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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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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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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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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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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Identifying Thermokarst Lakes Using Deep Learning and High-Resolution Satellite Images

Science of Remote Sensing, Год журнала: 2024, Номер 10, С. 100175 - 100175

Опубликована: Ноя. 2, 2024

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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Evaluation of Deep Learning-Based Water Bodies and Flooded Area Detection with Nanosatellites: The PlanetScope Satellite Imageries and HRNet Model

Korean Journal of Remote Sensing, Год журнала: 2024, Номер 40(5-1), С. 617 - 627

Опубликована: Окт. 31, 2024

Язык: Английский

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Diversity of lakes and ponds in the forest-tundra ecozone: from limnicity to limnodiversity

GIScience & Remote Sensing, Год журнала: 2024, Номер 61(1)

Опубликована: Ноя. 13, 2024

Arctic and subarctic landscapes have unique hydrological limnological features are now experiencing rapid change due to climate warming permafrost thaw. The highly abundant lakes, ponds, rivers across these play an increasingly important role in global biogeochemical cycles sentinels of environmental changes. However, studying remote waters poses challenges for both situ sampling remote-sensing analysis. Here we developed a synergistic strategy that combined PlanetScope Sentinel-2 satellite data estimate limnicity (water fraction per land surface), limnodensity (density water bodies), limnodiversity (optical diversity bodies) along boreal forest-tundra transect, from the non-permafrost continuous zones western Nunavik (Subarctic Canada). Our analyses show this region hosts 335,281 bodies, around 90% 0.0001 0.01 km2 size range. In bedrock outcrops, large bodies were mostly associated with glacially carved depressions (higher limnicity). contrast, small predominately found sedimentary infills valleys limnodensity). discontinuous zone had highest limnodiversity. This was likely thaw (thermokarst), particularly collapse, subsidence, erosion palsas (organic mounds), resulting ponds black- brown-colored waters, lithalsas (mineral brown, light-brown, sometimes white-colored waters. Some limnodense limnodiverse landscapes, although covering only 2 7% total area study region, contained over one-third (34%) number 97% which <0.01 km2; they accounted proportion black-colored (23%), but high brown- (60%) light (92%) throughout region. research underscores utility optical sensing assessing body types evaluating their individual distinct aquatic responses change. dataset may be used improve modeling carbon fluxes by better categorizing affected organic or mineral soil type settings. is factor dictating responses, effects on albedo, feedbacks, ecosystem dynamics framework here applied elsewhere world densities variable properties.

Язык: Английский

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