1Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China
2Key Laboratory of Forestry Remote Sensing and Information System, National Forestry and Grassland Administration, Beijing 100091, China
| Received 30 Mar 2023 |
Accepted 08 Jan 2024 |
Published 08 Feb 2024 |
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 [R2 = 0.71, N = 3,241, root mean square error (RMSE) = 0.26 m, P < 0.01] and terrestrial laser scanning (TLS)-based measurements (R2 = 0.78, N =443, RMSE = 0.23 m, P < 0.01). The ALS-based sapling crown width was comparable with TLS-based measurements (R2 = 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.
