Integration of RUSLE model with remotely sensed data over Google Earth Engine to evaluate soil erosion in Central Indus Basin

Earth Surface Processes and Landforms, Год журнала: 2025, Номер 50(3)

Опубликована: Март 6, 2025

Abstract Soil erosion presents a substantial environmental obstacle for farmers, especially in the plains of Indus Basin, which are characterised by rainfall scarcity. This study utilised remotely sensed data on Google Earth Engine (GEE) to estimate yearly soil implementing Revised Universal Loss Equation (RUSLE) model Central Basin. The study's primary objective was determine order importance and execute conservation strategies. input datasets were processed GEE produce essential factors, including erosivity ( R ), erodibility K slope length steepness LS land cover C ) management techniques P required model. area varied from 1 26.2 t ha −1 year . combined regions with low, moderate, high, extremely high rates amounted 445 397 ha. More precisely, 8670 (0.6%), 263 062 (18.2%) 468 310 (32.4%) allocated as first, second third‐class priority areas, respectively. These areas geographically dispersed across northwest eastern basin, sandy dunes infrequent agricultural cultivation. highlighted usability reliable estimation large scale. methodology amplifies effectiveness planning endeavours.

Язык: Английский

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Implications of policy changes for coastal landscape patterns and sustainability in Eastern China

Landscape Ecology, Год журнала: 2024, Номер 39(1)

Опубликована: Янв. 1, 2024

Abstract Context The capacity of a landscape to maintain multifunctionality through ongoing pressures relates its sustainability and is affected by land use policy environmental changes. In coastal zones, limited empirical evidence exists regarding the impact macro-level changes on local landscapes their resulting temporal spatial responses. Objectives This paper investigates national provincial policies patterns in China’s Zhejiang zone, encompassing human expansion ecological restoration terms sustainability. Methods A cluster-based pattern mining conducted from 1990 2020 using Google Earth Engine, which coupled with historical classification analysis. Results Coastal zone evolved three stages: development-oriented (1990–2010), conservation turning (2010–2017), land-sea coordination (2017-present). Consequently, significant differences are observed. Artificial surface aligned these stages, especially Hangzhou Bay, Xiangshan Sanmen Bay. Expansion responded more swiftly development-stimulating policies, exhibiting longer-lasting effects. Conservation faced delays due conflicting interests, varied implementation entities, unsynchronized cycles, lack coordinated priorities across terrestrial marine domains. Conclusions study provides insights into processes offering implications for It facilitates an evaluation effectiveness suggests differentiated sustainable transformation plans. Moreover, it underscores need strengthen between sea development effective management

Язык: Английский

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Spatiotemporal Evolution of Production–Living–Ecological Land and Its Eco-Environmental Response in China’s Coastal Zone

Remote Sensing, Год журнала: 2023, Номер 15(12), С. 3039 - 3039

Опубликована: Июнь 10, 2023

High-intensity human activities have caused dramatic transformations of land function in China’s coastal zone, putting enormous pressure on the region’s ecological environment. It is urgent to fully understand spatiotemporal evolution land-use zone promote sustainable development. Therefore, based CNLUCC data for 2000, 2010, and 2020, this study quantitatively explored production–living–ecological (PLEL) its eco-environmental response by using multiple analysis methods, gradient analysis, quality index. The results showed that over past 20 years, production (PL) continued decrease, whereas living (LL) blue (BEL) increased. In vertical direction, PL (EL) dominated northern southern respectively. horizontal with increasing distance from coastline, green (GEL) increased, it was opposite BEL. PLEL were high low frequency north south, From 2000 slightly degraded, conditions “excellent south poor north”. qualities each sub-coastal gradually improved coastline. main transformation types led improvement degradation other lands (OPL) BEL OPL, findings will guide planning, protection, science-based usage.

Язык: Английский

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Five Years of Natural Vegetation Recovery in Three Forests of Karst Graben Area and Its Effects on Plant Diversity and Soil Properties

Forests, Год журнала: 2025, Номер 16(1), С. 91 - 91

Опубликована: Янв. 8, 2025

In recent decades, excessive human activities have led to large-scale rocky desertification in karst areas. Vegetation restoration is one of the most important ways control desertification. this study, vegetation surveys were conducted on three typical plantations Jianshui County, Yunnan Province, a fault basin area, 2016 and 2021. The Pinus massoniana forest (PM), yunnanensis (PY), mixed forests Quercus variabilis (MF). Plant diversity soil nutrients compared during five-year period. This paper mainly draws following results: plant PM, PY, MF increased. With increase time, new species appeared tree layer, shrub herb layer forests. Tree with smaller importance values gradually withdrew from community. Patrick index, Simpson Shannon–Wiener index increased significantly. Pielou changed highest for PM PY There was no significant difference layer. bulk density (BD) decreased. total porosity (TP), capillary (CP), non-capillary (NCP). pH significantly 5.88~6.24 7.24~7.34. decreased (p < 0.05). contents nitrogen (TN) ammonium (NH4+-N) content nitrate (NO3−-N) Total phosphorus (TP) MF. available (AP) general, physical chemical properties also been improved. results can provide data support

Язык: Английский

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Modelling the Elevation of the Exposed Intertidal Zone along the Parts of Tamaulipas State Coastline, Mexico Using a Continental Scale Approach and Machine Learning Techniques

Journal of South American Earth Sciences, Год журнала: 2025, Номер 154, С. 105391 - 105391

Опубликована: Янв. 31, 2025

Язык: Английский

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Coastal Dynamics: Assessing Erosion and Progradation Patterns in Campeche Coastal Region Using Machine Learning Techniques for Geological Insights

Journal of South American Earth Sciences, Год журнала: 2025, Номер unknown, С. 105406 - 105406

Опубликована: Фев. 1, 2025

Язык: Английский

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Climate change-induced vulnerability assessment for the Florida Coast using hybrid machine learning models

Ecological Indicators, Год журнала: 2025, Номер 171, С. 113242 - 113242

Опубликована: Фев. 1, 2025

Язык: Английский

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Spatial Prediction of Soil Salinity: Remote Sensing and Machine Learning Approach

Journal of South American Earth Sciences, Год журнала: 2025, Номер unknown, С. 105440 - 105440

Опубликована: Фев. 1, 2025

Язык: Английский

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Integrating Geospatial Techniques and Machine Learning for Assessing Soil Erosion and Associated Geomorphic Risks

Journal of South American Earth Sciences, Год журнала: 2025, Номер unknown, С. 105463 - 105463

Опубликована: Фев. 1, 2025

Язык: Английский

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Zoning the soil salinization levels in the northern China’s coastal areas based on high-resolution soil mapping

Ecological Indicators, Год журнала: 2025, Номер 172, С. 113303 - 113303

Опубликована: Март 1, 2025

Язык: Английский

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