Developing the recommendations for restoration of Ashtamudi Lake, Kerala, India, by data analysis based on a novel water body index using Google Earth Engine

Environmental Science and Pollution Research, Год журнала: 2025, Номер unknown

Опубликована: Март 3, 2025

Язык: Английский

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A state-of-the-art review on the quantitative and qualitative assessment of water resources using google earth engine

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(12)

Опубликована: Ноя. 28, 2024

Язык: Английский

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Impacts of Climate Change and Human Activity on Lakes around the Depression of Great Lakes in Mongolia

Land, Год журнала: 2024, Номер 13(3), С. 310 - 310

Опубликована: Фев. 29, 2024

The western region of Mongolia is characterized by an arid climate and a fragile ecological environment. It sensitive zone in response to global change one the major sources dust globally. This home numerous lakes, their dynamic changes not only reflect variations but also have implications for environment quality. In this study, Landsat images were used as data source, Google Earth Engine (GEE) was employed extract lakes with area larger than 1 km2 from 1992 2021. spatiotemporal characteristics lake water (LWA) analyzed, structural equation model applied attribute changes. results indicate overall trend increasing followed decrease study area. Specifically, provinces Khovd Gobi-Altai exhibited decreasing trend, while Uvs Zavkhan showed trend. Three typical types namely, alpine throughflow terminal all decrease. analysis driving forces behind reveals that human activities primarily exert indirect influences on each province. lead soil moisture, which significant explanatory power Regarding serves primary force are main lakes.

Язык: Английский

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Spatiotemporal Variation, Meteorological Driving Factors, and Statistical Models Study of Lake Surface Area in the Yellow River Basin

Water, Год журнала: 2024, Номер 16(10), С. 1424 - 1424

Опубликована: Май 16, 2024

The surface area changes of 151 natural lakes over 37 months in the Yellow River Basin, based on remote sensing data and 21 meteorological indicators, employing spatial distribution feature analysis, principal component analysis (PCA), correlation multiple regression identify key factors influencing these variations their interrelationships. During study period, lake averages were from 0.009 km2 to 506.497 km2, with standard deviations ranging 0.003 184.372 km2. coefficient variation spans 3.043 217.436, indicating considerable variability stability. Six primary determined have a significant impact fluctuations: 24 h precipitation, maximum daily hours sunshine, wind speed, minimum relative humidity, source region generally showed positive correlation. For speed (m/s), 28 correlations, five twenty-three negative coefficients −0.34 −0.63, average −0.47, an overall between speed. precipitation (mm), 36 had showing correlation, larger lakes. Furthermore, 117 sufficient model, predictive capabilities various models for showcased distinct advantages, random forest model outperforming others dataset 65 lakes, Ridge is best Lasso performs 20 Linear only 4 cases. provides fit due its ability handle large number variables consider interactions, thereby offering fitting effect. These insights are crucial understanding influence within Basin instrumental developing data.

Язык: Английский

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Analysis of Lake Area Dynamics and Driving Forces in the Jianghan Plain Based on GEE and SEM for the Period 1990 to 2020

Remote Sensing, Год журнала: 2024, Номер 16(11), С. 1892 - 1892

Опубликована: Май 24, 2024

The lakes of Jianghan Plain, as an important component the water bodies in middle and lower reaches Yangtze River plain, have made significant contributions to maintaining ecological health promoting sustainable development Plain. However, there is a relatively limited understanding regarding trends lake area change for different types their dominant factors over past three decades Based on Google Earth Engine (GEE) platform, combined with body index method, changes (area > 1 km2) Lake Group from 1990 2020 were extracted analyzed. Additionally, Partial least squares structural equation model (PLS-SEM) was utilized analyze driving affecting these lakes. results show that 2020, wet season level exhibited decreasing trend, by 893.1 km2 77.9 km2, respectively. dry increased 59.27 km2. areas all reached minimum values 2006. According PLS-SEM results, continuous lakes’ are mainly controlled environmental overall. Furthermore, human influence mutation area. This study achieved precise extraction accurate analysis factors, providing basis comprehensive dynamic which beneficial rational utilization protection resources.

Язык: Английский

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Exploring the drivers and dynamics of urban waters: A case study of Wuhan from 1980 to 2060

Ecological Indicators, Год журнала: 2024, Номер 167, С. 112625 - 112625

Опубликована: Сен. 19, 2024

Язык: Английский

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