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Tailings dams' breaks are environmental disasters with direct and intense degradation of soil. This study analyzed the impacts of B1 tailings dam rupture occurred in the Ribeirão Ferro-Carvão watershed (Brumadinho, Brazil) in January 25, 2019. Soil organic carbon (SOC) approached environmental degradation. The analysis encompassed wetlands (high-SOC pools) located in the so-called Zones of Decreasing Destructive Capacity (DCZ5 to DCZ1) defined along the Ferro-Carvão's stream bed and banks after the disaster. Remote sensed water indices were extracted from Landsat 8 and Sentinel-2 satellite images spanning the 2017-2021 period and used to distinguish the wetlands from other land covers. The annual SOC was extracted from the MapBiomas repository inside and outside the DCZs in the same period, and assessed in the field in 2023. Before the dam collapse, the DCZs maintained stable levels of SOC, while afterwards they decreased substantially reaching minimum values in 2023. The reductions were abrupt: for example, in the DCZ3 the decrease was from 51.28 ton/ha in 2017 to 4.19 ton/ha in 2023. Besides, the SOC increased from DCZs located near to DCZs located farther from the dam site, a result attributed to differences in the percentages of clay and silt in the tailings, which also increased in the same direction. The Ferro-Carvão stream watershed as whole also experienced a slight reduction in the average SOC levels after the dam collapse, from nearly 43 ton/ha in 2017 to 38 ton/ha in 2021. This result was attributed to land use changes related with the management of tailings, namely opening of accesses to remove them from the stream valley, creation of spaces for temporary deposits, among others. Overall, the study highlighted the footprints of tailings dams' accidents on SOC, which affect not only the areas impacted with the mudflow but systemically the surrounding watersheds. This is noteworthy.

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This paper describes a dataset of convective systems (CSs) associated with hailstorms over Brazil tracked using GOES-16 Advanced Baseline Imager (ABI) measurements and the Tracking and Analysis of Thunderstorms (TATHU) tool. The dataset spans from June 5, 2018, to September 30, 2023, providing five-year period of storm activity. CSs were detected and tracked using the ABI's clean IR window brightness temperature at 10.3 µm, projected on a 2 km x 2 km Lat-Lon WGS84 grid. Systems were identified using a brightness temperature (BT) threshold of 235 K, conducive to detecting convective clusters with larger area and excluding smaller or non-convective cells such as groups of thin Cirrus clouds. Each detected CS was treated as an object, containing geographic boundaries and raster statistics such as BT's mean, minimum, standard deviation, and count of data points within the CS polygon, which serves as proxy for size estimates. The life cycle of each system was tracked based on a 10 % overlap area criterion, ensuring continuity, unless disrupted by dissociative or associative events. Then, the tracked CSs were filtered for intersections in space and time with verified ground reports of hail, from the Prevots group. The matches were then exported to a database with SpatiaLite enabled data format to facilitate spatial data queries and analyses. This database is structured to support advanced research in severe weather events, in particular hailfall. This setting allows for extensive temporal and spatial analyses of convective systems, making it useful for meteorologists, climate scientists, and researchers in related fields . The inclusion of detailed tracking information and raster statistics offers potential for diverse applications, including climate model validation, weather prediction enhancements, and studies on the climatological impact of severe weather phenomena in Brazil.

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BACKGROUND: The spread of mosquito-transmitted diseases such as dengue is a major public health issue worldwide. The Aedes aegypti mosquito, a primary vector for dengue, thrives in urban environments and breeds mainly in artificial or natural water containers. While the relationship between urban landscapes and potential breeding sites remains poorly understood, such a knowledge could help mitigate the risks associated with these diseases. This study aimed to analyze the relationships between urban landscape characteristics and potential breeding site abundance and type in cities of French Guiana (South America), and to evaluate the potential of such variables to be used in predictive models. METHODS: We use Multifactorial Analysis to explore the relationship between urban landscape characteristics derived from very high resolution satellite imagery, and potential breeding sites recorded from in-situ surveys. We then applied Random Forest models with different sets of urban variables to predict the number of potential breeding sites where entomological data are not available. RESULTS: Landscape analyses applied to satellite images showed that urban types can be clearly identified using texture indices. The Multiple Factor Analysis helped identify variables related to the distribution of potential breeding sites, such as buildings class area, landscape shape index, building number, and the first component of texture indices. Models predicting the number of potential breeding sites using the entire dataset provided an R² of 0.90, possibly influenced by overfitting, but allowing the prediction over all the study sites. Predictions of potential breeding sites varied highly depending on their type, with better results on breeding sites types commonly found in urban landscapes, such as containers of less than 200 L, large volumes and barrels. The study also outlined the limitation offered by the entomological data, whose sampling was not specifically designed for this study. Model outputs could be used as input to a mosquito dynamics model when no accurate field data are available. CONCLUSION: This study offers a first use of routinely collected data on potential breeding sites in a research study. It highlights the potential benefits of including satellite-based characterizations of the urban environment to improve vector control strategies.

