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Forest managers demand reliable tools to evaluate post-fire vegetation and soil damage. In this study, we quantify wildfire damage to vegetation and soil based on the analysis of burn severity, using multitemporal and multispectral satellite data and species distribution models, particularly maximum entropy (MaxEnt). We studied a mega-wildfire (9000 ha burned) in North-Western Spain, which occurred from 21 to 27 August 2017. Burn severity was measured in the field using the composite burn index (CBI). Burn severity of vegetation and soil layers (CBIveg and CBIsoil) was also differentiated. MaxEnt provided the relative contribution of each pre-fire and post-fire input variable on low, moderate and high burn severity levels, as well as on all severity levels combined (burned area). In addition, it built continuous suitability surfaces from which the burned surface area and burn severity maps were built. The burned area map achieved a high accuracy level (κ = 0.85), but slightly lower accuracy when differentiating the three burn severity classes (κ = 0.81). When the burn severity map was validated using field CBIveg and CBIsoil values we reached lower κ statistic values (0.76 and 0.63, respectively). This study revealed the effectiveness of the proposed multi-temporal MaxEnt based method to map fire damage accurately in Mediterranean ecosystems, providing key information to forest managers.

BackgroundThe southeastern United States (‘Southeast’) experiences high levels of fire activity, but the preponderance of small and prescribed fires means that existing burn severity products are incomplete across the region.AimsWe developed and applied a burn severity model across the Southeast to enhance our understanding of regional burn severity patterns.MethodsWe used Composite Burn Index (CBI) plot data from across the conterminous US (CONUS) to train a gradient-boosted decision tree model. The model was optimised for the Southeast and applied to the annual Landsat Burned Area product for 2000–2022 across the region.Key resultsThe burn severity model had a root mean square error (RMSE) of 0.48 (R2 = 0.70) and 0.50 (R2 = 0.37) for the CONUS and Southeast, respectively. The Southeast, relative to CONUS, had lower mean absolute residuals in low and moderate burn severity categories. Burn severity was consistently lower in areas affected by prescribed burns relative to wildfires.ConclusionsAlthough regional performance was limited by a lack of high burn severity CBI plots, the burn severity dataset demonstrated patterns consistent with low-severity, frequent fire regimes characteristic of Southeastern ecosystems.ImplicationsMore complete data on burn severity will enhance regional management of fire-dependent ecosystems and improve estimates of fuels and fire emissions.

As wildfire activity increases and fire‐size distributions potentially shift in many forested regions worldwide, anticipating the spatial patterns of burn severity expected with future fire activity is critical for ecological understanding and informing management and policy. Because spatial patterns of burn severity are influenced by a complex mixture of drivers, they remain difficult to predict for any given burned landscape. At broader extents, however, spatial scaling relationships relating high‐severity patch size and shape to overall fire size, when combined with scenarios regarding regional area burned and fire‐size distributions, offer a means to anticipate the spatial configuration of burn severity in future fires. Here, leveraging a satellite burn‐severity dataset for 1615 fire events occurring across the northwest United States between 1985 and 2020, we present an approach for simulating expected patch‐level burn‐severity patterns at the scale of a region or fire regime of interest. We demonstrate this approach in a historically climate‐limited fire regime within the Pacific Northwest, USA, where relatively infrequent but large and severe fires shape biomass‐rich forests, and where fire potential is projected to increase as summer fire seasons become warmer and drier. We quantify how, for a given total burned area, the range of cumulative burn‐severity patterns is expected to vary with the size distributions of fire events. Our results illustrate how shifts in fire‐size distributions toward larger fire events will lead to increasingly large high‐severity burn patches with interior areas that are increasingly far from unburned seed sources following fire. In contrast, the same total area burned in more numerous but smaller fire events will result in qualitatively different cumulative patterns of burn severity, characterized by smaller high‐severity patches and closer proximity to postfire seed sources across burned landscapes. These results have important implications in forested regions, informing management actions ranging from prefire planning (e.g., fire response preparedness) to real‐time decision‐making (e.g., fire suppression vs. managed wildfire use) and postfire responses (e.g., replanting to restore tree cover and/or promoting early‐seral habitat). The approach we present is generalizable and can be applied across regions and fire regimes to anticipate potential future fire effects.

