A Methodological Approach for Assessing the Post-Fire Resilience of Pinus halepensis Mill. Plant Communities Using UAV-LiDAR Data Across a Chronosequence

Abstract

The assessment of fire effects in Aleppo pine forests is crucial for guiding the recovery of burnt areas. This study presents a methodology using UAV-LiDAR data to quantify malleability and elasticity in four burnt areas (1970, 1995, 2008 and 2015) through the statistical analysis of different metrics related to height structure and diversity (Height mean, 99th percentile and Coefficient of Variation), coverage, relative shape and distribution strata (Canopy Cover, Canopy Relief Ratio and Strata Percent Coverage), and canopy complexity (Profile Area and Profile Area Change). In general terms, malleability decreases over time in forest ecosystems that have been affected by wildfires, whereas elasticity is higher than what has been determined in previous studies. However, a particular specificity has been detected from the 1995 fire, so we can assume that there are other situational factors that may be affecting ecosystem resilience. LiDAR metrics and uni-temporal sampling between burnt sectors and control aids are used to understand community resilience and to identify the different recovery stages in P. halepensis forests.

Plant phenotyping relevance

UAV-LiDARによる樹冠構造・被覆・複雑性などの植物群落形質を定量化する方法論が研究の中心であり、火災後の森林レジリエンス評価に応用している。

Code and data availability

The supplied blocks describe UAV-LiDAR acquisition, processing (DJI Terra, MCC-LiDAR, FUSION/LDV) and statistical analysis, but contain no data availability statement, repository deposit, or authors' public URL for the LiDAR point clouds, derived metrics, or analysis code. No paper-specific public asset is identified.

No evidence-backed public reproduction asset is currently recorded.