Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(17)
Published: Aug. 27, 2024
Language: Английский
Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(17)
Published: Aug. 27, 2024
Language: Английский
Construction and Building Materials, Journal Year: 2024, Volume and Issue: 447, P. 138149 - 138149
Published: Aug. 31, 2024
Language: Английский
Citations
4Remote Sensing, Journal Year: 2024, Volume and Issue: 16(20), P. 3842 - 3842
Published: Oct. 16, 2024
The black soil region experiences complex erosion due to natural processes and intense human activities, leading degradation adverse ecological agricultural impacts. However, the complexities involved in quantifying regional poses remarkable challenges accurately assessing current status of for effective conservation. To solve this issue, we proposed a new method monitoring using Interferometric synthetic aperture radar (InSAR) technology machine learning algorithms within Google Earth Engine platform. not only enables regional-scale monitoring, but also ensures high accuracy measurement (millimeter-level). susceptibility study area (Yanshou County, Heilongjiang Province, Northeastern China) was classified random forest refine monitored predicted erosion. results indicate that five-year (2016–2021) deformation Yanshou County −11.08 mm, with significant mean cumulative −8.08 mm yr−1 occurring 2017. driving factor analysis shows subject compound effect water freeze–thaw erosion, closely related crop phenological stages. indicates 73.3% susceptible higher probability river areas, at altitudes, on steep slopes. good vegetation cover can reduce risk some extent. This offers perspective China. holds potential future expansion monitor larger thereby guiding strategies development protection agriculturally important soil.
Language: Английский
Citations
2IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2024, Volume and Issue: 17, P. 15136 - 15147
Published: Jan. 1, 2024
Language: Английский
Citations
1Remote Sensing, Journal Year: 2024, Volume and Issue: 16(17), P. 3264 - 3264
Published: Sept. 3, 2024
Tailings ponds are recognized as significant sources of potential man-made debris flow and major environmental disasters. Recent frequent tailings dam failures growing trends in fine outputs underscore the critical need for innovative monitoring safety management techniques. Here, we propose an approach that integrates UAV photogrammetry with convolutional neural networks (CNNs) to extract beach line indicators (BLIs) conduct enhanced evaluations. The significance real 3D geometry construction numerical analysis is investigated. results demonstrate optimized You Only Look At CoefficienTs (YOLACT) model outperforms recognizing boundary line, achieving a mean Intersection over Union (mIoU) 72.63% Pixel Accuracy (mPA) 76.2%. This shows promise future integration autonomously charging UAVs, enabling comprehensive coverage automated BLIs. Additionally, anti-slide seepage stability evaluations impacted by shape water condition configuration. proposed provides more conservative calculations, suggesting simplified 2D modeling may underestimate stability, potentially affecting designs regulatory decisions. Multiple methods suggested cross-validation. crucial balancing regulations economic feasibility, helping prevent excessive unsustainable burdens on enterprises advancing towards goal zero harm people environment management.
Language: Английский
Citations
1Structures, Journal Year: 2024, Volume and Issue: 71, P. 108057 - 108057
Published: Dec. 16, 2024
Language: Английский
Citations
1Environmental Earth Sciences, Journal Year: 2024, Volume and Issue: 83(17)
Published: Aug. 27, 2024
Language: Английский
Citations
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