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A methodological approach for assessing the resilience of Pinus halepensis L. plant communities using UAV-LiDAR data
* 1 , 1 , 1 , 1 , 2 , 1 , 2 , 3
1  Grupo GEOFOREST-IUCA, Dpto. de Geografía y Ordenación del Territorio, Universidad de Zaragoza, Pedro Cerbuna 12, 50.009 Zaragoza, España
2  Centro Universitario de la Defensa de Zaragoza, Academia General Militar, Zaragoza, España
3  Department of Geography and Land Management, University of Zaragoza, Pedro Cerbuna 12, 50009 Zaragoza, Spain. University Institute of Research in Environmental Sciences (IUCA), University of Zaragoza, Spain
Academic Editor: Riccardo Buccolieri


Reproductive strategies of most plant species in Mediterranean ecosystems exhibit efficient mechanisms of germination and/or resprouting, ensuring the development of formations similar to those affected by fire based on models of plant succession or auto-succession. The assessment of fire effects and recovery becomes a key element in guiding the strategic orientation of burned areas, encompassing comprehensive management, adaptation, mitigation, and more. In this context, the design of operational methodologies based on the processing of UAV-LiDAR data holds great interest due to the quality of the structural information they provide.

This study presents a methodology for quantifying the malleability of resilience in burned areas through statistical analysis of dasometric parameters derived from UAV-LiDAR data. Flights were conducted over burned areas (fires in 1970, 1995, and 2008 in Pinus halepensis L. forests with Quercus coccifera L. located in Montes de Zuera, Aragón, Spain) and their respective controls (i.e., nearby unaffected areas with the same pre-disturbance characteristics). As a matrix-based linear approach is not applicable for analysis (due to uni-temporal images lacking corresponding elements), a methodology was designed that involved the following phases: (1) flights using an unmanned aerial vehicle (DJI Matrice 300 RTK) equipped with a DJI Zenmuse-L1 LiDAR sensor to analyze two forest attributes: tree height (99th percentile) and Profile Area Change (PAC, a multitemporal LiDAR metric introduced by Hu et al., 2019), using “DJI Terra v.3.6.7” and “FUSION-LDV v.4.21” software; (2) field-based floristic and physiognomic inventories conducted concurrently with the flights, utilizing sampling units of 20m² (10x2m); (3) extraction of random samples (10%) in equally sized quadrangular sectors, both burned and control; (4) application of basic statistics, construction of frequency distribution diagrams, and similarity analysis using the Kolmogorov-Smirnov test.

The maximum vegetation height ranges from 9 to 14 m in control areas, and from 4 to 12 m in burned sectors. Significant differences have been identified in the distributions of vegetation height (99th percentile) among the three fires, exhibiting specific maximum absolute differences (D) depending on the fire year (D = 0.26, 0.31, 0.76 for 1970, 1995, and 2008 fires, respectively), resulting in differences of approximately 2, 0.25, and 5 m between P. halepensis plant communities. Regarding the PAC index, the average values are -3.9, -22.5, and 33.7. Positive values in 2008 indicate greater LiDAR pulse penetration in the burned area, consequently leading to lower regeneration. Negative values in 1995 identify greater complexity and density of regenerated vegetation, while values close to 0 for the 1970 fire indicate greater homogeneity between regenerated structures and their control.

The use of LiDAR metrics and uni-temporal sampling between burned sectors and their corresponding controls facilitates an understanding of the resilience of these communities and the identification of different stages in the recovery process of P. halepensis forests. Considering other contextual variables, such as fire severity, post-fire hygrothermal characteristics, or anthropogenic treatments, may provide new insights into characterizing the malleability of burned Aleppo pine forests.

Keywords: Forest Fire; Drone; Vegetation, Recovery, Aleppo pine forests
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