Satellite Images Research Articles

Neighborhood Resources Associated With Psychological Trajectories and Neural Reactivity to Reward After Trauma

Research on resilience after trauma has often focused on individual-level factors (eg, ability to cope with adversity) and overlooked influential neighborhood-level factors that may help mitigate the development of posttraumatic stress disorder (PTSD). To investigate whether an interaction between residential greenspace and self-reported individual resources was associated with a resilient PTSD trajectory (ie, low/no symptoms) and to test if the association between greenspace and PTSD trajectory was mediated by neural reactivity to reward. As part of a longitudinal cohort study, trauma survivors were recruited from emergency departments across the US. Two weeks after trauma, a subset of participants underwent functional magnetic resonance imaging during a monetary reward task. Study data were analyzed from January to November 2023. Residential greenspace within a 100-m buffer of each participant's home address was derived from satellite imagery and quantified using the Normalized Difference Vegetation Index and perceived individual resources measured by the Connor-Davidson Resilience Scale (CD-RISC). PTSD symptom severity measured at 2 weeks, 8 weeks, 3 months, and 6 months after trauma. Neural responses to monetary reward in reward-related regions (ie, amygdala, nucleus accumbens, orbitofrontal cortex) was a secondary outcome. Covariates included both geocoded (eg, area deprivation index) and self-reported characteristics (eg, childhood maltreatment, income). In 2597 trauma survivors (mean [SD] age, 36.5 [13.4] years; 1637 female [63%]; 1304 non-Hispanic Black [50.2%], 289 Hispanic [11.1%], 901 non-Hispanic White [34.7%], 93 non-Hispanic other race [3.6%], and 10 missing/unreported [0.4%]), 6 PTSD trajectories (resilient, nonremitting high, nonremitting moderate, slow recovery, rapid recovery, delayed) were identified through latent-class mixed-effect modeling. Multinominal logistic regressions revealed that for individuals with higher CD-RISC scores, greenspace was associated with a greater likelihood of assignment in a resilient trajectory compared with nonremitting high (Wald z test = -3.92; P < .001), nonremitting moderate (Wald z test = -2.24; P = .03), or slow recovery (Wald z test = -2.27; P = .02) classes. Greenspace was also associated with greater neural reactivity to reward in the amygdala (n = 288; t277 = 2.83; adjusted P value = 0.02); however, reward reactivity did not differ by PTSD trajectory. In this cohort study, greenspace and self-reported individual resources were significantly associated with PTSD trajectories. These findings suggest that factors at multiple ecological levels may contribute to the likelihood of resiliency to PTSD after trauma.

Urban Green Areas per Resident: Review Study

Abstract: One of the essential elements of landscape planning is the design of urban green areas which is important for community welfare. Data about green area in large cities can be collected from aerial survey or satellite imageries. Analysis can be applied using GIS to determine the relation between urban green area (UGA) per resident in the city when statistics of number of inhabitants in the city are known. The computed UGA per resident can then be compared with respect to standard or recommended values given by: World Health Organization (WHO) or by the United Nations (UN) or any other organization. Studying the distances that people need to walk to the nearest green area should also satisfy a certain limit. This paper is an attempt to outline some published research about finding out the UGA per capita in various world cities.

Data processing for object recognition from satellite images using convolutional neural networks: a case study in solar panel recognition with FORMOSAT-2

Data processing for object recognition from satellite images using convolutional neural networks: a case study in solar panel recognition with FORMOSAT-2

Expansión territorial de la fruticultura agroindustrializada en el sur de Jalisco, México