Uncontrolled, large fires are a major threat to the biodiversity of protected heath landscapes. The severity of the fire is an important factor influencing vegetation recovery. We used airborne imaging spectroscopy data from the Airborne Prism Experiment (APEX) sensor to: (1) investigate which spectral regions and spectral indices perform best in discriminating burned from unburned areas; and (2) assess the burn severity of a recent fire in the Kalmthoutse Heide, a heathland area in Belgium. A separability index was used to estimate the effectiveness of individual bands and spectral indices to discriminate between burned and unburned land. For the burn severity analysis, a modified version of the Geometrically structured Composite Burn Index (GeoCBI) was developed for the field data collection. The field data were collected in four different vegetation types: Calluna vulgaris-dominated heath (dry heath), Erica tetralix-dominated heath (wet heath), Molinia caerulea (grass-encroached heath), and coniferous woodland. Discrimination between burned and unburned areas differed among vegetation types. For the pooled dataset, bands in the near infrared (NIR) spectral region demonstrated the highest discriminatory power, followed by short wave infrared (SWIR) bands. Visible wavelengths performed considerably poorer. The Normalized Burn Ratio (NBR) outperformed the other spectral indices and the individual spectral bands in discriminating between burned and unburned areas. For the burn severity assessment, all spectral bands and indices showed low correlations with the field data GeoCBI, when data of all pre-fire vegetation types were pooled (R2 maximum 0.41). Analysis per vegetation type, however, revealed considerably higher correlations (R2 up to 0.78). The Mid Infrared Burn Index (MIRBI) had the highest correlations for Molinia and Erica (R2 = 0.78 and 0.42, respectively). In Calluna stands, the Char Soil Index (CSI) achieved the highest correlations, with R2 = 0.65. In Pinus stands, the Normalized Difference Vegetation Index (NDVI) and the red wavelength both had correlations of R2 = 0.64. The results of this study highlight the superior performance of the NBR to discriminate between burned and unburned areas, and the disparate performance of spectral indices to assess burn severity among vegetation types. Consequently, in heathlands, one must consider a stratification per vegetation type to produce more reliable burn severity maps.

Background: Burn severity significantly increases the likelihood and volume of post-wildfire debris flows. Pre-fire severity predictions can expedite mitigation efforts because precipitation contributing to these hazards often occurs shortly after wildfires, leaving little time for post-fire planning and management.Aim: The aim of this study was to predict burn severity using pre-fire conditions of individual wildfire events and estimate potential post-fire debris flow to unburned areas.Methods: We used random forests to model dNBR from pre-fire weather, fuels, topography, and remotely sensed data. We validated our model predictions against post-fire observations and potential post-fire debris-flow hazard estimates.Key results: Fuels, pre-fire weather, and topography were important predictors of burn severity, although predictor importance varied between fires. Post-fire debris-flow hazard rankings from predicted burn severity (pre-fire) were similar to hazard assessments based on observed burn severity (post-fire).Conclusion: Predicted burn severity can serve as an input to post-fire debris-flow models before wildfires occur, antecedent to standard post-fire burn severity products. Assessing a larger set of fires under disparate conditions and landscapes will be needed to refine predictive models.Implications: Burn severity models based on pre-fire conditions enable the prediction of fire effects and identification of potential hazards to prioritise response and mitigation.

Forest inventory data are useful for determining forest stand structure, growth, and change. Among the information collected on forest inventory plots by the USDA Forest Service Forest Inventory and Analysis Program, attributes characterizing various types of disturbance provide researchers a means of selecting plots specifically affected by disturbances, such as fire. We determine the performance of three of these attributes as indicators of recent fires on forest inventory plots of the United States by comparing them to independent records of wildland fire occurrence. The indicators are plot-level observations of fire effects on (1) general site appearance, (2) tree mortality, and (3) damage to live trees. Independent spatial layers of wildland fire perimeters provide an approach to test indicator performance and identify characteristics of fires that may affect detection. The sensitivities of indicators are generally higher in the West relative to the East. Detection rates exceed 90 percent for the Pacific Coast forests but seldom reach 80 percent in the East. Among the individual indicators, site appearance has higher identification rates than tree indicators for fires in the Pacific Coast, Great Plains, North, and South regions. Tree mortality is the most important single indicator for identifying Rocky Mountain fires. Tree damage is more important than tree mortality in the South; otherwise, the tree damage indicator is of relatively lower importance, particularly where high-severity fires are common, and tree survival is low. The rate of detection by the indicators is affected by the severity of the fire or the recency of the fire. The joint effect of severity and recency influence all three indicators for the Pacific Coast and Rocky Mountain fires, as well as the site appearance indicator in the South. Only a small proportion of fires are clearly missed by all three of the indicators.

