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首页> 外文期刊>Environmental Geology >Spatial prediction of soil erosion risk by remote sensing, GIS and RUSLE approach: a case study of Siruvani river watershed in Attapady valley, Kerala, India
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Spatial prediction of soil erosion risk by remote sensing, GIS and RUSLE approach: a case study of Siruvani river watershed in Attapady valley, Kerala, India

机译：基于遥感，GIS和RUSLE方法的土壤侵蚀风险空间预测：以印度喀拉拉邦阿塔帕迪河谷Siruvani河流域为例

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摘要

Siruvani watershed with a surface area of 205.54 km2 (20,554 hectare), forming a part of the Western Ghats in Attapady valley, Kerala, was chosen for testing RUSLE methodology in conjunction with remote sensing and GIS for soil loss prediction and identifying areas with high erosion potential. The RUSLE factors (R, K, LS, C and P) were computed from local rainfall, topographic, soil classification and remote sensing data. This study proved that the integration of soil erosion models with GIS and remote sensing is a simple and effective tool for mapping and quantifying areas and rates of soil erosion for the development of better soil conservation plans. The resultant map of annual soil erosion shows a maximum soil loss of 14.917 t h~(-1) year~(-1) and the computations suggest that about only 5.76% (1,184 hectares) of the area comes under the severe soil erosion zone followed by the higherosion zone (11.50% of the total area). The dominant high soil erosion areas are located in the central and southern portion of the watershed and it is attributed to the shifting cultivation, and forest degradation along with the combined effect of K, LS and C factor. The RUSLE model in combination with GIS and remote sensing techniques also enables the assessment of pixel based soil erosion rate.
机译：选择面积为205.54平方公里（20,554公顷）的Siruvani分水岭作为喀拉拉邦Attapady谷西高止山脉的一部分，被选择用于结合遥感和GIS来测试RUSLE方法学以预测土壤流失并确定高侵蚀区域潜在。 RUSLE因子（R，K，LS，C和P）是根据当地降雨，地形，土壤分类和遥感数据计算得出的。这项研究证明，将土壤侵蚀模型与GIS和遥感技术相集成是一种简单有效的工具，可用于绘制和量化土壤侵蚀的面积和速率，以制定更好的土壤保护计划。由此得出的年度土壤侵蚀图显示，最大土壤流失量为14.917 th〜（-1）年〜（-1），计算结果表明，该地区仅约5.76％（1,184公顷）位于严重的土壤侵蚀区以下。较高的区域（占总面积的11.50％）。主要的水土流失严重地区位于流域的中部和南部，这归因于种植的转移，森林的退化以及K，LS和C因子的综合作用。结合了GIS和遥感技术的RUSLE模型还可以评估基于像素的土壤侵蚀速率。

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  • 来源
    《Environmental Geology》 |2011年第4期|p.965-972|共8页
  • 作者

    V. Prasannakumar; R. Shiny; N. Geetha; H. Vyith;

  • 作者单位

    Center for Geo Information Science and Technology, University of Kerala, Kariavattom, Thiruvananthapuram 695581, Kerala, India;

    Center for Geo Information Science and Technology, University of Kerala, Kariavattom, Thiruvananthapuram 695581, Kerala, India;

    Center for Geo Information Science and Technology, University of Kerala, Kariavattom, Thiruvananthapuram 695581, Kerala, India;

    Center for Geo Information Science and Technology, University of Kerala, Kariavattom, Thiruvananthapuram 695581, Kerala, India;

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  • 正文语种 eng
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  • 关键词

    rusle; erodability; erosivity; slope length; siruvani; attapady;

    机译：罗素;可蚀性;侵蚀力;边坡长度;西鲁瓦尼;阿塔帕迪;

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