The coffee leaf miner (Leucoptera coffeella) is a key coffee pest in Brazil that can cause severe defoliation and a negative impact on the productivity. Thus, it is essential to identify initial pest infestation for the sake of appropriate time control to avoid further economic damage to the coffee crops. A fast non-destructive method is an important tool that can be used to monitor the occurrence of the coffee leaf miner. The present work aims to identify the occurrence of coffee leaf miner infestation through a new vegetation index, using multispectral images from the Sentinel-2 satellite and the Google Earth Engine platform. Coffee leaf miner infestation was measured in the field in four cities in the state of Minas Gerais. The largest infestations occurred in September, October, and November but particularly in October 2021, in which the rate of infestation reached 85%, followed by September 2020 with a maximum infestation of 76%. The calculation steps of the vegetation indices and mappings were carried out in the Google Earth Engine cloud processing platform through the development of a script in JavaScript programming language. Combinations of two sensitive bands were selected to detect coffee leaf miner infestation, and from these, the “Coffee-Leaf-Miner Index” was developed, which was compared with other existing vegetation indices in terms of their performance for coffee leaf miner detection. The combination of the NIR–BLUE and NIR–RED bands was more sensitive for the detection of coffee leaf miner infestation; therefore, the NIR, BLUE, and RED bands were selected to develop the new index. The “Coffee-Leaf-Miner Index” presented the best performance among those evaluated, with a coefficient of determination of about 0.87, a root mean square error of 4.92% coffee leaf miner infestation, accuracy of 89.47%, and kappa coefficient of 95.39. The R² range of other spectral indices which exist in the literature and which were used in this study was from 0.017 to 0.867, and the root mean square error ranged from 4.996 to 13.582% coffee leaf miner infestation. The machine learning method was then adopted using the supervised Random Forest and Support Vector Machine algorithms to recognize patterns of coffee leaf miner infestation in the field, only the Coffee-Leaf-Miner Index was used for the identification test of the coffee leaf miner infestation. The Support Vector Machine with linear Kernel type was applied to establish a discrimination model. The number of trees for the Random Forest classifier was 100. The Support Vector Machine presented a lower performance than the Random Forest algorithm, but the performance of both were above 80% for user and producer precision. Three bands (Blue, Red, NIR) were selected for the creation of the new index, which showed capacity for remote detection of coffee leaf miner infestation on a regional scale. Thus, “Coffee-Leaf-Miner Index” can identify coffee leaf miner infestation thanks to all the complexity involved in detecting pests via orbital remote sensing.

Coffee Rust (Hemileia vastatrix) is considered the primary coffee disease in the world. The pathogenic fungus can find favorable environmental conditions in different countries, constantly threatening coffee producers. The previous detection of the incidence of coffee rust in a region is crucial because it provides an overview of the disease’s progress aiding in coffee plantations management. The objective of this work was the development of a vegetation index for remote monitoring of coffee rust infestation. Using satellite images from the MSI/Sentinel-2 collection, the Machine Learning classifier algorithm - Random Forest, and the cloud processing platform - Google Earth Engine, the most sensitives bands in coffee rust detection were determined, namely B4 (Red), B7 (Red Edge 3) and B8A (Red Edge 4). Thus, the Triangular Vegetation Index method was used to create a new vegetative index for remote detection of coffee rust infestation on a regional scale, named Coffee Rust Detection Index (CRDI). A linear regression model was created to estimate rust infestation based on the performance of the new index. The model presented a coefficient of determination (R²) of 62.5%, and a root mean square error (RMSE) of 0.107. In addition, a comparison analysis of the new index with eight other vegetative indices commonly used in the literature was carried out. The CRDI obtained the best performance in coffee rust detection among the others. This study shows that the new index CRDI has the robustness and general capacity to be used in monitoring coffee rust infestation on a regional scale. Key words: Hemileia vastatrix; disease monitoring; triangular vegetation index method; google earth engine.

There is no doubt about the importance of coffee cultivation to the Brazilian economy, a fact revealed by a large number of national and international events in this sector. However, it has only been in the last two decades that women have taken part in such events, mainly because of the prejudice that prevailed until the beginning of the 21st century. The present study was carried out with the aim of identifying the profile of the women participating in the events of the coffee throughout Brazil. A total of 556 questionnaires were applied during events held in the years 2016 and 2017 in ten coffee regions of Brazil. The questioned were composed by 23 questions addressing: schooling; the areas of performance and income of women in the coffee sector. Approximately 25 per cent of the women interviewed have post-graduate level training, and only 0.6 per cent are not literate. Most women operate in the area of coffee production have the monthly income between 2 to 5 minimum wages. In general, women who work in the coffee sector and participate in events linked to the national coffee industry, work mainly in the production stage and have a high degree of education, however, receive low wages.

