Forest structural diversity characterization in Mediterranean landscapes affected by fires using Airborne Laser Scanning data |
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| Authors: | P J Gelabert AL Montealegre MT Lamelas D Domingo |
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| Institution: | 1. Department of Agricultural and Forest Engineering, University of Lleida, Lleida, Catalonia, Spain;2. Interdepartmental research groups, Group GAMES, University of Lleida, Lleida, Catalonia, Spain;3. Interdepartmental research groups, Joint Research Unit AGROTECNIO- CTFC, Solsona, Lleida, Catalonia, Spainperejoan.gelabert@udl.cat https://orcid.org/0000-0001-8020-4932;5. GEOFOREST-IUCA, Department of Geography, University of Zaragoza, Zaragoza, Spainhttps://orcid.org/0000-0001-6288-2780;6. GEOFOREST-IUCA, Department of Geography, University of Zaragoza, Zaragoza, Spain;7. Centro Universitario de la Defensa de Zaragoza, Academia General Militar, Zaragoza, Spainhttps://orcid.org/0000-0002-8954-7517;8. Land Change Science Research Unit, Swiss Federal Institute for Forest, Snow and Landscape Research WSL, Birmensdorf, Switzerlandhttps://orcid.org/0000-0002-8362-7559 |
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| Abstract: | ABSTRACTForest 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. |
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| Keywords: | Forest structure LIDAR machine learning landscape |
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![ORCID Icon]({"type":"image","src":"/templates/jsp/images/orcid.png"} "ORCID Icon")
https://orcid.org/0000-0001-8020-4932;5. GEOFOREST-IUCA, Department of Geography, University of Zaragoza, Zaragoza, Spain