Disentangling the role of prefire vegetation vs. burning conditions on fire severity in a large forest fire in SE Spain

Abstract

Fire severity is a function of dynamic interactions between vegetation and burning conditions. To understand the factors that control it, accurate methods for estimating prefire vegetation structure and composition as well as fire propagation conditions are required. Here we analyzed the spatial variability of fire severity in a mixedseverity fire (3217 ha) that occurred in southeast Spain (Yeste, Albacete) from 27th July to 1th August 2017, burning mostly a pine woodland, including part of an earlier fire in 1994. Fire severity was estimated using three satellite-based indices derived from the Normalized Burn Ratio (NBR) using Sentinel 2 and Landsat 8 images from the dates before and immediately after fire. The field-based Composite Burn Index (CBI) was used for validation. Prefire vegetation conditions and fuel models were derived from LiDAR metrics and other vegetation data. Fire propagation conditions were estimated based on a fire progression map provided by the Forestry Services of Castilla-La Mancha. In addition, hourly fire weather and aligned (i.e., in the sense of the propagating fire-front) slope and wind speed were calculated for each burning period. Regression models using different spectral fire severity indices and their driving factors were obtained applying Boosted Regression Trees (BRTs). Fire severity was highly predicted by both burning conditions and prefire vegetation (mean adjusted R 2 [Adj.R 2 ]: 86% ± 0.04 and 68% ± 0.05 for training and validation sets, respectively). Alone, burning conditions explained more variance than LiDAR metrics and vegetation separately. The single variables that contributed most to the models were the rate of spread of the fire-front, biomass proxies (i.e., Leaf Area Index [LAI] and fraction of Photosynthetically Active Radiation [fPAR]) and understory vegetation (i.e., density of LiDAR points 1-2 m). Higher fire severity occurred in areas burning uphill, with a high rate of spread driven by high velocity winds and under high maximum temperature. Fire severity was high in wooded stands that were heterogeneous in height, composed by scattered and small Pinus halepensis trees, with high and homogeneous understory cover. In contrast, lower fire severity occurred in mature stands dominated by tall Pinus pinaster and Pinus nigra trees. There were strong interactions between vegetation, weather, fire-aligned topography and rate of spread. Because vegetation variables were important drivers of fire severity, even under extreme fire weather conditions, fuel management treatments to limit fire severity and, potentially, fire size should be implemented.

Recent bark beetle outbreaks influence wildfire severity in mixed‐conifer forests of the Sierra Nevada, California, USA

“…Plot-level weather data may be mismatched with plot data temporally because we have hourly weather data but do not know what time of day our plots burned, and spatially because few weather stations were appropriate to each site, yet much microsite weather variability exists. While our fire weather metric (daytime average RH) provides a reasonable estimate of broad scale burning conditions, a more nuanced relationship between prefire mortality, fire weather, and fire severity might be detected with more spatially and temporally specific weather metrics (e.g., Viedma et al 2020).…”

Drivers of fire severity shift as landscapes transition to an active fire regime, Klamath Mountains, USA

“…The breadth of fire effects included within "moderate severity" makes analysis of this category difficult (Lydersen et al 2016) and makes reburn severity difficult to predict due to the diversity of post-firevegetation responses in moderate-severity areas (Collins et al 2018). Future possibilities to improve statistical models of pixel-level fire severity such as ours include mapping sub-daily fire progression to more accurately characterize weather (Viedma et al 2020), incorporating more complete information on forest and fire management activities, and quantifying other aspects of weather such as atmospheric inversions that trap smoke (Estes et al 2017) and atmospheric instability leading to plume-driven fire behavior (Lydersen et al 2014).…”

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

“…We identified 100% of the trees selected from LiDAR in the field. Moreover, we found a very good match between the tree crowns delineated by low-density pre-fire LiDAR data [53] and the post-fire crown segments ( Figure S1). Finally, we related field-estimated DBH with the LiDAR-estimated tree height and crown area to assess the coherence of the results ( Figure S2).…”

“…The study area was the Yeste fire (province of Albacete, SE Spain), that occurred in summer 2017. The fire started on 27th July, one day before an incursion of warm, tropical Africa air, and was controlled on August 1st, after burning 3217 ha (see [53] for further details). High-density LiDAR flights were carried out six months after the fire on three areas of 5-6 ha size with different levels of fire severity and in an unburned area adjacent to the fire ( Figure 1a and RBR Sentinel 2 fire severity levels (rows) to broadly characterize each LiDAR flight by the RBR fire severity levels.…”

“…Correction of the images for surface reflectivity (level L2A) (hereafter 'Sentinel 2A') was performed using the Sen2Cor processing tool in SNAP 6.0 software [67]. All bands were resampled to 10 m pixel resolution, using the reference band B2, and a water and cloud mask, available for each scene in SNAP 6.0, were overlaid [53].We used the Normalized Burn Ratio (NBR) to calculate the Relativized Burnt Ratio (RBR) = (dNBR/(NBR prefire + 1.001)) ( [68]), which was the spectral fire severity index most related to field-based fire severity measures (see [53] for further details). Three fire severity levels were established based on RBR thresholds (i.e., <0.3, 0.3-0.5 and >0.5), following the results obtained by Viedma [53].…”