Landsat monitoring reveals the history of river organic pollution across China during 1984-2023

Water Research, Journal Year: 2025, Volume and Issue: unknown, P. 123210 - 123210

Published: Jan. 1, 2025

Language: Английский

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Inferring Water Quality in the Songhua River Basin Using Random Forest Regression Based on Satellite Imagery and Geoinformation

Hydrology, Journal Year: 2025, Volume and Issue: 12(3), P. 61 - 61

Published: March 17, 2025

Maintaining high water quality is essential not only for human survival but also social and ecological safety. In recent years, due to the influence of activities natural factors, has significantly deteriorated, effective monitoring urgently needed. Traditional requires substantial financial investment, whereas remote sensing random forest model reduces operational costs achieves a paradigm shift from discrete sampling points spatially continuous surveillance. The was adopted establish inversion three parameters (conductivity, total nitrogen (TN), phosphorus (TP)) during growing period (May September) 2020 2022 in Songhua River Basin (SRB), using Landsat 8 imagery China’s national section data. Model verification shows that R2 conductivity 0.67, followed by TN at 0.52 TP 0.47. results revealed downstream SRB (212.72 μS/cm) higher than upstream (161.62 μS/cm), with concentrations exhibiting similar increasing pattern. This study significant improving conservation health SRB.

Language: Английский

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A Summary of Recent Advances in the Literature on Machine Learning Techniques for Remote Sensing of Groundwater Dependent Ecosystems (GDEs) from Space

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(8), P. 1460 - 1460

Published: April 19, 2025

While groundwater-dependent ecosystems (GDEs) occupy only a small portion of the Earth’s surface, they hold significant ecological value by providing essential ecosystem services such as habitat for flora and fauna, carbon sequestration, erosion control. However, GDE functionality is increasingly threatened human activities, rainfall variability, climate change. To address these challenges, various methods have been developed to assess, monitor, understand GDEs, aiding sustainable decision-making conservation policy implementation. Among these, remote sensing advanced machine learning (ML) techniques emerged key tools improving evaluation dryland GDEs. This study provides comprehensive overview progress made in applying ML algorithms assess monitor It begins with systematic literature review following PRISMA framework, followed an analysis temporal geographic trends applications research. Additionally, it explores different their across types. The paper also discusses challenges mapping GDEs proposes mitigation strategies. Despite promise studies, field remains its early stages, most research concentrated China, USA, Germany. enable high-quality classification at local global scales, model performance highly dependent on data availability quality. Overall, findings underscore growing importance potential geospatial approaches generating spatially explicit information Future should focus enhancing models through hybrid transformative techniques, well fostering interdisciplinary collaboration between ecologists computer scientists improve development result interpretability. insights presented this will help guide future efforts contribute improved management

Language: Английский

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Review of Recent Advances in Remote Sensing and Machine Learning Methods for Lake Water Quality Management

Remote Sensing, Journal Year: 2024, Volume and Issue: 16(22), P. 4196 - 4196

Published: Nov. 11, 2024

This review examines the integration of remote sensing technologies and machine learning models for efficient monitoring management lake water quality. It critically evaluates performance various satellite platforms, including Landsat, Sentinel-2, MODIS, RapidEye, Hyperion, in assessing key quality parameters chlorophyll-a (Chl-a), turbidity, colored dissolved organic matter (CDOM). highlights specific advantages each platform, considering factors like spatial temporal resolution, spectral coverage, suitability these platforms different sizes characteristics. In addition to this paper explores application a wide range models, from traditional linear tree-based methods more advanced deep techniques convolutional neural networks (CNNs), recurrent (RNNs), generative adversarial (GANs). These are analyzed their ability handle complexities inherent data, high dimensionality, non-linear relationships, multispectral hyperspectral data. also discusses effectiveness predicting parameters, offering insights into most appropriate model–satellite combinations scenarios. Moreover, identifies challenges associated with data quality, model interpretability, integrating imagery models. emphasizes need advancements fusion techniques, improved generalizability, developing robust frameworks multi-source concludes by targeted recommendations future research, highlighting potential interdisciplinary collaborations enhance sustainable management.

Language: Английский

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Uncertainty assessment of optically active and inactive water quality parameters predictions using satellite data, deep and ensemble learnings

Journal of Hydrology, Journal Year: 2024, Volume and Issue: unknown, P. 132091 - 132091

Published: Oct. 1, 2024

Language: Английский

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Balanced hydropower and ecological benefits in reservoir-river-lake system: An integrated framework with machine learning and game theory

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 373, P. 123746 - 123746

Published: Dec. 17, 2024

Language: Английский

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