Objective: to identify the geographical distribution of fruit growing in the Southern region of Jalisco. Methodology: the georeferencing of avocado and berry orchards identified through satellite images provided by the Google Earth system. The resulting polygons were processed with the qgi s software, and we went on to corroborate the information on site. Results: it was estimated that in 2021, in the South of Jalisco, there were almost 40 000 hectares planted with avocado and more than 5 000 hectares ded-icated to the production of berries. In the case of avocado, more than 8 000 hectares had been planted in forest areas, which has created conditions of greater vulnerabili-ty in several parts of the region. Value: this work takes a step towards estimating the amount of land actually dedicated to agro-industrial fruit growing, which is underes-timated in official information. Limitation: the research used satellite images, so it is possible that some orchards were not identified, and it will be necessary to conduct more field work in the area to corroborate the data. Conclusions: since official sources tend to underestimate the size of the area planted with crops such as avocado, it is necessary to conduct further research that will clarify this point in order to help with the implementation of public and private policies, especially where there are mono-cultures that have strong repercussions on the environment. In Southern Jalisco, their effects are increasingly evident.

Remote sensing analysis of base cover of the water in Bontosua Island, Pangkajene and Islands Regency

Background: Among the Spermonde Archipelago's strategically important and predominantly medieval islands is Bontosua Island. In order to preserve the health and vitality of the ecosystems, the local communities on this island actively participate in environmental conservation. With the help of this study, the bottom cover of the waters surrounding Bontosua Island, Pangkajene Regency, and the archipelago will be mapped, and the relationships between various types of bottom cover will be ascertained. Methods: From March to September of 2022, the study was carried out. Images from the Sentinel-2A satellite were obtained on July 29, 2021. Using the Lyzenga procedure and the values ki/kj = 0.876875437 and a = -0.280796034, water column adjustment was carried out. The Unsupervised Classification approach was used to classify the images, and the Rapid Reef Assessment (RRA) method was used to verify the bottom water cover in the field. Findings: With a total area of 69.42 hectares, the results revealed seven dominating objects: deep sea, sand, dead coral, dead coral with algae, and coral debris (11.17 ha), living coral (17.32 ha), broken coral (12.73 ha), sand (4.43 ha), seagrass (12.61 ha), and dead coral with algae (11.16 ha). In the waters surrounding Bontosua Island, live coral constituted the greatest portion of the bottom cover, accounting for 25% of the total, while sand made up the least amount, just 6%. Novelty/Originality of this Study: The study on Bontosua Island's water bottom cover is novel due to its use of Sentinel-2A satellite imagery combined with the Lyzenga procedure and Unsupervised Classification approach, offering high-resolution mapping of underwater ecosystems. Additionally, the study's comprehensive classification and verification process through RRA provides detailed insights into the distribution and health of various bottom cover types, contributing to enhanced environmental conservation efforts in the Spermonde Archipelago.

Harvesting Knowledge: Data Science and Machine Learning Techniques for Evaluating Pesticide Impact in Vegetable Organic Farming

Abstract: The integration of data science and machine learning is revolutionizing the assessment of pesticide impact in organic vegetable farming. This review explores methodologies, applications, and research examples showcasing the transformative potential of data-driven approaches. Remote sensing, including satellite imagery and drones, is essential for monitoring crop health and detecting pesticide impacts on vegetable crops like tomatoes, lettuce, and red peppers. By synthesizing research and trends, the review underscores technology's significance in informed decision-making for sustainable vegetable organic farming practices. Spectral analysis and vegetation indices quantify changes in crop health, informing pesticide efficacy and environmental impact. Sensor networks and IoT devices allow real-time monitoring of environmental conditions and pesticide dynamics, optimizing application practices to minimize contamination while maximizing yield. Machine learning, particularly decision tree-based models like random forests, predicts and mitigates pesticide impacts by analyzing complex datasets. Incorporating variables such as soil type and climate, these models accurately forecast pesticide fate, aiding in targeted mitigation strategies. Deep learning, such as convolutional neural networks (CNNs), identifies pesticide stress symptoms from digital images of vegetable leaves, facilitating rapid intervention. Challenges like data integration and model interpretability persist, yet ongoing research addresses these through data fusion and explainable AI. This review emphasizes the progress in leveraging data science and machine learning for pesticide impact evaluation in organic vegetable farming. By synthesizing research and trends, it offers insights for future sustainable agriculture applications.

