Landslides can have serious environmental, economic, and social consequences. Using artificial neural networks (ANN) to map these landslides is becoming more frequent every year, being one of the most reliable methods for this. Among the prime influences on the generated maps, sample areas are significantly interesting, since they directly influence the result. In this research, we investigated how the performance of these models is influenced by the use of partial sampling (with landslides caused by a single precipitation event - Single Model) or total (with landslides caused by multiple precipitation events - Full Model). This is one of the main topics that our study approaches. This study aims to evaluate the criteria for landslide sampling and ANN modeling by analyzing the influence of distance on the sampling processes, the use of multiple landslide events, and the relationship between terrain attributes and susceptibility models. To this end, were used five sampling areas (1638 points samples of landslides) in the Serra Geral, southern Brazil, distance buffers in the non-occurrence sampling process (2-40 km), and 16 terrain attributes. The training of the multilayer network was carried out by backpropagation algorithm, and the accuracy was calculated using the Area Under the Receiver Operating Characteristic Curve. The results showed that the greater the distances of the non-occurring samples, the greater the accuracy of the model, with a 40 km buffer resulting in the best models. They also showed that the use of multiple events (Full Model) produced better results than each event used separately (Single Model), obtaining accuracies of 0.954 and 0.931, respectively. This is mainly because there is greater differentiation between occurrence and non-occurrence samples when using multiple events, thus facilitating the distinction between more and less susceptible areas.

Aquaculture is currently one of the fastest growing food production systems globally, and shrimp is considered one of the most highly valued products. Our study area is the lower Jaguaribe River sub-basin (LJRSB), located in the northeastern part of Ceará in Brazil. The aquaculture activity in this area began in the early 1990s and is currently one of the largest shrimp producers in Brazil. This study generated a spatial-temporal analysis of vegetation index and land use and land cover (LULC) using remote sensing images from Landsat satellites processed using geographic information systems (GIS). The findings showed an increase in the water bodies class where shrimp farms are found. In addition, to help us discuss the results, data from the Global Surface Water Explorer was also used to understand this change throughout intra and interannual water variability. Besides shrimp farms’ intensification, agricultural areas in the LJRSB also increased, mainly in the irrigated perimeter lands (IPLs), causing a loss in the Caatinga native vegetation. In summary, over recent years, significant changes have been noticeable in the LJRSB coastal region, caused by an increase in shrimp farms mainly located on the Jaguaribe River margins, destroying the native riparian forest.

Background: Understanding carbon dynamics in Earth’s ecosystem is necessary for mitigating climate change. With recent advancements in technologies, it is important to understand both how carbon quantification in soil and vegetation is measured and how it can be improved. Therefore, this study conducted a bibliometric and bibliographic review of the most common carbon quantification methodologies. Results: Among the most widely used techniques, the Walkley-Black method and Elemental Analysis stand out for measuring below-ground carbon, while forest inventories are prominent for assessing above-ground carbon. Additionally, we found that the United States and China have the largest number of publications on this topic, with forest and agricultural areas being the most studied, followed by grasslands and mangroves. However, it should be noted that despite being indirect techniques, remote sensing, regression analysis, and machine learning have increasingly been used to generate geo-environmental carbon models for various areas. Landsat satellite images are the most widely used in remote sensing, followed by LiDAR digital models. Conclusions: These results demonstrate that while new technologies do yet not replace analytical techniques, they are valuable allies working in conjunction with the current carbon quantification process.

A bacia hidrográfica do Rio dos Sinos concentra uma intensa atividade agrícola e industrial em seu território, o que ao longo dos anos tem provocado um desequilíbrio ambiental e um comprometimento dos seus recursos naturais. Entendendo a bacia como uma unidade de gestão territorial, se faz necessário o aprimoramento e a criação de ferramentas que auxiliem na gestão deste território. Neste sentido, o presente artigo tem como objetivo a aplicação de uma metodologia de zoneamento ambiental, denominada Potencial de Uso Conservacionista (PUC), com fins de estratificar o território da bacia em níveis de potencial natural de uso. Para isso, foram utilizadas bases geoespaciais de solo, litologia e declividade da região, como dados de entrada deste modelo, determinando pesos para essas variáveis, através de uma Análise Hierárquica de Processos (AHP). A partir disto, foram obtidos os níveis de PUC da bacia do Rio dos Sinos, que foram combinados com um mapa de uso e cobertura do solo da região para um diagnóstico dos níveis de conservação da área. Os resultados mostraram que na bacia predominam os níveis de potencial de uso entre “Médio” e “Alto”. A bacia apresenta certo nível de conservação em seu uso do solo no alto curso, o que combina com os níveis “Baixo” e “Muito Baixo” de PUC ali presentes. O desafio principal reside na necessidade de adequação das atividades agrícolas e indústrias no médio e baixo curso da referida bacia, onde se encontram os níveis de potencial de uso “Alto” e “Muito Alto”, a fim de promover o uso sustentável dos recursos ambientais ali presentes e a mitigação dos impactos ambientais destas atividades.

Grande parte dos deslizamentos no Brasil são relacionados a eventos pluviométricos extremos, ao tipo e espessura de solo e às características do relevo, e frequentemente causam perdas sociais e danos econômicos. O presente trabalho teve como objetivo analisar a relação entre pontos de ruptura (coroa) e parâmetros morfométricos de deslizamentos, relativos a um evento hidrometeorológico extremo, na bacia hidrográfica do Rio Rolante - RS. A partir de imagens SRTM 30m, foram obtidos dados de declividade, elevação, curvatura horizontal e vertical e aspecto. Com base no mapeamento foram obtidos 143 pontos de ruptura em cicatrizes de deslizamentos. Utilizando agrupamento K-means foram identificados padrões morfométricos relacionados aos pontos de ruptura, e gerados perfis de vertentes. Os resultados mostram 4 tipos de vertentes na bacia do rio Rolante, com ocorrência de processos de deslizamentos. Perfis côncavos e convergentes tiveram menor ocorrência, e perfis convexos e divergentes apresentaram maior ocorrência. Com base nas médias dos principais agrupamentos de clusters, a declividade e as curvaturas tiveram a maior importância em relação à localização das rupturas na vertente, e foram importantes condicionantes de suscetibilidade aos deslizamentos. As médias dos agrupamentos ocorreram acima de 30º de declividade no agrupamento com 4 clusters, que foi o mais representativo.