首页|基于UAV-SfM方法的黄土高原砒砂岩区侵蚀监测算法比较

基于UAV-SfM方法的黄土高原砒砂岩区侵蚀监测算法比较

扫码查看
原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
[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。
Comparison of Erosion Monitoring Methods in the Pisha Sandstone Areas of the Chinese Loess Plateau Based on UAV-SfM Data
[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.

SfMterrain change monitoring algorithmpoint densityerosion monitoringloess plateaupisha sandstone area

刘益麟、李朋飞、李豆、胡晋飞、白晓、严露、丹杨

展开 >

西安科技大学测绘科学与技术学院,西安 710054

SfM 地形变化监测算法 点云密度 侵蚀监测 黄土高原 砒砂岩地区

国家重点研发计划政府间国际科技创新合作重点专项国家自然科学基金项目国家自然科学基金项目国家自然科学基金项目水利部重大科技项目陕西省自然科学基金项目陕西省教育厅基金项目

2022YFE011920041977059U224321142207407SKS-20220922022JQ-25922JK0463

2024

10.13870/j.cnki.stbcxb.2024.03.030

水土保持学报
中国土壤学会　中国科学院水利部水土保持研究所

水土保持学报

CSTPCD北大核心
影响因子：1.226
ISSN：1009-2242
年,卷(期)：2024.38(3)