Identifying Regenerated Saplings by Stratifying Forest Overstory Using Airborne LiDAR Data.

Abstract

Identifying the spatiotemporal distributions and phenotypic characteristics of understory saplings is beneficial in exploring the internal mechanisms of plant regeneration and providing technical assistances for continues cover forest management. However, it is challenging to detect the understory saplings using 2-dimensional (2D) spectral information produced by conventional optical remotely sensed data. This study proposed an automatic method to detect the regenerated understory saplings based on the 3D structural information from aerial laser scanning (ALS) data. By delineating individual tree crown using the improved spectral clustering algorithm, we successfully removed the overstory canopy and associated trunk points. Then, individual understory saplings were segmented using an adaptive-mean-shift-based clustering algorithm. This method was tested in an experimental forest farm of North China. Our results showed that the detection rates of understory saplings ranged from 94.41% to 152.78%, and the matching rates increased from 62.59% to 95.65% as canopy closure went down. The ALS-based sapling heights well captured the variations of field measurements [ R 2 = 0.71, N = 3,241, root mean square error (RMSE) = 0.26 m, P R 2 = 0.78, N =443, RMSE = 0.23 m, P R 2 = 0.64, N = 443, RMSE = 0.24 m). This study provides a solution for the quantification of understory saplings, which can be used to improve forest ecosystem resilence through regulating the dynamics of forest gaps to better utilize light resources.

Plant phenotyping relevance

航空LiDARの3D構造情報を用いて林冠下の実生を自動検出・分割し、樹高を野外測定と検証する手法が研究の中心であるため。

Code and data availability

The paper's Data Availability statement points to two public GitHub repositories containing the authors' code (and stated relevant data) for the NSC overstory segmentation and adaptive mean shift sapling segmentation methods used in this ALS-based phenotyping analysis.