Research and Application of Destriping Algorithm based on Weighted Block Sparse Representation to FengYun Satellite Imager Data

Abstract In remote sensing imaging systems, stripe noise is a pervasive issue primarily caused by the inconsistent response of multiple detectors. Stripe noise not only affects image quality but also severely hinders subsequent quantitative derived products and applications. Therefore, it is crucial to eliminate stripe noise while preserving detailed structure information in order to enhance image quality. Although existing destriping methods have achieved certain effects to some extent, they still face problems such as loss of image details, image blur, and ringing artifacts. To address these issues, this study proposes an image stripe correction algorithm based on weighted block sparse representation. This research applies techniques such as differential low-rank constraint and edge weight factor to remove stripe noise while retaining image detail information. The algorithm also uses the alternating direction method of multipliers (ADMM) to solve the MCP regularized least squares optimization problem model, improving the processing efficiency of the model. The results of this study have been applied and validated in imager data from the Medium Resolution Spectral Imager (MERSI-II) onboard FengYun-3D satellite, the Multi-channel Scanning Radiometer (AGRI) onboard FengYun-4A satellite and Precipitation Microwave Radiometer (MWRI–RM) onboard FengYun-3G. Compared to typical stripe correction methods, the proposed method achieves better stripe removal while preserving image detail information. The destriped image data can be used to generate high-quality quantitative products for various applications. Overall, by combining insights from prior research and innovative techniques, this study provides a more effective and robust solution to the stripe noise problem in remote sensing and weather forecast.

Is Your Training Data Really Ground Truth? A Quality Assessment of Manual Annotation for Individual Tree Crown Delineation

For the accurate and automatic mapping of forest stands based on very-high-resolution satellite imagery and digital orthophotos, precise object detection at the individual tree level is necessary. Currently, supervised deep learning models are primarily applied for this task. To train a reliable model, it is crucial to have an accurate tree crown annotation dataset. The current method of generating these training datasets still relies on manual annotation and labeling. Because of the intricate contours of tree crowns, vegetation density in natural forests and the insufficient ground sampling distance of the imagery, manually generated annotations are error-prone. It is unlikely that the manually delineated tree crowns represent the true conditions on the ground. If these error-prone annotations are used as training data for deep learning models, this may lead to inaccurate mapping results for the models. This study critically validates manual tree crown annotations on two study sites: a forest-like plantation on a cemetery and a natural city forest. The validation is based on tree reference data in the form of an official tree register and tree segments extracted from UAV laser scanning (ULS) data for the quality assessment of a training dataset. The validation results reveal that the manual annotations detect only 37% of the tree crowns in the forest-like plantation area and 10% of the tree crowns in the natural forest correctly. Furthermore, it is frequent for multiple trees to be interpreted in the annotation as a single tree at both study sites.

Revolutionizing Agriculture: Innovative Techniques, Applications, and Future Prospects in Precision Farming

