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

Abstract

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.

UAV-Based Forest Health Monitoring: A Systematic Review

“…Beyond comparison, Viedma et al [95] crossed Sentinel-2-based fire severity rates with UAV-based LiDAR point clouds to improve tree-level interpretation using structural information to additionally distinguish between canopy, midstory, and ground fires. In other studies, UAV-based data were used to calibrate models to exploit the added value of multiscale approaches further.…”

“…The algorithms were applied to CHMs and point clouds based on LiDAR and SfM (or fused products), as well as orthophotos. Some papers provided a comparative analysis of different segmentation algorithms [22,95,155]. Minařík et al [120] tested several segmentation techniques on different photogrammetric data products, while Lin et al [116] compared the applicability of hyperspectral imagery and LiDAR point clouds for image segmentation.…”

“…One study stood out, in which four cameras were mounted on the platform, including two oblique RGB, one multispectral (including thermal), and one hyperspectral camera [94]. In all but two studies [93,95], passive sensors were used.…”

Quality Control of Outsourced LiDAR Data Acquired with a UAV: A Case Study

“…Among the LiDAR systems currently commercialized for use with UAV, the lowcost ones (even if costly compared to photogrammetric systems for UAV) rely significantly on post-processing software to deliver satisfactory results [8]. As existing geo-information companies are entering the market, driven by the increasing use of LiDAR data from UAV as shown by several published works (e.g., References [9][10][11][12][13][14][15][16]), we can still expect some lack of expertise and experience that justifies the need for explicit procedures to inspect the quality of the delivered products. Therefore, this study presents and discusses the problems that the end-user may encounter concerning the quality of outsourced UAV LiDAR data acquisition as well as possible solutions.…”

Aspects Regarding of a UGV Fire Fighting Thermal Shield

“…Excessively equipping the robot with sensors isn't the best solution. This is why additional risk area monitoring systems are used for areas where robotic system must intervene [15][16][17][18][19][20][21].…”