基于UAV-SfM方法的黄土高原砒砂岩区侵蚀监测算法比较

刘益麟; 李朋飞; 李豆; 胡晋飞; 白晓; 严露; 丹杨

doi:10.13870/j.cnki.stbcxb.2024.03.030

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基于UAV-SfM方法的黄土高原砒砂岩区侵蚀监测算法比较

更新时间：2025-03-12

- 基于UAV-SfM方法的黄土高原砒砂岩区侵蚀监测算法比较
- Comparison of Erosion Monitoring Methods in the Pisha Sandstone Areas of the Chinese Loess Plateau Based on UAV-SfM Data
- 水土保持学报 2024年38卷第3期页码：91-100
- 作者机构：
  
  西安科技大学测绘科学与技术学院,西安,710054
- 作者简介：
  
  [ "刘益麟(1999—),男,陕西咸阳人,硕士研究生,主要从事地貌遥感与土壤侵蚀监测研究。E-mail:1214377322@qq.com" ]
- 基金信息：
  
  国家重点研发计划政府间国际科技创新合作重点专项(2022YFE0119200);国家自然科学基金项目(41977059,U2243211,42207407);水利部重大科技项目(SKS-2022092);陕西省自然科学基金项目(2022JQ-259);陕西省教育厅基金项目(22JK0463)
- DOI：10.13870/j.cnki.stbcxb.2024.03.030
  中图分类号： S157.1
- 纸质出版：2024
- 稿件说明：
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刘益麟, 李朋飞, 李豆, 等. 基于UAV-SfM方法的黄土高原砒砂岩区侵蚀监测算法比较[J]. 水土保持学报, 2024,38(3):91-100.

LIU Yilin, LI Pengfei, LI Dou, et al. Comparison of Erosion Monitoring Methods in the Pisha Sandstone Areas of the Chinese Loess Plateau Based on UAV-SfM Data[J]. 2024, 38(3): 91-100.
刘益麟, 李朋飞, 李豆, 等. 基于UAV-SfM方法的黄土高原砒砂岩区侵蚀监测算法比较[J]. 水土保持学报, 2024,38(3):91-100. DOI： 10.13870/j.cnki.stbcxb.2024.03.030.

LIU Yilin, LI Pengfei, LI Dou, et al. Comparison of Erosion Monitoring Methods in the Pisha Sandstone Areas of the Chinese Loess Plateau Based on UAV-SfM Data[J]. 2024, 38(3): 91-100. DOI： 10.13870/j.cnki.stbcxb.2024.03.030.

摘要

[目的] 为比较地形变化监测算法在黄土高原砒砂岩区的适用性。[方法] 以皇甫川流域特拉沟一支沟为研究对象

采用无人机摄影测量技术获取2022年7月至2023年3月影像

结合SfM技术生成三维点云数据

比较分析[digital elevation model of difference(DoD)、cloud to cloud(C2C)、cloud to mesh(C2M)、multiscale model to model cloud comparison(M3C2)]等4种算法的侵蚀产沙监测精度

并分析点云密度变化对各方法精度的影响。[结果] (1)4种常用算法在空间上都能监测到大幅度地表变化。其中

以M3C2算法的结果最优

线性拟合结果最好(R2=0.953

p＜0.01)

且综合误差最小(MAE=0.016 1 m

MRE=3.37%

RMSE=0.019 4 m)

C2M算法其次

DoD算法再次

而C2C算法结果最差。(2)通过比较

DoD算法仅适用于平坦区域的快速检测

坡度陡峭的区域监测侵蚀沉积量存在高估的现象。(3)M3C2和C2C算法对点云密度变化敏感

而C2M和DoD受点云密度变化影响较小。[结论] 研究结果可为黄土高原砒砂岩地区基于UAV-SfM的侵蚀产沙监测方法的选择提供参考。

Abstract

[Objective] To compare the applicability of terrain change monitoring algorithms in the pisha sandstone areas of the loess plateau. [Methods] A branch gully Telagouagou Huangfuchuan was taken as the research object

including digital elevation model of difference (DoD)

cloud to cloud (C2C)

cloud to mesh (C2M)

and multiscale model to model cloud comparison (M3C2). Point cloud data employed to operate the four algorithms were produced using the SfM technique based on images acquired by UAV between July 2022 and March 2023. The impact of point density changes in the accuracy of the employed algorithms was also investigated. [Results] (1) All four algorithms were capable of effectively monitoring large surface changes. Among them

the M3C2 algorithm performed the best with the highest accuracy (R2=0.953

p＜0.01) and the lowest error (MAE=0.016 1 m

MRE=3.37%

RMSE=0.019 4 m)

followed by the C2M algorithm

DoD algorithm

and C2C algorithm. (2) The DoD algorithm was only suitable for flat areas and yielded overestimated results for steep sloping areas. (3) The M3C2 and C2C algorithms were sensitive to point cloud density

while the C2M and DoD algorithms were less sensitive. [Conclusion] The study provided a useful reference for the selection of erosion monitoring methods for the Pisha sandstone areas.

关键词

Keywords

references

COOK K L. An evaluation of the effectiveness of low-cost UAVs and structure from motion for geomorphic change detection[J].Geomorphology,2017,278:195-208.

LANE S N, WESTAWAY R M, MURRAY H D. Estimation of erosion and deposition volumes in a large, gravel-bed, braided river using synoptic remote sensing[J].Earth Surface Processes and Landforms,2003,28(3):249-271.