Precision agriculture (PA) represents a transformative approach to farming, employing advanced technologies to enhance productivity, efficiency, and sustainability. This review article provides an in depth analysis of the latest innovations in PA techniques, their diverse applications, and future directions. Precision agriculture is revolutionizing the agricultural landscape by integrating sophisticated tools such as GPS, remote sensing, Internet of Things (IoT), and big data analytics. These technologies enable farmers to monitor and manage variability in crop production meticulously, optimize the use of inputs, and enhance overall farm management practices. The key innovations in PA include the development and application of Geographic Information Systems (GIS) and Global Positioning Systems (GPS), which facilitate accurate mapping and variable rate technology (VRT) for site specific input management. Remote sensing technologies, encompassing both satellite imagery and UAVs (unmanned aerial vehicles), provide critical insights into crop health, soil conditions, and weather patterns, allowing for proactive and informed decision making. The integration of IoT in agriculture involves deploying sensors and connected devices to monitor soil moisture, temperature, and other environmental parameters in real time. This integration supports precision irrigation, climate monitoring, and efficient resource utilization. Big data analytics further enhances PA by processing vast amounts of data to generate actionable insights, enabling predictive analytics and decision support systems (DSS) that aid in optimizing farming operations. The article explores the applications of these advanced techniques in crop management, resource use optimization, and environmental stewardship. Examples include variable rate application of fertilizers, precision irrigation systems, and automated machinery such as drones and robotic harvesters. These innovations lead to significant improvements in crop yields, resource efficiency, and sustainability. Moreover, the review addresses the challenges associated with the adoption and implementation of PA technologies. These challenges include data management complexities, high initial costs, limited accessibility, and the need for technical expertise. The article discusses potential solutions such as cloud computing, machine learning algorithms, government subsidies, collaborative models, and comprehensive training programs to mitigate these barriers, the review highlights the integration of advanced technologies like artificial intelligence (AI), blockchain, and enhanced connectivity through 5G networks as pivotal developments that will further revolutionize precision agriculture. AI and machine learning will enhance predictive modeling and automated decision making, while blockchain will ensure transparency and traceability in supply chains. Enhanced connectivity will facilitate real time monitoring and collaborative platforms, driving efficiency and innovation in farming practices.

Advanced remote sensing technologies for tracking landscape changes and environmental conditions

The integration of cutting-edge remote sensing technologies, biophysical principles, and advanced spatial statistics enables innovative landscape analysis across various spatial and temporal scales. Traditional approaches relied on classification methods and indices derived from multi-spectral imagery to assess landscape degradation. However, modern techniques can extract biophysical indices like leaf area index and canopy chemistry from satellite imagery. Long-term remote sensing archives (e.g., Landsat, AVHRR) facilitate retrospective studies of landscape changes and trajectories. Recent advancements in sensors and analysis techniques, such as sub-pixel classifications and continuous fields, have improved the accuracy of variable retrieval (e.g., Albedo, chlorophyll concentration). These developments enable powerful monitoring tools for land use/cover change detection, leading to a better understanding of landscape dynamics and the mapping of previously unexplored features. However, a trade-off exists between high spatial and high temporal resolution depending on the platform used.

Monitoring of Channel Deformations of the Lower Dniester Using Topographic Maps and Satellite Imagery with the Implementation of GIS Technologies

Rivers are one of the most complex formations on the Earth’s surface; they are connected with the deep structure of the Earth, the landscape environment, and unpredictable weather cataclysms. The study aims to investigate the peculiarities of the geological structure and tectonics of the Lower Dniester region, examine the types of meanders, determine the horizontal displacements of the riverbed, and assess the impact of anthropogenic factors on the riverbed and riparian areas. The methodology is based on the use of tectonic maps, geological maps of Quaternary deposits, historical topographic maps, and satellite images to determine the long-term horizontal displacements of the riverbed, identify meander types, and study the meandering characteristics and their relationships with tectonic structures. Horizontal riverbed displacements were measured at 76 points using data from the 1980 map and a Sentinel-2 image obtained in 2020. For the purpose of studying the types of meanders and their analysis, it is proposed to use the characteristics of meanders and their relationships. This approach will allow for more efficient analysis and prediction of changes in channel processes under the influence of natural and anthropogenic factors across various temporal scales. The research shows that the Lower Dniester region is influenced by the tectonics of internal structures and the seismically active Vrancea zone in Romania. Additionally, a comparison of cartographic data from 1880 and 1980 and satellite images from 2020 allows us to assess how the territory of the Lower Dniester riparian area has changed under the influence of anthropogenic factors.