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Several remote sensing indices have been used to monitor droughts, mainly in semi-arid regions with limited coverage by meteorological stations. The objective of this study was to estimate and monitor agricultural drought conditions in the Jequitinhonha Valley region, located in the Brazilian biomes of the Cerrado and Atlantic Forest, from 2001 to 2021, using vegetation indices and the meteorological drought index from remote sensing data. Linear regression was applied to analyze drought trends and Pearson's correlation coefficient was applied to evaluate the relationship between vegetation indices and climatic conditions in agricultural areas using the Standardized Precipitation Index. The results revealed divergences in the occurrences of regional droughts, predominantly covering mild to moderate drought conditions. Analysis spatial of drought trends revealed a decreasing pattern, indicating an increase in drought in the Middle and Low Jequitinhonha sub-regions. On the other hand, a reduction in drought was observed in the High Jequitinhonha region. Notably, the Vegetation Condition Index demonstrated the most robust correlation with the Standardized Precipitation Index, with R values ​​greater than 0.5 in all subregions of the study area. This index showed a strong association with precipitation, proving its suitability for monitoring agricultural drought in heterogeneous areas and with different climatic attributes. The use of remote sensing technology made it possible to detect regional variations in the spatio-temporal patterns of drought in the Jequitinhonha Valley. This vision helps in the implementation of personalized strategies and public policies, taking into account the particularities of each area, in order to mitigate the negative impacts of drought on agricultural activities in the region.

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Digital image datasets for Precision Agriculture (PA) still need to be available. Many problems in this field of science have been studied to find solutions, such as detecting weeds, counting fruits and trees, and detecting diseases and pests, among others. One of the main fields of research in PA is detecting different crop types with aerial images. Crop detection is vital in PA to establish crop inventories, planting areas, and crop yields and to have information available for food markets and public entities that provide technical help to small farmers. This work proposes public access to a digital image dataset for detecting green onion and foliage flower crops located in the rural area of Medellín City - Colombia. This dataset consists of 245 images with their respective labels: green onion (Allium fistulosum), foliage flowers (Solidago Canadensis and Aster divaricatus), and non-crop areas prepared for planting. A total of 4315 instances were obtained, which were divided into subsets for training, validation, and testing. The classes in the images were labeled with the polygon method, which allows training machine learning algorithms for detection using bounding boxes or segmentation in the COCO format.

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Changes in land cover directly affect biodiversity. Here, we assessed land-cover change in Cuba in the past 35 years and analyzed how this change may affect the distribution of Omphalea plants and Urania boisduvalii moths. We analyzed the vegetation cover of the Cuban archipelago for 1985 and 2020. We used Google Earth Engine to classify two satellite image compositions into seven cover types: forest and shrubs, mangrove, soil without vegetation cover, wetlands, pine forest, agriculture, and water bodies. We considered four different areas for quantifications of land-cover change: (1) Cuban archipelago, (2) protected areas, (3) areas of potential distribution of Omphalea, and (4) areas of potential distribution of the plant within the protected areas. We found that "forest and shrubs", which is cover type in which Omphalea populations have been reported, has increased significantly in Cuba in the past 35 years, and that most of the gained forest and shrub areas were agricultural land in the past. This same pattern was observed in the areas of potential distribution of Omphalea; whereas almost all cover types were mostly stable inside the protected areas. The transformation of agricultural areas into forest and shrubs could represent an interesting opportunity for biodiversity conservation in Cuba. Other detailed studies about biodiversity composition in areas of forest and shrubs gain would greatly benefit our understanding of the value of such areas for conservation.

