Species distribution models have become an essential tool for the selection of conservation areas. Given this perspective, this research aimed to analyze the dynamics of the fundamental niche of Ziziphus joazeiro Mart. facing climate change. The MaxEnt algorithm was used to correlate species points of occurrence with bioclimatic variables of four periods: Middle Holocene, current period, and the optimistic and pessimistic future scenarios of the HadGEM2-ES model of general atmospheric circulation. The accuracy of the predictions and the influence of the variables was evaluated using the area under the curve (AUC index) and the Jackknife test. The predictions showed high precision (AUC > 0.895), and the bioclimatic variables related to precipitation were the ones that most contributed to determining potential areas for the occurrence of the species. A reduction of more than 60% was observed in the area with climatic suitability for the occurrence of Z. joazeiro, over time. The range that comprises the central region of the distribution of the species stands out as the region with the greatest suitability, as projected for the current and future period, this being the area that should receive the most attention for the conservation programs of the species. Os modelos de distribuição das espécies tornaram-se uma ferramenta essencial para a seleção de áreas de conservação. Dada esta perspectiva, esta pesquisa teve como objetivo analisar a dinâmica do nicho fundamental do Ziziphus joazeiro Mart. frente a mudança climática. O algoritmo MaxEnt foi usado para correlacionar pontos de ocorrência das espécies com variáveis bioclimáticas de quatro períodos: Holoceno médio, período atual e os cenários futuros otimista e pessimista do modelo HadGEM2-ES de circulação geral atmosférica. A precisão das predições e a influência das variáveis foi avaliada usando a área sob a curva (índice AUC) e o teste de Jackknife. As predições mostraram alta precisão (AUC > 0,895) e as variáveis bioclimáticas relacionadas à precipitação foram as que mais contribuíram para determinar áreas potenciais para a ocorrência da espécie. Foi observada uma redução de mais de 60% na área com adequabiliade climática para a ocorrência de Z. joazeiro, ao longo do tempo. A faixa que compreende a região central da distribuição da espécie se destaca como a região com maior adequação, conforme projetada para o período atual e futuro, sendo esta a área que deve receber mais atenção para os programas de conservação da espécie.

Studies that characterize the effects of climatic factors on the geographic distribution of arboreal individuals are of fundamental importance, especially for widely exploited species of wood potential, such as Mimosa tenuiflora (Willd) Poiret (jurema-preta). In this sense, the objective of this work was to predict the climatically adequate areas for the occurrence of Mimosa tenuiflora, present (1960-1990) and future (2070). We used the Maxent algorithm to relate the occurrence records of the species to the climatic variables. For the year 2070, we test two scenarios and three general atmospheric circulation models, HadGEM2-ES, GISS-E2-R and MIROC-ESM. Modeling for the present presented an AUC index (area under the curve) of 0.94 (± 0.02), indicating a good fit of the model used. For the future scenario, the AUC value ranged from 0.88 to 0.89 and 0.87 to 0.88 for the optimistic and pessimistic scenarios, respectively. The highest percentage of contribution was to the annual precipitation variable. The areas of adequacy occupied the states of Ceará and Rio Grande do Norte in higher intensity and almost all of them. When compared to the present, the geographic territory with high suitability for the future presented a reduction from 28.7% to 53.7% in the optimistic scenario and 30.9% to 59.4% in the pessimistic scenario. The information obtained can be used as a subsidy for the establishment of commercial plantations, the definition of management and conservation strategies, and the creation of an in situ conservation bank for Mimosa tenuiflora species.

Forest fire detection towers are crucial in supporting rapid firefighting actions in conservation units and thus reducing environmental, social, and economic damages. Thus, the aim was to evaluate scenarios for optimal allocation of forest fire detection towers, according to the risk of occurrence, in the Caparaó National Park, Brazil. Thus, by geotechnological analysis, the areas most susceptible to forest fires and the optimal locations for installation of detection and monitoring for these events were delimited. To run the proposed models, biological, physical, socioeconomic, and meteorological variables were used. From the application of the methodologies, it was observed that 76.70% of the study area was covered by low, moderate, and shallow fire risk classes, while high and very high-risk classes were concentrated in the buffer zone. The scenario with 45 towers was considered the most advantageous, given that they presented viewing levels above 70% and a lower cost per hectare viewed than the scenario with 48 towers. Results showed no critical risks of fire occurrence within the conservation unit, but preventive measures are still needed to avoid fire spread, particularly near the buffer zone. The study's methodologies can be applied in other areas to improve forest fire prevention and control efforts.

We present a novel and efficient approach that enables the evaluation of environmental quality in cities worldwide using high-resolution satellite imagery, based on a new green index (GI) through multivariate analysis, to compare the proportion of urban green spaces (UGSs) with built and impervious surfaces. High-resolution images were used to perform a supervised classification of 25 districts in the city of São Paulo, Brazil. Only 11 districts showed higher urban forests, green spaces, green index, and green vs. built values, and impervious surface proportions with lower impervious and built spaces. On the other hand, the remaining districts had higher population densities and unfavorable conditions for urban ecosystem development. In some cases, urban green spaces were three-times smaller than the built and impervious surfaces, and none of the districts attained a high green quality index (0.75 to 1). Artificial intelligence techniques improved the precise identification of land cover, particularly vegetation, such as trees, shrubs, and grasses. The development of a novel green index, using multivariate statistical analysis, enhanced positive interactions among soil cover classes, emphasizing priority areas for enhancing environmental quality. Most of them should be prioritized by decision makers due to the low environmental quality, as identified by the low green index and worse ecosystem services, well-being, and health outcomes. The method can be employed in many other cities to enhance urban ecosystem quality, well-being, and health. The green index and supervised classification can characterize pastures, degraded forest fragments, and guide forest restoration techniques in diverse landscapes.