Recent Innovation in Precision Agriculture and their Impact on Crop and Soil Health: A Compressive Review

The maintenance of soil fertility and on-farm research or demonstrations might be enhanced by precision agricultural technologies. Using state-of-the-art technology, precision agriculture boosts agricultural output without negatively affecting the environment. Utilising cutting-edge technology and data analysis, precision agriculture aims to boost production, minimise waste, and maximise crop yields. This might be a viable approach to addressing some of the main problems facing modern agriculture, such feeding an expanding global population while lessening its impact on the environment. The application of precision agriculture starts with the collection of real-time data from various sources, such as satellite imagery, remote sensing, global positioning systems, geographic information systems, drones, soil sensors, and weather stations. Precision agriculture has become essential in addressing the challenges posed by a growing global population, climate change, and resource constraints. Recognising within-field variability and providing chances for differentiating treatment of sections within a field or industrial unit are what fuel demand for precision agriculture. Precision agriculture technology plays an important part in sustainable soil and crop management in modern agriculture by lowering crop production inputs and managing lands in an ecologically responsible way.

Evaluating Lakes Boundary Change: A Case Study from Ward Number 26 of Pokhara Metropolitan City

Lakes are crucial for their multi-faceted services, including drinking water, irrigation, tourism, and more. Understanding their status, including area coverage, is important, and continuous monitoring is essential. Lakes can be monitored through various approaches, ranging from traditional ground-based surveys to advanced earth observation satellites. This study aimed to monitor several lakes in Ward Number 26 of Pokhara Metropolitan City, including Khaste, Neurini, and Gunde Lakes, using high-resolution cadastral maps, ground-based field surveys, and Sentinel-2 earth observation satellites. Additionally, focus group discussions and key informant interviews were conducted. Supervised land use and land cover classification and the Normalized Difference Water Index method were employed to determine the area of each lake in 2017 and 2021. The focus group discussions and key informant interviews investigated factors affecting lake dynamics. The study found that climate change, land use change, floods, water abstraction, and landslides are major factors contributing to changes in lake areas. The results showed that the satellite-derived lake boundaries were smaller than those indicated on the cadastral map. Ground surveys using total station equipment also revealed that Khaste Lake shrunk by 6122.73 m² (0.01 km²), Neurini Lake lost 474.95 m² (0.00005 km²), and Gunde Lake shrunk by 4226 m² (0.004 km²) due to encroachment. Although satellite images are useful for detecting large-scale changes, this study indicates that the lake boundary areas identified through satellite imagery were smaller than those delineated by cadastral maps and ground surveys. This suggests that remote sensing analysis alone is insufficient for studying small lake boundary changes and should be combined with ground-based surveys. Therefore, a combination of satellite images and ground-based surveys is recommended for accurately and comprehensively detecting changes in lake areas.

Detection the Dust Storms Using MODIS Reflection Mode

In previous years, most regions of the Middle East suffered from frequent dust storms, Iraq at the top of that where Iraq was classified as the fifth most vulnerable country to climate change according to the Paris Climate Agreement; the frequent dust storms are considered the most prominent effects of climate change, which are a result of lack of rain and drought, and thus desertification and desertification. The storms appear when the winds blow. This research reviews using of space data, MODIS (Moderate Resolution Imaging Spectroradiometer, satellite images), and its two platforms, Terra and Aqua, to monitor storms movement and determine the starting point source using the PCA (Principal Component Analysis algorithm) in ENVI and sub-software MATLAB for (MOD09GA) and (MYD09GA) that represents the reflectivity daily monitoring, the first layer from HDF file of MODIS data red band, 620-670 nm, was used. The period was studied in research in May of 2022 with the kernel of PCA containing five bands represented by the day of the storm and two days before and after the storm for the study area located Coordinates: 35.0468, 43.0305. The results determined that the source of dust storms are cross-border storms that are not local in origin; the estimated area of the storm that covered large regions of Iraq by (351353.45) km2 for 2022-5-5, (308024.9.km2) for the day 2022-5-16 and with an estimated area (326933.35 km2) for the day 2022-5-23.

