1. 基于三维地形扫描的坡沟系统侵蚀产沙监测方法对比.
- Author
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李朋飞, 王锦博, 张晓晨, 胡晋飞, 刘立峰, 高健健, and 党恬敏
- Subjects
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SOIL erosion , *DIGITAL elevation models , *MULTISCALE modeling , *LOESS , *SEDIMENTS , *EROSION - Abstract
[Objective] The aim of this study is to elucidate the accuracy and applicability of different algorithms for detecting slope erosion, which can provide a reference for the selection and development of detection methods for soil erosion processes. [Methods] Five plots established on a natural slope in the loess hilly and gully region to conduct runoff scouring experiments. Using TLS data, we calculated erosion and sediment yield by using various methods such as Digital Elevation Model (DEM) of difference (DoD), Cloud to Cloud (C2C), Cloud to Mesh/Model (C2M), and Multiscale Model to Model Cloud Comparison (M3C2), and compared their results. [Results] The results of the uncertainty analysis showed that M3C2 produced the smallest average uncertainty, followed by C2C and C2M, while DoD yielded the largest uncertainty. The sediment yield calculated by the detection algorithms demonstrated that under high flow rates (85, 70 and 55 L/min), there was a significant linear relationship between consecutive sediment yield and cumulative sediment yield derived by the four algorithms and the corresponding measured sediment yield (R²>0.62, p<0.05), and M3C2 performed best; under low flow rates (40 and 25 L/min), no significant linear relationships were found between consecutive sediment yield and measured sediment yield, but significant linear relationships were found between cumulative sediment yield and measured sediment yield (R²>0.91, p<0.05), and DoD performed best. The spatial distribution of erosion and deposition indicated that C2C, M3C2, and DoD could reflect two stages of erosion evolution on hillslopes and gully slopes (rapid development stage and stable stage), with M3C2 being able to detect subtle topographic changes. However, M3C2 results were subject to ‘voids’ in the blind area of TLS scanning due to not finding corresponding points in the normal direction. [Conclusion] The M3C2 algorithm is more suitable for detecting complex terrain, but it will still fail in the blind spot of scanning, and the algorithm should be improved in the future to help cope with more complex and drastic terrain changes. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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