Using Pre-Fire High Point Cloud Density LiDAR Data to Predict Fire Severity in Central Portugal

Abstract

The wall-to-wall prediction of fuel structural characteristics conducive to high fire severity is essential to provide integrated insights for implementing pre-fire management strategies designed to mitigate the most harmful ecological effects of fire in fire-prone plant communities. Here, we evaluate the potential of high point cloud density LiDAR data from the Portuguese áGiLTerFoRus project to characterize pre-fire surface and canopy fuel structure and predict wildfire severity. The study area corresponds to a pilot LiDAR flight area of around 21,000 ha in central Portugal intersected by a mixed-severity wildfire that occurred one month after the LiDAR survey. Fire severity was assessed through the differenced Normalized Burn Ratio (dNBR) index computed from pre- and post-fire Sentinel-2A Level 2A scenes. In addition to continuous data, fire severity was also categorized (low or high) using appropriate dNBR thresholds for the plant communities in the study area. We computed several metrics related to the pre-fire distribution of surface and canopy fuels strata with a point cloud mean density of 10.9 m−2. The Random Forest (RF) algorithm was used to evaluate the capacity of the set of pre-fire LiDAR metrics to predict continuous and categorized fire severity. The accuracy of RF regression and classification model for continuous and categorized fire severity data, respectively, was remarkably high (pseudo-R2 = 0.57 and overall accuracy = 81%) considering that we only focused on variables related to fuel structure and loading. The pre-fire fuel metrics with the highest contribution to RF models were proxies for horizontal fuel continuity (fractional cover metric) and the distribution of fuel loads and canopy openness up to a 10 m height (density metrics), indicating increased fire severity with higher surface fuel load and higher horizontal and vertical fuel continuity. Results evidence that the technical specifications of LiDAR acquisitions framed within the áGiLTerFoRus project enable accurate fire severity predictions through point cloud data with high density.

Examining the Impacts of Pre-Fire Forest Conditions on Burn Severity Using Multiple Remote Sensing Platforms

“…Through our correlation and regression assessments, we found that certain vegetation and biomass attributes exhibited a stronger relationship with burn severity compared to other factors such as terrain. Prior research primarily concentrated on evaluating the fuel structure and loading solely through the utilization of dNBR indices [79], in contrast to the present investigation, which integrated an expanded array of structural and functional variables encompassing terrain and canopy attributes. Despite the relatively diminished overall predictive precision concerning the potential burn severity discerned within this study, it offers a means of identifying variables with greater likelihoods of predicting the burn severity.…”

Assessment of Burn Severity and Monitoring of the Wildfire Recovery Process in Mongolia

“…Many studies from the wildfire severity study field were reviewed. The studies were carried out using different satellite images, including optical [7][8][9][10], thermal [11,12], lidar [13], and synthetic aperture radar (SAR) [11,14,15] satellite images. Most of them used optical satellite images obtained from MODIS data, Sentinel-2, Landsat series images, and KOMPSAT-3A [16], which uses Shortwave Infrared (SWIR) bands for the calculation.…”

“…Other issues and different phenomena occur in different areas within different natural zones around the world. We collected and reviewed a few studies from different study areas, including Siberia, Russia [23,25,26], Indonesia [27], Canada [28,29], Australia [18,[30][31][32], Spain [33], Portugal [13], the Mediterranean [7,[34][35][36][37], Turkey [1,4], Greece [2,3], China [10,[38][39][40][41], California and Alaska [42][43][44], the US [45][46][47][48][49], Peru [14], Iran [50], Bolivia [51], the Amazon of Brazil [52] and India [53]. The wildfire studies from each country had their own characteristics.…”