Optimization Method of Vegectation and Terrain Factors in CSLE Model at a Mountainous Area of Central Shandong Province

Xing Xianshuang; Dong Mingming; Guo Jing; Meng Lin; Zhang Yu; Zhao Dengliang; Pang Haiwei; Bian Zhen

doi:10.13961/j.cnki.stbctb.2022.02.019

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Optimization Method of Vegectation and Terrain Factors in CSLE Model at a Mountainous Area of Central Shandong Province

- Optimization Method of Vegectation and Terrain Factors in CSLE Model at a Mountainous Area of Central Shandong Province
- Bulletin of Soiland Water Conservation Vol. 42, Issue 2, Pages: 136-143(2022)
- 作者机构：
  
  1. 山东省水文中心,山东,济南,250002
  2. 潍坊市水文中心,山东,潍坊,266071
  3. 济南大学水利与环境学院,山东,济南,250022
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2022.02.019
  CLC： S157.1
- Published：2022
- 稿件说明：
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Xing Xianshuang, Dong Mingming, Guo Jing, et al. Optimization Method of Vegectation and Terrain Factors in CSLE Model at a Mountainous Area of Central Shandong Province[J]. Bulletin of Soiland Water Conservation, 2022, 42(2): 136-143.
DOI：

Xing Xianshuang, Dong Mingming, Guo Jing, et al. Optimization Method of Vegectation and Terrain Factors in CSLE Model at a Mountainous Area of Central Shandong Province[J]. Bulletin of Soiland Water Conservation, 2022, 42(2): 136-143. DOI： 10.13961/j.cnki.stbctb.2022.02.019.

摘要

[目的

]

优化中国土壤侵蚀模型CSLE的因子计算方法，提高该模型在鲁中低山丘陵小流域的模拟精度，为低山丘陵区小流域的土壤侵蚀因子动态监测方法提供技术支撑。[方法

]

以淄博市郝峪小流域为研究区，将国产高分卫星数据和无人机低空遥感数据相结合，对CSLE模型中的地形地貌因子和植被覆盖与生物措施因子进行了算法优化，通过与国家监测数据的对比，验证优化后土壤侵蚀模型因子的优越性。[结果

]

①模型因子算法优化验证结果表明，坡度坡长因子频率曲线相似度为0.91，可以进行土壤侵蚀计算； ②对于植被覆盖度提取的尺度转换模型，拟合优度系数R

为0.686 8，可以进行植被覆盖与生物措施因子计算。③优化模型后的土壤侵蚀数据与国家监测的土壤侵蚀数据的相关系数为0.686 8。[结论

]

优化了CSLE模型中因子提取计算的方法提高了模型的模拟精度。因子优化后计算的土壤侵蚀模数更符合当地实际情况。

Abstract

[Objective] The factor calculation method of the Chinese soil loss equation (CSLE) were optimized to improve the simulation accuracy of CSLE for a hilly watershed of Shandong Province in order to provide technical support for dynamic monitoring of soil erosion factors in this region. [Methods] The Haoyu small watershed of Zibo City was taken as the research area. Factors in the CSLE model

such as vegetation cover

biological measures

and topographical features

were optimized by combining domestic high-resolution satellite data and UAV low-altitude remote sensing data. The superiority of the optimized soil erosion model factor was verified by comparing with national monitoring data. [Results] ① The factors of rainfall erosion and soil erodibility were consistent with the dynamic monitoring results of soil erosion in Shandong Province. The main soil conservation engineering measures were terraces

for which the factor value for ridge terraces was 0.084 and the factor value for stone ridge terraces was 0.121; the factor value for farming measures in the study area was 0.425. ② The similarity of the slope and length factor frequency curve was 0.91

which can be used for soil erosion calculation; the goodness of fit coefficient (R2) was 0.686 8 for the scale conversion model for vegetation coverage extraction

which can be used for the calculation of vegetation cover and biological measures. ③ The correlation coefficient between the soil erosion data calculated by the optimized model and the soil erosion data monitored by the state was 0.686 8. [Conclusion] The optimized method of factor extraction and calculation in the CSLE model could improve the simulation accuracy of the model

and the soil erosion modulus calculated after factor optimization is more in line with the actual local situation.

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Keywords

references

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