Paraopeba River.

As operações aritméticas efetuadas entre bandas espectrais de imagens de sensoriamento remoto necessitam de correção atmosférica para eliminar os efeitos atmosféricos na resposta espectral dos alvos, pois os números digitais não apresentam escala equivalente em todas as bandas. Índices de vegetação, calculados com base em operações aritméticas, além de caracterizarem a vegetação, minimizam os efeitos da iluminação da cena causados pela topografia. Com o objetivo de analisar a eficácia da correção atmosférica no cálculo de índices de vegetação, este trabalho comparou os Índices de Vegetação por Diferença Normalizada (Normalized Difference Vegetation Index - NDVI), calculados com base em imagens corrigidas e não corrigidas de um recorte de uma cena Landsat 8/OLI situado na cidade do Rio de Janeiro, Brasil. Os resultados mostraram que o NDVI calculado pela reflectância, ou seja, imagem corrigida, apresentou o melhor resultado, devido ao maior discriminação das classes de vegetação e de corpos d'água na imagem, bem como à minimização do efeito topográfico nos valores dos índices de vegetação. Analysis of the atmospheric correction impact on the assessment of the Normalized Difference Vegetation Index for a Landsat 8 oli image A B S T R A C TThe image arithmetic operations must be executed on previously atmospherically corrected bands, since the digital numbers do not present equivalent scales in all bands. Vegetation indices, calculated by means of arithmetic operations, are meant for both targets characterization and the minimization of illumination effects caused by the topography. With the purpose to analyze the efficacy of atmospheric correction in the calculation of vegetation indices with respect to the mitigation of atmospheric and topographic effects on the targets spectral response, this paper compared the NDVI (Normalized Difference Vegetation Index) calculated using corrected and uncorrected images related to an inset of a Landsat 8 OLI scene from Rio de Janeiro, Brazil. The result showed that NDVI calculated from reflectance values, i.e, corrected images, presented the best results due to a greater number of vegetation patches and water bodies classes that could be discriminated in the image, as well the mitigation of the topographic effect in the vegetation indices values.Keywords: remote sensing, urban forest, atmospheric correction.

Com o passar dos anos, as elevadas concentrações de gases do efeito estufa (GEE) na atmosfera têm se acentuado, principalmente devido à incidência de desmatamentos e queimadas, os estudos sobre estoque e fluxo de carbono tornam-se necessários. Neste sentido, o presente trabalho visou avaliar, por meio de sensoriamento remoto multiespectral, o fluxo de CO2 no bioma do Cerrado, para os meses de março, maio, junho, agosto e outubro de 2019, com intervalo entre as cenas de, aproximadamente, 51 dias, sendo que a área em estudo está localizada em parte do Parque Nacional da Chapada dos Veadeiros – Goiás. Foram utilizados para base de dados, cinco cenas multiespectrais do satélite Sentinel-2, sensor MSI, avaliando a influência de incêndios no sequestro de carbono nesta região, no ano de 2019. A metodologia adotada, engloba o cálculo dos índices espectrais de vegetação NDVI, sPRI e CO2flux. Os resultados obtidos para os índices, mostram que o sequestro de carbono é menor no período da seca, dado ao fato de ocorrer maiores focos de incêndio e baixa precipitação, o que não é visto em períodos de quantidade ideal de precipitação, bem como de menor número de focos de incêndio. Estes índices corroboraram para mensurar a dinâmica dos fluxos de carbono na área em estudo e seus efeitos. Além disso, tornou-se perceptível a influência dos focos de incêndio para o fluxo de carbono.

Given the wide variety of image classifiers available nowadays, some questions remain about the accuracy and processing time of Very High Resolution (VHR) images. Another question concerns the use of a Single or Ensemble Classifiers. Of course, the main factor to consider is the quality of the classified image, but computational cost is also important, especially in applications that require real-time processing. Given this scenario, this paper aims to relate the accuracy of seven single classifiers and the ensemble of the same classifiers with the processing time. In this paper the ensemble of classifiers had the best results in terms of accuracy, however, it comes to processing time, the decision tree had the best performance.

The use of remote sensing and digital processing of images is increasingly present in the monitoring and analysis of environmental data, mainly due to the ease of obtaining orbital images and the advanced methods of image classification. Temporal evaluation of restoration of native vegetation surrounding springs is important for the protection of water resources. A project named Water Conserver (Conservador das Águas) was developed between 2005 and 2015, involving 380 springs in Extrema, state of Minas Gerais, Brazil, focused on the preservation of water sources. The present study was carried out to quantify and analyze Permanent Preservation Areas (PPA) surrounding these water bodies. Free orbital images with spatial resolution of 30 m were acquired from the satellites Landsat-7 and Landsat-8 in 2003 and 2021, respectively. Object-oriented classification was used to evaluate the total PPA of restored springs over 18 years. The success of the Water Conserver Project resulted in a total restoration of more than 82% of areas surrounding springs up to 2021.