Straw mulch is commonly applied to land after high-severity wildfires because of its effectiveness in reducing post-fire runoff and erosion. However, information about the effect on vegetation recovery is still scarce and usually limited to the first 2 years after wildfire. In this study, the effects of straw helimulching on vegetation recovery and species composition were assessed in 30 experimental plots established in four shrubland areas in north-west Spain 5 years after wildfire. The influence of the treatment on biomass accumulation in the medium term was also assessed. The relationships between soil burn severity, site characteristics (altitude, aspect, soil depth and percentage of stoniness) and the vegetation variables (total vegetation cover, weighted mean height of vegetation, total fuel load and litter and duff load) were also explored. Overall, the mulching treatment did not have significant effects on the variables studied. In the mulched plots, no non-native species were recorded 5 years after the wildfire. Site characteristics significantly affected the vegetation complex, but soil burn severity did not have any residual effect. The study findings indicate that straw helimulching has neutral effects on vegetation cover and composition in coastal shrublands in NW Spain in the medium term.

Shrubland and forestland covers are highly prone to fire. The Normalized Difference Vegetation Index (NDVI) has been widely used for biomass quantitative assessment. The objectives of this study were as follows: (1) to compute the NDVI annual curve for two types of land cover eucalypts and shrubland areas; (2) to collect field data in these two types of land cover to estimate aboveground biomass (AGB); and (3) to produce AGB maps for eucalypts and shrubland areas by modelling AGB with NDVI, validate them with other data sources, and to compare fuel loads with fire severity levels. A study area in the central inland region of Portugal was considered. The wildfire on 4 August 2023 was considered for burn severity levels assessment using the Normalized Burn Index (NRB). The Sentinel-2 MSI imagery was used to compute the NDVI for the years of 2022 and 2023 and the NBR for the pre-fire and post-fire dates. The NDVI annual curve for 2022 showed a minimum observed between July and August, in accordance with the climatological data, and allowed differentiating eucalypts from shrubland areas. Spectral signatures also confirmed this differentiation. The fitted linear models for AGB prediction using the NDVI imagery showed good fitting performances (R2 of 0.76 and 0.77). The AGB maps provided a relevant decision support tool for forest management and for fire hazard and fire severity mitigation. Further research is needed using more robust datasets for an independent validation of the model.

Wildfires have nearly become a guaranteed annual event in most western National Forests. Severe fire effects can be mitigated with a goal of minimizing the hydrologic response and promoting soil and vegetation recovery towards the pre-disturbance condition. Sometimes, post-fire actions include salvage logging to recover timber value and to remove excess fuels. Salvage logging was conducted after three large wildfires on the Lolo National Forest in Montana, USA, between 2017 and 2019. We evaluated detrimental soil disturbance (DSD) on seven units that were burned at low, moderate, and high soil burn severity in 2022, three to five years after the logging occurred. We found a range of exposed soil of 5%–25% and DSD from 3% to 20%, and these values were significantly correlated at r = 0.88. Very-high-resolution WorldView-2 imagery that coincided with the field campaign was used to calculate Normal Differenced Vegetation Index (NDVI) across the salvaged areas; we found that NDVI values were significantly correlated to DSD at r = 0.87. We were able to further examine this relationship and determined NDVI threshold values that corresponded to high-DSD areas, as well as develop a model to estimate the contributions of equipment type, seasonality, topography, and burn severity to DSD. A decision-making tool which combines these factors and NDVI is presented to support land managers in planning, evaluating, and monitoring disturbance from post-fire salvage logging.

Forest fires can have a direct and immediate impact on soil properties, particularly soil water repellency. This study investigated the direct impacts of the Gangneung forest fire of 2019 on soil properties and the spatial variability of soil water repellency with vegetation burn severity in the Korean red pine (Pinus densiflora Siebold and Zucc) forest of South Korea. A total of 36 soil samples were collected at depth intervals of 0–5 cm, 10–15 cm, and 20–25 cm from three burned sites, representing surface-fuel consumption (SC), foliage necrosis (FN), and crown-fuel consumption (CC), respectively. An unburned site was also used as a control. Soil properties such as soil texture, pH, bulk density, electrical conductivity (EC), total organic carbon (TOC), and cation exchange capacity (CEC) were analyzed in the laboratory. The increase in the sand fraction near the soil surface after a fire was associated with changes in silt and clay fractions. Moderate to high vegetation burn severity at the FN and CC sites caused a decrease in soil pH due to the thermal destruction of kaolinite mineral structure, but organic matter combustion on the soil surface increased soil pH at the SC site. Forest fires led to increases in total organic carbon at the FN and SC sites, owing to the external input of heat damaged foliage and burnt materials. Molarity of an ethanol droplet (MED) tests were also conducted to measure the presence and intensity of soil water repellency from different locations and soil depths. MED tests showed that vegetation burn severity was important for determining the strength of water repellency, because severely burned sites tended to have stronger water repellency of soil than slightly burned sites. Unburned soils had very hydrophilic characteristics across soil depths, but a considerably thick hydrophobic layer was found in severely burned sites. The soil water repellency tended to be stronger on steep (>30°) slopes than on gentle (<15°) slopes.