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author:

Chen, Miao (Chen, Miao.) [1] | Wang, Xiaoqin (Wang, Xiaoqin.) [2] (Scholars:汪小钦)

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EI Scopus

Abstract:

Soil erosion constitutes a critical environmental issue with far-reaching ramifications. Vegetation cover has been identified as a key factor in mitigating soil erosion. This study utilized remote sensing data and cloud computing resources provided by Google Earth Engine (GEE) to compute the land cover classification and Fractional Vegetation Cover (FVC) of the Minjiang River Basin in 2020. Subsequent to the utilization of the CSLE model incorporating precipitation data, Digital Elevation Model (DEM) data, and other relevant data, an assessment of soil erosion in the Minjiang River basin during 2020 was conducted. Furthermore, the correlation between FVC and soil erosion modulus was quantitatively examined. Our findings demonstrate that the predominant land cover type in the Minjiang River basin is forest and grassland, followed by arable land, with water having the smallest coverage. Over 65%of the study are a exhibits a FVC exceeding 0.8, indicative of a generally high level of vegetation coverage. The soil erosion modulus exhibited a marked decline with increasing FVC within the FVC intervals of 0-0.15 and 0.45-0.8. While the change of soil erosion modulus with FVC under other ranges was relatively flat. As such, erosion control measures may be more effective when implemented in the FVC ranges of 0-0.15 and 0.45-0.80. These findings provide decision-making references for governmental departments to formulate targeted soil and water conservation measures. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

Keyword:

Decision making Erosion Forestry Remote sensing Rivers Soil conservation Soils Surveying Vegetation Water conservation Watersheds

Community:

  • [ 1 ] [Chen, Miao]Key Laboratory of Spatial Data Mining & Information Sharing, Fuzhou University, The Academy of Digital China(Fujian), Fuzhou; 350108, China
  • [ 2 ] [Wang, Xiaoqin]Key Laboratory of Spatial Data Mining & Information Sharing, Fuzhou University, The Academy of Digital China(Fujian), Fuzhou; 350108, China

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ISSN: 0277-786X

Year: 2024

Volume: 12980

Language: English

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ESI Highly Cited Papers on the List: 0 Unfold All

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30 Days PV: 0

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