Cocoa is a plant with origins in northwestern South America with high relevance in the global economy. Evidence indicates that cocoa is sensitive to a dry climate, under which crop production is reduced. Projections for future climate change scenarios suggest a warmer and drier climate in the Amazon basin. In this paper, we quantify the potential effects in cocoa production due to its edaphoclimatic suitability changes to the Brazilian Amazon biome and account for regional differences in planning occupation territories. We modeled the suitability of cocoa’s geographical distribution using an ensemble of 10 correlative models that were run in the “biomod2” library and projected to two future climate scenarios (RCPs 4.5 and 8.5) by 2050. Combining information on climate and soil suitability and installed infrastructure in the macro-regions of the Brazilian Amazon. We defined a zoning system to indicate how cocoa production may respond to climate change according to the current and future suitability model. Our results suggest that a reduction in precipitation and an increase in temperature may promote a reduction in the suitability of cocoa production in the Brazilian Amazon biome. In addition of the areas suitable for cocoa plantation, we found a 37.05% and 73.15% decrease in the areas suitable for intensification and expansion zones under RCP 4.5 and 8.5, respectively, compared with the current scenario. We conclude that there may be a need to expand land to cocoa production in the future, or else it will be necessary to plant a cocoa variety resistant to new climatic conditions. Besides, we recommend procedures to combat illegal deforestation to prevent the most critical climate change scenarios from occurring.

A gestão do lixo produzido pelos centros urbanos tem sido um desafio para o Brasil. Nos últimos 20 anos, este assunto vem sendo o alvo de muitas medidas educativas, leis e diretrizes que buscam minimizar os problemas causados pela má gestão desses resíduos. Neste trabalho foi proposta uma metodologia, utilizando dados do sistema de informações geográficas e do sensoriamento remoto, com o objetivo de avaliar o território onde está localizado a cidade de Castanhal e selecionar a área que mais se ajuste as atuais normas federais de gestão do lixo urbano e de sustentabilidade. Observou-se que além da cidade manter um lixão a céu aberto, sem nenhum tratamento, o local é inadequado por estar próxima de rios, região de alagamento e casas. Em seguida, usando dos mesmos critérios que tornam a área do atual lixão imprópria, foi selecionado através da análise de terreno (relevo, hidrografia e proximidade do centro urbano) o local ideal para a instalação do projeto de aterro sanitário. Sendo este um trabalho preliminar com o objetivo de apresentar uma ferramenta simples e de baixo custo e que pode ser essencial para a tomada de decisão dos responsáveis pela gestão do lixo urbano da cidade de Castanhal.

Perennial crops are a key component of sustainable agriculture due to their higher carbon storage compared to annual systems. Improving their mapping is essential to support effective public policies. This study proposes an integrated approach combining field observations, waveform LiDAR data from Global Ecosystem Dynamics Investigation (GEDI), and hyperspectral imagery from EnMAP to enhance perennial crop classification.Field data were used to develop an interpretation key for the analyzed classes. GEDI Level 1B waveforms were normalized to extract metrics such as the number of peaks, while structural attributes, including canopy height (RH95), were derived from GEDI Level 2A. From EnMAP imagery, spectral reflectance was obtained and processed using mean values, first- and second-order derivatives, and continuum removal. Differences among classes were evaluated using Tukey’s test and principal component analysis (PCA). Results show that waveform structure and canopy height significantly distinguish secondary vegetation from perennial crops. Additionally, spectral regions in the visible and shortwave infrared present significant differences between these classes. These findings demonstrate that integrating structural and spectral variables improves the discrimination of perennial crops and helps overcome challenges in separating them from secondary vegetation.

Perennial crops are a key component of sustainable agriculture due to their higher carbon storage compared to annual systems. Improving their mapping is essential to support effective public policies. This study investigates the use of field observations, waveform Light Detection and Ranging (LiDAR) data from the Global Ecosystem Dynamics Investigation (GEDI), and hyperspectral imagery from EnMAP to identify spectral and structural features and assess their separability. Field data were used to develop an interpretation key for the analyzed classes. GEDI Level 1B waveforms were normalized to extract metrics such as the number of peaks, while structural attributes, including canopy height (RH95), were derived from GEDI Level 2A. From EnMAP imagery, spectral reflectance was obtained and processed using mean values, first- and second-order derivatives, and continuum removal. Differences among classes were evaluated using Tukey’s test and principal component analysis (PCA). Results show that waveform structure and canopy height significantly distinguish secondary vegetation from perennial crops. Additionally, spectral regions in the visible and shortwave infrared present significant differences between these classes. These findings demonstrate that the structural and spectral variables could improve the discrimination of perennial crops and help overcome challenges in separating them from secondary vegetation.

Percepção socioambiental dos usuários da praia de Panaquatira no município de São José de Ribamar/MA – Brasil/ Socio-environmental perception of users of Panaquatira beach in the municipality of São José de Ribamar/MA – Brazil