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The intensive global use of pesticides presents an escalating threat to human health, ecosystems, and water quality. To develop national and local environmental management strategies for mitigating pollution caused by pesticides, it is essential to understand the quantities, timing, and location of their application. This study aims to estimate the spatial distribution of pesticide use in an agricultural region of La Plata River basin in Uruguay. Estimates of pesticide use were made by surveying doses applied to each crop. This information was spatialized through identifying agricultural rotations using remote sensing techniques. The study identified the 60 major agricultural rotations in the region and mapped the use and application amount of the nine most significant active ingredients (glyphosate, 2,4-dichlorophenoxyacetic acid, flumioxazin, S-metolachlor, clethodim, flumetsulam, triflumuron, chlorantraniliprole, and fipronil). The results reveal that glyphosate is the most extensively used pesticide (53.5% of the area) and highest amount of use (> 1.44 kg/ha). Moreover, in 19% of the area, at least seven active ingredients are applied in crop rotations. This study marks the initial step in identifying rotations and estimating pesticide applications with high spatial resolution at a regional scale in agricultural regions of La Plata River basin. The results improve the understanding of pesticide spatial distribution based on data obtained from agronomists, technicians, and producers and provide a replicable methodological approach for other geographic and productive contexts. Generating baseline information is key to environmental management and decision making, towards the design of more robust monitoring systems and human exposure assessment.

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Identifying plantation lines in aerial images of agricultural landscapes is re-quired for many automatic farming processes. Deep learning-based networks are among the most prominent methods to learn such patterns and extract this type of information from diverse imagery conditions. However, even state-of-the-art methods may stumble in complex plantation patterns. Here, we propose a deep learning approach based on graphs to detect plantation lines in UAV-based RGB imagery, presenting a challenging scenario containing spaced plants. The first module of our method extracts a feature map throughout the backbone, which consists of the initial layers of the VGG16. This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) the displacement vectors between the plants. A graph modeling is applied considering each plant position on the image as vertices, and edges are formed between two vertices (i.e. plants). Finally, the edge is classified as pertaining to a certain plantation line based on three probabilities (higher than 0.5): i) in visual features obtained from the backbone; ii) a chance that the edge pixels belong to a line, from the KEM step; and iii) an alignment of the displacement vectors with the edge, also from the KEM step. Experiments were conducted initially in corn plantations with different growth stages and patterns with aerial RGB imagery to present the advantages of adopting each module. We assessed the generalization capability in the other two cultures (orange and eucalyptus) datasets. The proposed method was compared against state-of-the-art deep learning methods and achieved superior performance with a significant margin considering all three datasets. This approach is useful in extracting lines with spaced plantation patterns and could be implemented in scenarios where plantation gaps occur, generating lines with few-to-no interruptions.

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Concerns about methane (CH4) emissions from rice, a staple sustaining over 3.5 billion people globally, are heightened due to its status as the second-largest contributor to greenhouse gases, driving climate change. Accurate quantification of CH4 emissions from rice fields is crucial for understanding gas concentrations. Leveraging technological advancements, we present a groundbreaking solution that integrates machine learning and remote sensing data, challenging traditional closed chamber methods. To achieve this, our methodology involves extensive data collection using drones equipped with a Micasense Altum camera and ground sensors, effectively reducing reliance on labor-intensive and costly field sampling. In this experimental project, our research delves into the intricate relationship between environmental variables, such as soil conditions and weather patterns, and CH4 emissions. We achieved remarkable results by utilizing unmanned aerial vehicles (UAV) and evaluating over 20 regression models, emphasizing an R2 value of 0.98 and 0.95 for the training and testing data, respectively. This outcome designates the random forest regressor as the most suitable model with superior predictive capabilities. Notably, phosphorus, GRVI median, and cumulative soil and water temperature emerged as the model's fittest variables for predicting these values. Our findings underscore an innovative, cost-effective, and efficient alternative for quantifying CH4 emissions, marking a significant advancement in the technology-driven approach to evaluating rice growth parameters and vegetation indices, providing valuable insights for advancing gas emissions studies in rice paddies.

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Accurate assessment of evapotranspiration (ETa) and crop coefficient (Kc) is crucial for optimizing irrigation practices in water-scarce regions. While satellite-based surface energy balance models offer a promising solution, their application to sparse canopies like apple orchards requires specific validation. This study investigated the spatial and temporal dynamics of ETa and Kc in a drip-irrigated 'Pink Lady' apple orchard under Mediterranean conditions over three growing seasons (2012/13, 2013/14, 2014/15). The METRIC model, incorporating calibrated sub-models for leaf area index (LAI), surface roughness (Zom), and soil heat flux (G), was employed to estimate ETa and Kc. These estimates were validated against field-scale Eddy Covariance data. Results indicated that METRIC overpredicted Kc and ETa with errors less than 10 %. These findings highlight the potential of the calibrated METRIC model as a valuable decision-making tool for irrigation management in apple orchards.