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Dr. Reda Fekry Abdlekawy KHALIEL :: Publications:

Title:
Characterizing Forest Plot Decay Levels Based on Leaf Area Index, Gap Fraction, and L-Moments from Airborne LiDAR
Authors: Abubakar Sani-Mohammed;Wei Yao;Tsz Chung Wong;Reda Fekry;Marco Heurich
Year: 2024
Keywords: plot decay levels; ALS metrics; leaf area index; leaf area density; L-moments; forest management; deadwood
Journal: Remote Sensing
Volume: 16
Issue: 15
Pages: 2824
Publisher: MDPI
Local/International: International
Paper Link:
Full paper Reda Fekry Abdlekawy KHALIEL_remotesensing-16-02824.pdf
Supplementary materials Not Available
Abstract:

Effective forest management is essential for mitigating climate change effects. This is why understanding forest growth dynamics is critical for its sustainable management. Thus, characterizing forest plot deadwood levels is vital for understanding forest dynamics, and for assessments of biomass, carbon stock, and biodiversity. For the first time, this study used the leaf area index (LAI) and L-moments to characterize and model forest plot deadwood levels in the Bavarian Forest National Park from airborne laser scanning (ALS) data. This study proposes methods that can be tested for forests, especially those in temperate climates with frequent cloud coverage and limited access. The proposed method is practically significant for effective planning and management of forest resources. First, plot decay levels were characterized based on their canopy leaf area density (LAD). Then, the deadwood levels were modeled to assess the relationships between the vegetation area index (VAI), gap fraction (GF), and the third L-moment ratio (T3). Finally, we tested the rule-based methods for classifying plot decay levels based on their biophysical structures. Our results per the LAD vertical profiles clearly showed the declining levels of decay from Level 1 to 5. Our findings from the models indicate that at a 95% confidence interval, 96% of the variation in GF was explained by the VAI with a significant negative association (VAIslope = −0.047; R2 = 0.96; (p < 0.001)), while the VAI explained 92% of the variation in T3 with a significant negative association (VAIslope = −0.50; R2 = 0.92; (p < 0.001)). Testing the rule-based methods, we found that the first rule (Lcv = 0.5) classified Levels 1 and 2 at (Lcv < 0.5) against Levels 3 to 5 at (Lcv > 0.5). However, the second rule (Lskew = 0) classified Level 1 (healthy plots) as closed canopy areas (Lskew < 0) against Levels 2 to 5 (deadwood) as open canopy areas (Lskew > 0). This approach is simple and more convenient for forest managers to exploit for mapping large forest gap areas for planning and managing forest resources for improved and effective forest management.

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