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Article

Postfire Tree Structure from High-Resolution LiDAR and RBR Sentinel 2A Fire Severity Metrics in a Pinus halepensis-Dominated Burned Stand

1
Department of Environmental Sciences, University of Castilla-La Mancha, Avda. Carlos III, s/n, 45071 Toledo, Spain
2
Department of Forest Sciences, “Luiz de Queiroz” College of Agriculture, University of São Paulo (USP/ESALQ), Av. Pádua Dias, 11, 13418-900 Piracicaba, SP, Brazil
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(21), 3554; https://doi.org/10.3390/rs12213554
Received: 23 September 2020 / Revised: 28 October 2020 / Accepted: 28 October 2020 / Published: 30 October 2020
(This article belongs to the Special Issue Advances in Forest Fire Behaviour Modelling Using Remote Sensing)
Tree and plant structures remaining after fires reflect well their degree of consumption, and are therefore good indicators of fire severity. Satellite optical images are commonly used to estimate fire severity. However, depending on the severity of a fire, these sensors have a limited ability to penetrate the canopy down to the ground. Airborne light detection and ranging (LiDAR) can overcome this limitation. Assessing the differences between areas that have been burned in different fire severities based on satellite images of plant and tree structures remaining after fires is important, given its widespread use to characterize fires and fire impacts (e.g., carbon emissions). Here, we measured the remaining tree structures after a fire in a forest stand burned in SE Spain in the summer of 2017. We used high-resolution LiDAR data, acquired from an unmanned aerial vehicle (UAV) six months after the fire. This information was crossed with fire severity levels based on the relativized burnt ratio (RBR) derived from Sentinel 2A images acquired a few months before and after fire. LiDAR tree structure data derived from vertical canopy profiles (VCPs) were classified into three clusters, using hierarchical principal component analysis (HPCA), followed by a random forest (RF) to select the most important variables in distinguishing the cluster groups. Among these, crown leaf area index (LAI), crown leaf area density (LAD), crown volume, tree height and tree height skewness, among others, were the most significant variables, and reflected well the degree of combustion undergone by the trees based on the response of these variables to variations in fire severity from RBR Sentinel 2A. LiDAR metrics were able to distinguish crown fire from surface fire through changes in the understory LAI and understory and midstory vegetation. The three tree structure clusters were well separated among each other and significantly related with the RBR Sentinel 2A-derived fire severity categories. Unburned and low-severity burned areas were more diverse in tree structures than moderate and high severity burned ones. The LiDAR metrics derived from VCPs demonstrated promising potential for characterizing fine-grained post-fire plant structures and fire damage when crossed with satellite-based fire severity metrics, turning into a promising approach for better characterizing fire impacts at a resolution needed for many ecological processes. View Full-Text
Keywords: crown fire; surface fire; fire severity; random forest; LiDAR; Sentinel 2 crown fire; surface fire; fire severity; random forest; LiDAR; Sentinel 2
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MDPI and ACS Style

Viedma, O.; Almeida, D.R.A.; Moreno, J.M. Postfire Tree Structure from High-Resolution LiDAR and RBR Sentinel 2A Fire Severity Metrics in a Pinus halepensis-Dominated Burned Stand. Remote Sens. 2020, 12, 3554. https://doi.org/10.3390/rs12213554

AMA Style

Viedma O, Almeida DRA, Moreno JM. Postfire Tree Structure from High-Resolution LiDAR and RBR Sentinel 2A Fire Severity Metrics in a Pinus halepensis-Dominated Burned Stand. Remote Sensing. 2020; 12(21):3554. https://doi.org/10.3390/rs12213554

Chicago/Turabian Style

Viedma, Olga, Danilo R.A. Almeida, and Jose M. Moreno. 2020. "Postfire Tree Structure from High-Resolution LiDAR and RBR Sentinel 2A Fire Severity Metrics in a Pinus halepensis-Dominated Burned Stand" Remote Sensing 12, no. 21: 3554. https://doi.org/10.3390/rs12213554

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