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Effects of thinning on structural complexity of Larix olgensis plantation.

Fan CongWei-Wei JiaFeng-Ri LiDan-Dan LiHao-Tian Guo
Published in: Ying yong sheng tai xue bao = The journal of applied ecology (2024)
In this paper, we collected the individual tree point cloud data in the plots of Larix olgensis plantations with different thinning intensities in Mengjiagang Forest Farm, applied the fractal analysis theory to extract box dimensions ( D b ) on MATLAB platform, and characterized the structural complexity of L. olgensis . We assessed the effect of different thinning intensities and tree attributes on the structural complexity of L. olgensis . The results showed significant differences in L. olgensis D b between control (CK: 1.68±0.07), low and medium intensity thinning (T 1 , T 2 , T 3 : 1.74±0.07), and high intensity thinning (T 4 : 1.81±0.06), which indicated that the thinning intensity increased tree structural complexity. For trunk attribute, the diameter at breast height and tree height was significantly positively correlated with D b , while the height-to-diameter ratio was significantly negatively correlated with D b . For canopy attribute, crown volume, surface area, projected area, and crown diameter was significantly positively correlated with D b . Hegyi competition index was significantly negatively correlated with D b in the control and low-moderate-intensity thinning treatments, but not significantly correlated with D b in the high-intensity thinning treatment. It indicated that thinning influenced L. olgensis structural complexity, with trunk attribute and canopy attribute as the main drivers of L. olgensis structural complexity.
Keyphrases
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  • machine learning
  • deep learning