GIRARDEAU M D, ROUXA M, MARCB R, et al. Change detection on points cloud data acquired with a ground laser scanner[J].Research Gate,2005,36(part 3):W19.

CIGNONI P, ROCCHINI C, SCOPIGNO R. Metro: Measuring error on simplified surfaces[J].Computer Graphics Forum,1998,17(2):167-174.

LAGUE D, BRODU N, LEROUX J. Accurate 3D comparison of complex topography with terrestrial laser scanner: Application to the Rangitikei canyon (N-Z)[J].ISPRS Journal of Photogrammetry and Remote Sensing,2013,82:10-26.

KALIRAJ S, CHANDRASEKAR N, RAMACHANDRAN K K. Mapping of coastal landforms and volumetric change analysis in the south west coast of Kanyakumari, South India using remote sensing and GIS techniques[J].The Egyptian Journal of Remote Sensing and Space Science,2017,20(2):265-282.

LIU X Y, ZHU W, LIAN X G, et al. Monitoring mining surface subsidence with multi-temporal three-dimensional unmanned aerial vehicle point cloud[J].Remote Sensing,2023,15(2):e374.

NOURBAKHSHBEIDOKHTI S, KINOSHITA A, CHIN A, et al. A workflow to estimate topographic and volumetric changes and errors in channel sedimentation after disturbance[J].Remote Sensing,2019,11(5):e586.

OREM C A, PELLETIER J D. Quantifying the time scale of elevated geomorphic response following wildfires using multi-temporal LiDAR data: An example from the Las Conchas fire, Jemez Mountains, New Mexico[J].Geomorphology,2015,232:224-238.

BENJAMIN J, ROSSER N, BRAIN M. Rockfall detection and volumetric characterisation using LiDAR[J].Landslides and Engineered Slopes,2016,5:389-395.

BARNHART T B, CROSBY B T. Comparing two methods of surface change detection on an evolving thermokarst using high-temporal-frequency terrestrial laser scanning, selawik river, Alaska[J].Remote Sensing,2013,5(6):2813-2837.

ALEXIOU S, DELIGIANNAKIS G, PALLIKARAKIS A, et al. Comparing high accuracy t-LiDAR and UAV-SfM derived point clouds for geomorphological change detection[J].ISPRS International Journal of Geo-Information,2021,10(6):e367.

董亚维,曹雪峰.皇甫川流域水土保持率变化状况及阈值预测[J].人民黄河,2022,44(9):131-134. DONG Y W, CAO X F. Variation ofsoil and water conservation rate and threshold prediction in huangfuchuan watershed[J].Yellow River,2022,44(9):131-134.

李景宗,张永永,赵帮元,等.皇甫川特拉沟小流域近期泥沙减少成因分析[J].人民黄河,2018,40(4):1-4,36. LI J Z, ZHANG Y Y, ZHAO B Y, et al. Cause analysis of recent sediment reduction in Telagou small watershed of Huangfuchuan basin[J].Yellow River,2018,40(4):1-4,36.

葛海明,张维,王小龙,等.基于SfM的城市电缆隧道三维重建方法优化研究[J].图学学报,2023,44(3):540-550. GE H M, ZHANG W, WANG X L, et al. Optimization of 3D reconstruction method for urban cable tunnel based on SfM method[J].Journal of Graphics,2023,44(3):540-550.

WANG F, ZHAO Z J. A survey of iterative closest point algorithm//2017 Chinese Automation Congress (CAC). Jinan, China. IEEE,2017:4395-4399.

LI L, NEARING M A, NICHOLS M H, et al. Using terrestrial LiDAR to measure water erosion on stony plots under simulated rainfall[J].Earth Surface Processes and Landforms, 2020,45(2):484-495.

ZHANG W M, QI J B, WAN P, et al. An easy-to-use airborne LiDAR data filtering method based on cloth simulation[J].Remote Sensing,2016,8(6):e501.

SHIMIZU K, NISHIZONO T, KITAHARA F, et al. Integrating terrestrial laser scanning and unmanned aerial vehicle photogrammetry to estimate individual tree attributes in managed coniferous forests in Japan[J].International Journal of Applied Earth Observation and Geoinformation, 2022,106:e102658.

孟永东,袁昌纬,田斌,等.滑坡表面位移的无人机航测点云比对监测方法[J].测绘通报,2023(5):1-8. MENG Y D, YUAN C W, TIAN B, et al. The method of landslide surface displacement monitoring based on UAV aerial survey point cloud comparison[J].Bulletin of Surveying and Mapping,2023(5):1-8.

LU L J, ZHOU Y, WALKER R T. Using historical aerial photographs to measure earthquake deformation: Testing the effects of scan resolution[J].Remote Sensing of Environment,2021,252:e112118.

MILAN D J, HERITAGE G L, LARGE A R G, et al. Filtering spatial error from DEMs: Implications for morphological change estimation[J].Geomorphology,2011,125(1):160-171.

HERITAGE G L, MILAN D J, LARGE A R G, et al. Influence of survey strategy and interpolation model on DEM quality[J].Geomorphology,2009,112(3/4):334-344.

WILLIAMS J G, ANDERS K, WINIWARTER L, et al. Multi-directional change detection between point clouds[J].ISPRS Journal of Photogrammetry and Remote Sensing,2021,172:95-113.

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