Abstract
Abstract. Unman aerial vehicle (UAV) LiDAR has been widely used in the field of forestry. Individual tree extraction is a key step for forest inventory. Although many individual tree extraction methods have been proposed, the individual tree extraction accuracy is still low due to the complex forest environments. Moreover, many parameters in these methods generally need to be set. Thus, the degree of automation of the methods is generally low. To solve these problems, this paper proposed an automatic mean shift segmentation method, in which the kernel bandwidths can be calculated self-adaptively. Meanwhile, a hierarchy mean shift segmentation technique was proposed to extract individual tree gradually. A plot-level UAV LiDAR tree dataset was adopted for testing the performance of the proposed method. Experimental results showed that the proposed method can achieve better individual tree extraction result without any parameter setting. Compared with the traditional mean shift segmentation method, both the completeness and mean accuracy of the proposed method are higher.
Highlights
Forest resources are one of the most important resources on the earth and have a major impact on human survival and development (Lim et al 2003)
A hierarchy mean shift segmentation method is proposed to extract individual trees iteratively according to the calculated horizontal bandwidths
To test the performance of the proposed automatic mean shift individual tree extraction method, the Unman aerial vehicle (UAV) LiDAR dataset provided by Brede et al (2019) was adopted for the testing
Summary
Forest resources are one of the most important resources on the earth and have a major impact on human survival and development (Lim et al 2003). The emergence of LiDAR technology has made new breakthroughs in forest resource surveys (Yu et al 2017). Compared with traditional optical remote sensing technology, LiDAR technology is less affected by the external environment. LiDAR technology can directly, quickly and accurately obtain three-dimensional coordinates of ground objects, and the emitted light beam can penetrate the inside of vegetation to obtain accurate internal structure (Vega et al 2016). LiDAR technology has significant advantages in identifying forest tree types, establishing vegetation models, extracting forest structure parameters, and measuring forest volume (Liu et al 2019). UAV LiDAR has a good balance among convenience, spatial coverage and data quality (Hu et al 2021).
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