Seasonal Land Use and Land Cover Mapping in South American Agricultural Watersheds Using Multisource Remote Sensing: The Case of Cuenca Laguna Merín, Uruguay

Sensors, Год журнала: 2025, Номер 25(1), С. 228 - 228

Опубликована: Янв. 3, 2025

Recent advancements in Earth Observation sensors, improved accessibility to imagery and the development of corresponding processing tools have significantly empowered researchers extract insights from Multisource Remote Sensing. This study aims use these technologies for mapping summer winter Land Use/Land Cover features Cuenca de la Laguna Merín, Uruguay, while comparing performance Random Forests, Support Vector Machines, Gradient-Boosting Tree classifiers. The materials include Sentinel-2, Sentinel-1 Shuttle Radar Topography Mission imagery, Google Engine, training validation datasets quoted methods involve creating a multisource database, conducting feature importance analysis, developing models, supervised classification performing accuracy assessments. Results indicate low significance microwave inputs relative optical features. Short-wave infrared bands transformations such as Normalised Vegetation Index, Surface Water Index Enhanced demonstrate highest importance. Accuracy assessments that various classes is optimal, particularly rice paddies, which play vital role country’s economy highlight significant environmental concerns. However, challenges persist reducing confusion between classes, regarding natural vegetation versus seasonally flooded vegetation, well post-agricultural fields/bare land herbaceous areas. Forests Trees exhibited superior compared Machines. Future research should explore approaches Deep Learning pixel-based object-based integration address identified challenges. These initiatives consider data combinations, including additional indices texture metrics derived Grey-Level Co-Occurrence Matrix.

Язык: Английский

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A comparative study of remotely sensed reservoir monitoring across multiple land cover types

The Science of The Total Environment, Год журнала: 2024, Номер 948, С. 174678 - 174678

Опубликована: Июль 9, 2024

Язык: Английский

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Fusing aerial photographs and airborne LiDAR data to improve the accuracy of detecting individual trees in urban and peri-urban areas

Urban forestry & urban greening, Год журнала: 2025, Номер 105, С. 128696 - 128696

Опубликована: Янв. 30, 2025

Язык: Английский

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Efficient glacial lake mapping by leveraging deep transfer learning and a new annotated glacial lake dataset

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 133072 - 133072

Опубликована: Март 1, 2025

Язык: Английский

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Spatial analysis of interannual changes in thermokarst lakes across the Qinghai-Tibet Plateau with Time-Series SAR imagery

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 133269 - 133269

Опубликована: Апрель 1, 2025

Язык: Английский

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Adaptive YOLOv6 with spatial Transformer Networks for accurate object detection and Multi-Angle classification in remote sensing images

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 127796 - 127796

Опубликована: Апрель 1, 2025

Язык: Английский

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Groundwater Arsenic Contamination in Nepal

World Water Policy, Год журнала: 2025, Номер unknown

Опубликована: Май 13, 2025

ABSTRACT Arsenic contamination in the drinking water of Nepal, particularly Terai region, poses a serious public health challenge, as groundwater serves primary source for millions peoples. This study investigates prevalence, sources, and consequences arsenic contamination, employing recent data scientific analyses. Chronic exposure to is associated with severe complications, including skin lesions, various forms cancer, cardiovascular diseases, disproportionately affecting vulnerable populations. Despite ongoing efforts mitigate exposure, challenges remain effective identification management contaminated sources. Beyond accumulates soil crops, jeopardizing food safety. While traditional mapping methods are expensive, machine learning offers cost‐effective, high‐resolution solutions. Portable field kits enable rapid detection water, but proper disposal filtration waste remains challenge. Iron‐based biosand filters present viable interim solution comply standards set by Government while also effectively eliminating bacteria viruses. Nevertheless, establishment sustainable treatment systems clustered communities region imperative. paper shows necessity comprehensive testing, enhanced awareness, practices safeguard Nepal.

Язык: Английский

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Geospatial artificial intelligence for detection and mapping of small water bodies in satellite imagery

Environmental Monitoring and Assessment, Год журнала: 2025, Номер 197(6)

Опубликована: Май 16, 2025

Язык: Английский

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Integrating UAV-RGB Spectral Indices by Deep Learning Model Enables High-Precision Olive Tree Segmentation Under Small Sample

Forests, Год журнала: 2025, Номер 16(6), С. 924 - 924

Опубликована: Май 31, 2025

Accurate maps of olive plantations are very important to monitor and manage the rapid expansion cultivation. Nevertheless, in situations where data samples limited study area is relatively small, low spatial resolution satellite imagery poses challenges accurately distinguishing trees from surrounding vegetation. This presents an automated extraction model for accurate identification using unmanned aerial vehicle RGB (UAV-RGB) imagery, multi-index combinations, deep learning algorithm based on ENVI-Net5. The combined use Lightness, Normalized Green-Blue Difference Index (NGBDI), Modified Vegetation (MGBVI) indices effectively capture subtle spectral differences between vegetation, enabling more precise classification. Study results indicate that proposed minimizes omission misclassification errors through incorporating ENVI-Net5 three indices, especially differentiating other Compared conventional models such as Random Forest (RF) Support Vector Machine (SVM), method yields highest metrics—overall Accuracy (OA) 0.98, kappa coefficient 0.96, producer’s accuracy (PA) 0.95, user’s (UA) 0.92. These values represent improvement 7%–8% OA 15%–17% over baseline models. Additionally, highlights sensitivity performance iterations, underlining importance selecting optimal number iterations achieving peak accuracy. research provides a valuable technical foundation effective monitoring plantations.

Язык: Английский

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Machine-Learning Approach for Identifying Arsenic-Contamination Hot Spots: The Search for the Needle in the Haystack

ACS ES&T Water, Год журнала: 2024, Номер 4(8), С. 3110 - 3114

Опубликована: Июль 15, 2024

Язык: Английский

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