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Dr. Reda Fekry Abdlekawy KHALIEL :: Publications:

Forest Plot Decay Level Classification from ALS-Derived L-moments
Authors: Abubakar Sani-Mohammed; Wei Yao; Reda Fekry; Tsz-Chung Wong; Marco Heurich
Year: 2024
Keywords: LiDAR metrics; ALS; L-moments; Plot decay levels; Forest management; Forest sustainability; Biodiversity; Deadwood
Journal: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume: XLVIII
Issue: -1-2024
Pages: 587
Publisher: ISPRS
Local/International: International
Paper Link:
Full paper Not Available
Supplementary materials Not Available

Forests play key roles in climate regulation and essential environmental services for living organisms. This is why forests are the central focus of the United Nations (UN) Sustainable Development Goal (SDG 15). Thus, effective forest management is critical for forest sustainability and preservation. Remote sensing advancements have improved forest mensuration leveraging cost and time, contrary to the field surveying approach. Often, field data is required to validate remotely sensed results. However, circumstances in the forest may render field data collection impossible. This study applied LiDAR-derived L-moments to directly estimate and classify five forest plot decay levels, to understand forest growth dynamics in the absence of field data. Two L-moment-based rules were tested and evaluated for classifying the plot decay levels from ALS height returns. Our findings show that the first rule (Lcv = 0.5) classified decay Levels 1 and 2 at Lcv < 0.5 and Levels 3 to 5 at Lcv > 0.5, while the second rule (Lskew = 0) classified decay Level 1 at Lskew < 0, and Levels 2 to 5 at Lskew > 0. This indicates that, while discriminating plot decay levels, the L-moment-based rules can classify healthy forest areas and areas of deadwood of varying decay levels directly from ALS height returns. This can be convenient for forest managers to exploit for classifying plot decay levels and for mapping areas of large gaps for planning forest resources for effective forest management. Furthermore, the approach can equally be significant for assessing forest biomass, biodiversity, and carbon stock.

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