Blue carbon assessment in Avicennia marina sediments and vegetation along the Red Sea Coast of Egypt: Improving methods and insights

Mangrove forests play a crucial role in the coastal environment. A plethora of research is focusing on mangroves’ role in climate mitigation through sequestering and storing CO2. This study aims to assess the organic carbon (OC) stocks and carbon sequestration rate (CSR) of the largest stands of mangroves in the Red Sea Coast of Egypt, to evaluate their significance in climate mitigation strategies. Intensive sampling was conducted in mangrove stands of Safaga, Qulaan, and Hamata to investigate OC and sediment properties along depth intervals to 1 m deep. Additionally, OC stored in the above- and below-ground biomass was estimated using Landsat 9 OLI/TIRS satellite images. TOC content (∼0.50%) and stocks in the sediments (54.49 ± 31.71–86.00 ± 29.00 MgC/ha) were very low compared to the global average for mangrove stands, and comparable to the mangrove forest in the Middle East region. This can be attributed to the desert and semi-desert conditions, limited riverine input, and low precipitation rates. Moreover, the above-ground biomass, estimated from Normalized Difference Vegetation Index, exhibited extremely low OC storage. Therefore, the sediment acted as the main pool of organic carbon in these mangrove ecosystems. The research findings revealed that mangrove stands in Egypt exhibit low CSR, ranging from 11.94 to 18.02 g C m−2 year−1. Additionally, the study determined that the stable nature of mangrove stands in Egypt makes their annual CO2 emissions negligible. Despite local studies suggesting otherwise, it is highly unlikely that mangroves in Egypt contribute to the climate mitigation related to CO2 sequestration. Therefore, it is important to implement restoration plans to ensure the preservation of other ecological services provided by mangroves along the Red Sea Coast.

Changing Pattern and Drivers of Land Use and Land Cover in Bagmati Province of Nepal

Land use and land cover (LULC) represents the physical component of the Earth's environment, and often changes as a result of natural and anthropogenic activities. This study carried out changing patterns and orientations of LULC types of Bagmati province of Nepal by using satellite images between 1996 and 2016. The study applied a classification scheme through support vector machines (SVM) for the period from 1996 to 2006, and 2006 to 2016. Our study explored that built-up area expansion was the major change witnessed which increased from 85.25 km2 in 1996 and sharply increased and labeled 250.06 km2 in 2016. Similarly, the forest land of the province increased by 329.02 km2 between 1996 and 2016. The status of cultivated land was found slightly decreasing trend, which was noted at 6113.50 km2 in 1996 and labeled at 5725.82 km2 in 2016. The shrubland of the province declined by 272.03 km2 from 1851.67 km2 in 1996 to 1579.64 km2 in 2016. However, the status of snow/ ice cover found at fluctuated form, which was noted 774.27 km2 in 1996, 1297.43 km2 in 2006, and 828.92 km2 in 2016. The increasing population trend is the main driver of the high rate of built-up area expansion and contraction of the cultivated land. Community forestry programs highly supported the expansion of forest cover. The proper implementation and improvement of the existing policy are the major concerns for sustainable LULC management in the province.

Spatiotemporal Analysis of Jorgo Wato Forest Cover Changes, West Wallagga, Oromia, Ethiopi

A study was conducted in the Jorgo Wato forest, located in the West Wallagga zone and extending to the Buno Bedelle zone of Oromia National Regional State, with the objective of assessing the dynamics of forest cover change in the Jorgo Wato forest over the last three decades (1986–2016). In order to assess forest cover changes, the whole study period was categorized into three periods: 1986–1995, 1995–2006, and 2006–2016. Satellite images from Landsat TM, ETM+, and OLI/TIRS were used in this study. Support Vector Machines for supervised classification and post-classification were used for image classification, with results showing an overall accuracy of up to 99.49%, maximum producer’s accuracies of up to 99.80, and User’s accuracies of up to 100%, while the Kappa coefficient ranged between 96.98% and 99.17%. The result of the change analysis revealed that forests changed from 67.62% in 1986 to 74.77% in 2016, with an average rate of change of 20.13ha/year from 1986 to 2016, whereas farmland changed from 19.60% in 1986 to 12.28% in 2016, with a variation through 30 years and an average rate of change of -20.604ha/year from 1986-2016. Changes in forest cover increment have come from the expansion of coffee plantations and plantations of different tree species by the communities and by Oromia Forest and Wildlife Enterprise till now. Monoculture plantations cannot take the place of a natural forest and cannot support a lot of biodiversity, which is crucial for the management of natural resources. Future maintenance of the region's environmental equilibrium will require a method of combining both systems and concentrating more diverse species.