This study presents a fire risk prediction framework applied to two conservation units within the Atlantic Forest biome (AFb): Serra da Gandarela National Park (PNSG), Minas Gerais, and Campos de Palmas Wildlife Refuge (RVSCP), Paraná. Daily climate data (2001–2023), remote sensing vegetation indices Normalized Difference Vegetation Index (NDVI) and Normalized Multi Band Drought Index (NMDI), fire foci, and estimates of soil volumetric moisture were integrated to analyze the climatic and environmental drivers of fire occurrence and to develop predictive models. Sea Surface Temperature (SST) anomalies in the Niño 3.4 region revealed the influence of El Niño–Southern Oscillation (ENSO) variability on local hydrometeorological dynamics. Vegetation indices and soil moisture data reinforced this relationship, with NMDI values below 0.4 and sharp declines in volumetric moisture indicating water stress during the dry season. Kernel density maps identified clusters of fire foci during this period, confirming the strong seasonality of fire occurrence. Based on climatic predictors and environmental indicators, fire risk indices were developed for each conservation unit and validated using independent data. Model performance showed moderate explanatory capacity, with coefficients of determination ranging from 0.53 to 0.68 and high agreement between estimated and observed values. Validation stratified by ENSO phases (Neutral, El Niño, and La Niña) demonstrated stable performance across contrasting climatic regimes, indicating temporal resilience of the modeling framework. Overall, the integration of climate data, spectral indices, and soil moisture information improves the ability to anticipate fire risk in Atlantic Forest conservation units, providing a useful tool to support prevention, monitoring, and decision-making in protected areas.

Os produtos de origem florestal, com o óleo de palma e outros, necessitam de condições meteorológicas ideais para maximizar sua produção ao longo do ano, porém existe uma lacuna de conhecimento no tocante a dados em campo destas variáveis. A limitação de estações meteorológicas na região amazônica, resultante de dificuldades logísticas, contribuem negativamente no que diz respeito à gestão e monitoramento ambiental. O presente estudo visa avaliar a eficiência do conjunto de dados de reanálise TerraClimate, para capital Rio Branco, usando a variável chuva e os dados disponíveis na estação meteorológica automática (A104) do Instituto Nacional de Meteorologia (INMET), durante anos de El Niño Oscilação-Sul (ENOS), buscando verificar a acurácia dos dados de reanálise para tais eventos. Foi analisado o comportamento das séries de chuva dos períodos de 2010-2011 (La Niña), 2012-2013 (neutralidade) e 2023-2024 (El Niño). Os resultados do TerraClimate evidenciaram uma baixa variação com os dados da A104. No período de 2010-2011 (La Niña), houve uma correlação de 0,677 e um índice de concordância de 0.8 entre os dados. No período de 2012–2013 (neutralidade) a correlação manteve-se positiva moderada, correspondente 0,578, sendo também o período com menor concordância com essa tendo valores de 0,584. Por fim, embora os resultados do TerraClimate apresentam-se como uma ferramenta eficiente para analisar a variabilidade temporal na região estudada, se faz necessário destacar que os dados de reanálise podem indicar valores que subestimam a realidade, salientando a importância de medições por meio de instrumentos in loco, como a A104.

Os incêndios florestais são problemáticas recorrentes na cidade de Rio Branco, Acre, Brasil, especialmente durante a estação seca. Este estudo tem como objetivo analisar os padrões de risco de fogo durante os anos de 2023 e 2024, utilizando dados climáticos para calcular os riscos de incêndios baseado no índice FMA+, derivado da tabela de risco de incêndio do Sisclass. Os dados de uso da terra foram obtidos do Instituto Brasileiro de Geografia e Estatística, os dados de focos de calor foram extraídos do banco de dados BDQueimadas, e os dados climáticos foram provenientes de conjuntos de reanálise com resolução de 0,1° x 0,1°. Os dados climáticos ausentes a partir de março de 2024 foram complementados com observações da estação automática A104 do Instituto Nacional de Meteorologia. A metodologia integrou variáveis climáticas com dados de uso da terra e de focos de fogo para o cálculo do índice FMA+. Os resultados revelaram que o mês de agosto apresentou a maior ocorrência de risco extremo de fogo, com 68 casos. O ano de 2024 registrou o maior número de eventos extremos, totalizando 53 casos, superando 2023. Observou-se forte influência da estação seca e da estação chuvosa na redução e o aumento do risco de incêndios. As florestas presentes no município são as mais afetadas e, a perda de cobertura florestal pode agravar os riscos de incêndios florestais em áreas urbanas e também as práticas agrícolas, se não forem realizadas de forma adequada, contribuirão para a perda da vegetação em escalas mais elevadas.