The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 129–134, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-129-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 129–134, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-129-2022
 
30 May 2022
30 May 2022

TREE CANOPY HEIGHT ESTIMATION AND ACCURACY ANALYSIS BASED ON UAV REMOTE SENSING IMAGES

J. Hao1, Z. Fang1, B. Wu1, S. Liu2, Y. Ma1, and Y. Pan3 J. Hao et al.
  • 1State Grid Ningxia Electric Power Research Institute, Ningxia 750011, China
  • 2State Grid Ningxia Electric Power Co.Ltd, Ningxia 750001, China
  • 3School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China

Keywords: UAV, Danger tree monitoring, CHM, Point cloud, Accuracy verification

Abstract. The development of unmanned aerial vehicle (UAV) technology makes the traditional method of danger tree monitoring more digital and convenient. In order to explore the accuracy of tree height measurement by general consumer UAV, this paper takes a campus of University as the research area. The high-resolution images acquired by UAV were processed and compared the accuracy of extracting tree height based on the digital canopy height model (CHM) and point cloud information, respectively. The experimental results show that the mean absolute error of tree height based on point cloud extraction is 10.28cm, which is better than that based on CHM. The tree height extracted from CHM will be less than the measured height. In addition, the accuracy of extracting the height of round crown is better than that of cone crown. The correlation between the measured and true values of the two methods was 0.987 and 0.994, respectively, which indicates that the method of danger tree monitoring by UAV is feasible.