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A Reliable DBH Estimation Method Using Terrestrial LiDAR Points through Polar Coordinate Transformation and Progressive Outlier Removal
Diameter at breast height (DBH) is a crucial parameter for forest inventory. However, accurately estimating DBH remains challenging due to the noisy and incomplete cross-sectional points. To address this, this paper proposed a reliable DBH estimation method using terrestrial LiDAR points through polar coordinate transformation and progressive outlier removal. In this paper, the initial center was initially detected by rasterizing the convex hull, and then the Cartesian coordinates were transformed into polar coordinates. In the polar coordinate system, the outliers were classified as low and high outliers according to the distribution of polar radius difference. Both types of outliers were then removed using adaptive thresholds and the moving least squares algorithm. Finally, DBH was estimated by calculating the definite integral of arc length in the polar coordinate system. Twenty publicly available individual trees were adopted for the test. Experimental results indicated that the proposed method performs better than the other four classical DBH estimation methods. Furthermore, several extreme cases scanned using terrestrial LiDAR in practice, such as cross-sectional points with lots of outliers or larger data gaps, were also tested. Experimental results demonstrate that the proposed method accurately calculates DBH even in these challenging cases.
A Reliable DBH Estimation Method Using Terrestrial LiDAR Points through Polar Coordinate Transformation and Progressive Outlier Removal
Diameter at breast height (DBH) is a crucial parameter for forest inventory. However, accurately estimating DBH remains challenging due to the noisy and incomplete cross-sectional points. To address this, this paper proposed a reliable DBH estimation method using terrestrial LiDAR points through polar coordinate transformation and progressive outlier removal. In this paper, the initial center was initially detected by rasterizing the convex hull, and then the Cartesian coordinates were transformed into polar coordinates. In the polar coordinate system, the outliers were classified as low and high outliers according to the distribution of polar radius difference. Both types of outliers were then removed using adaptive thresholds and the moving least squares algorithm. Finally, DBH was estimated by calculating the definite integral of arc length in the polar coordinate system. Twenty publicly available individual trees were adopted for the test. Experimental results indicated that the proposed method performs better than the other four classical DBH estimation methods. Furthermore, several extreme cases scanned using terrestrial LiDAR in practice, such as cross-sectional points with lots of outliers or larger data gaps, were also tested. Experimental results demonstrate that the proposed method accurately calculates DBH even in these challenging cases.
A Reliable DBH Estimation Method Using Terrestrial LiDAR Points through Polar Coordinate Transformation and Progressive Outlier Removal
Zhenyang Hui (author) / Lei Lin (author) / Shuanggen Jin (author) / Yuanping Xia (author) / Yao Yevenyo Ziggah (author)
2024
Article (Journal)
Electronic Resource
Unknown
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