Forest structural diversity characterization in Mediterranean landscapes affected by fires using Airborne Laser Scanning data

ABSTRACT

Forest fires can change forest structure and composition, and low-density Airborne Laser Scanning (ALS) can be a valuable tool for evaluating post-fire vegetation response. The aim of this study is to analyze the structural diversity differences in Mediterranean Pinus halepensis Mill. forests affected by wildfires on different dates from 1986 to 2009. Several types of ALS metrics, such as the Light Detection and Ranging (LiDAR) Height Diversity Index (LHDI), the LiDAR Height Evenness Index (LHEI), and vertical and horizontal continuity of vegetation, as well as topographic metrics, were obtained in raster format from low point density data. In order to map burned and unburned areas, differentiate fire occurrence dates, and distinguish between old and more recent fires, a sample of pixels was previously selected to assess the existence of differences in forest structure using the Kruskal–Wallis test. Then, k-nearest neighbors algorithm (k-NN), support vector machine (SVM) and random forest (RF) classifiers were compared to select the most accurate technique. The results showed that, in more recent fires, around 70% of the laser returns came from grass and shrub layers, yielding low LHDI and LHEI values (0.37–0.65 and 0.28–0.46, respectively). In contrast, the areas burned more than 20 years ago had higher LHDI and LHEI values due to the growth of the shrub and tree strata. The classification of burned and unburned areas yielded an overall accuracy of 89.64% using the RF method. SVM was the best classifier for identifying the structural differences between fires occurring on different dates, with an overall accuracy of 68.79%. Furthermore, SVM yielded an overall accuracy of 75.49% for the classification between old and more recent fires.

KEYWORDS:

• Forest structure
• LIDAR
• machine learning
• landscape

Disclosure Statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Instituto de Investigación en Ciencias Ambientales de Aragón (IUCA) under scholarship [University of Zaragoza PEX-16-076], and by the research grant program Ajuts UdL, Jade Plus i Fundació Bancària La Caixa [Agreement 79/2018 of the Governing Council of the University of Lleida]. This research article has received a grant for its linguistic revision from the Language Institute of the University of Lleida (2020 call).