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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