Leveraging Copernicus for Ice Block Detection: A Step Towards Safer Arctic Navigation

Background The Arctic is becoming more accessible due to ice melting, posing challenges for navigation due to floating ice blocks. Detecting these ice blocks is crucial for ensuring the safety of vessels navigating these waters. Methods This study introduces a novel method for detecting ice blocks in the Arctic using Artificial Intelligence (AI) and Synthetic Aperture Radar (SAR) satellite images from the Sentinel-1 mission of the Copernicus program. Our approach combines state-of-the-art image segmentation and deep learning techniques. We utilize a Convolutional Neural Network (CNN) classifier to accurately locate ice blocks within the images and retrieve their geographic coordinates. The method's performance is validated using precision, recall, and F1-score metrics. Results Our CNN classifier demonstrates robust performance in detecting ice blocks, validated through precision, recall, and F1-score metrics. The practical application of our technology was showcased in the AI-ARC project’s Arctic demo, receiving positive feedback from coast guards across various European countries. The system provides near-real-time alerts about detected ice blocks, allowing for timely route adjustments and reducing collision risks. Conclusions The developed system significantly contributes to Arctic navigation safety by providing accurate and timely detection of ice blocks. This work underscores the transformative potential of AI in environmental monitoring and maritime safety. Future refinements will be based on user feedback and advancements in AI technology, enhancing the system's effectiveness and reliability.

Extreme precipitation events over the east coast of northeast Brazil: Synoptic study and MPAS simulation

In the present study two extreme events that occurred in the East Coast of Northeast Brazil (ENEB) during 2022 and 2023 were evaluated. These events are becoming increasingly frequent in all regions of Brazil, associated with significant material and human losses, emphasizing the significance of a deeper comprehension of these events. ERA5 global reanalysis data, GOES-16 satellite imagery and pluviometric stations were used for the analysis. Model simulations were also conducted using the Model for Prediction Across Scales (MPAS) with variable resolution (60–3 km). Both events corresponded to Easterly Wave Disturbances (EWDs) that occurred under opposite large-scale conditions of the ENSO cycle, since extreme events are becoming increasingly frequent in all regions of Brazil and could be responsible for significant material and human losses. Thus, an emphasis was given to characterize the synoptic conditions. Both analyzed cases occurred along the ENEB, specifically over the Alagoas state. The trough axis penetrating the studied area was observed on both examined dates, with a very characteristic relative vorticity of this tropical disturbance. In general, moisture convergence resulted from the high flow of moisture prevailing over the region combined with upward movements caused by the trough present at low levels, which combined with local factors in the region such as topography, contributed to the increase in rainfall over the study area in both analyzed cases. The MPAS showed excellent spatial representation when compared to station data, highlighting intense precipitation over parts of Alagoas.

NTL-Unet: A Satellite-Based Approach for Non-Technical Loss Detection in Electricity Distribution Using Sentinel-2 Imagery and Machine Learning

This study introduces an orbital monitoring system designed to quantify non-technical losses (NTLs) within electricity distribution networks. Leveraging Sentinel-2 satellite imagery alongside advanced techniques in computer vision and machine learning, this system focuses on accurately segmenting urban areas, facilitating the removal of clouds, and utilizing OpenStreetMap masks for pre-annotation. Through testing on two datasets, the method attained a Jaccard index (IoU) of 0.9210 on the training set, derived from the region of France, and 0.88 on the test set, obtained from the region of Brazil, underscoring its efficacy and resilience. The precise segmentation of urban zones enables the identification of areas beyond the electric distribution company’s coverage, thereby highlighting potential irregularities with heightened reliability. This approach holds promise for mitigating NTL, particularly through its ability to pinpoint potential irregular areas.