Remote Sensing (Aug 2024)
Characterizing Forest Plot Decay Levels Based on Leaf Area Index, Gap Fraction, and L-Moments from Airborne LiDAR
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 VAIslope = −0.50; R2 = 0.92; (p Lcv = 0.5) classified Levels 1 and 2 at (Lcv Lcv > 0.5). However, the second rule (Lskew = 0) classified Level 1 (healthy plots) as closed canopy areas (Lskew 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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