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1. Field-Validated Burn-Severity Mapping in North Patagonian Forests

   https://doi.org/10.3390/rs12020214  

   Franco, María Guadalupe; Mundo, Ignacio A.; Veblen, Thomas T. (January 2020, Remote Sensing)

   Burn severity, which can be reliably estimated by validated spectral indices, is a key element for understanding ecosystem dynamics and informing management strategies. However, in North Patagonian forests, where wildfires are a major disturbance agent, studies aimed at the field validation of spectral indices of burn severity are scarce. The aim of this work was to develop a field validated methodology for burn-severity mapping by studying two large fires that burned in the summer of 2013–2014 in forests of Araucaria araucana and other tree species. We explored the relation between widely used spectral indices and a field burn-severity index, and we evaluated index performance by examining index sensitivity in discriminating burn-severity classes in different vegetation types. For those indices that proved to be suitable, we adjusted the class thresholds and constructed confusion matrices to assess their accuracy. Burn severity maps of the studied fires were generated using the two most accurate methods and were compared to evaluate their level of agreement. Our results confirm that reliable burn severity estimates can be derived from spectral indices for these forests. Two severity indices, the delta normalized burn ratio (dNBR) and delta normalized difference vegetation index (dNDVI), were highly related to the fire-induced changes observed in the field, but the strength of these associations varied across the five different vegetation types defined by tree heights and tree and tall shrub species regeneration strategies. The thresholds proposed in this study for these indices generated classifications with global accuracies of 82% and Kappa indices of 70%. Both the dNBR and dNDVI classification approaches were more accurate in detecting high severity, but to a lesser degree for detecting low severity burns. Moderate severity was poorly classified, with producer and user errors reaching 50%. These constraints, along with detected differences in separability, need to be considered when interpreting burn severity maps generated using these methods. 

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2. A global forest burn severity dataset from Landsat imagery (2003–2016)

   https://doi.org/10.5194/essd-16-3061-2024  

   He, Kang; Shen, Xinyi; Anagnostou, Emmanouil N (January 2024, Earth System Science Data)

   Abstract. Forest fires, while destructive and dangerous, are important to the functioning and renewal of ecosystems. Over the past 2 decades, large-scale, severe forest fires have become more frequent globally, and the risk is expected to increase as fire weather and drought conditions intensify. To improve quantification of the intensity and extent of forest fire damage, we have developed a 30 m resolution global forest burn severity (GFBS) dataset of the degree of biomass consumed by fires from 2003 to 2016. To develop this dataset, we used the Global Fire Atlas product to determine when and where forest fires occurred during that period and then we overlaid the available Landsat surface reflectance products to obtain pre-fire and post-fire normalized burn ratios (NBRs) for each burned pixel, designating the difference between them as dNBR and the relative difference as RdNBR. We compared the GFBS dataset against the Canada Landsat Burned Severity (CanLaBS) product, showing better agreement than the existing Moderate Resolution Imaging Spectrometer (MODIS)-based global burn severity dataset (MOdis burn SEVerity, MOSEV) in representing the distribution of forest burn severity over Canada. Using the in situ burn severity category data available for the 2013 wildfires in southeastern Australia, we demonstrated that GFBS could provide burn severity estimation with clearer differentiation between the high-severity and moderate-/low-severity classes, while such differentiation among the in situ burn severity classes is not captured in the MOSEV product. Using the CONUS-wide composite burn index (CBI) as a ground truth, we showed that dNBR from GFBS was more strongly correlated with CBI (r=0.63) than dNBR from MOSEV (r=0.28). RdNBR from GFBS also exhibited better agreement with CBI (r=0.56) than RdNBR from MOSEV (r=0.20). On a global scale, while the dNBR and RdNBR spatial patterns extracted by GFBS are similar to those of MOSEV, MOSEV tends to provide higher burn severity levels than GFBS. We attribute this difference to variations in reflectance values and the different spatial resolutions of the two satellites. The GFBS dataset provides a more precise and reliable assessment of burn severity than existing available datasets. These enhancements are crucial for understanding the ecological impacts of forest fires and for informing management and recovery efforts in affected regions worldwide. The GFBS dataset is freely accessible at https://doi.org/10.5281/zenodo.10037629 (He et al., 2023). 

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3. Modeling wildland fire burn severity in California using a spatial Super Learner approach

   https://doi.org/10.1007/s10651-024-00601-1  

   Simafranca, Nicholas; Willoughby, Bryant; O’Neil, Erin; Farr, Sophie; Reich, Brian J.; Giertych, Naomi; Johnson, Margaret C.; Pascolini-Campbell, Madeleine A. (March 2024, Environmental and Ecological Statistics)

   Given the increasing prevalence of wildland fires in the Western US, there is a crit- ical need to develop tools to understand and accurately predict burn severity. We develop a novel machine learning model to predict post-fire burn severity using pre- fire remotely sensed data. Hydrological, ecological, and topographical variables col- lected from four regions of California — the site of the Kincade fire (2019), the CZU Lightning Complex fire (2020), the Windy fire (2021), and the KNP Fire (2021) — are used as predictors of the differenced normalized burn ratio. We hypothesize that a Super Learner (SL) algorithm that accounts for spatial autocorrelation using Vec- chia’s Gaussian approximation will accurately model burn severity. We use a cross- validation study to show that the spatial SL model can predict burn severity with reasonable classification accuracy, including high burn severity events. After fitting and verifying the performance of the SL model, we use interpretable machine learn- ing tools to determine the main drivers of severe burn damage, including greenness, elevation, and fire weather variables. These findings provide actionable insights that enable communities to strategize interventions, such as early fire detection systems, pre-fire season vegetation clearing activities, and resource allocation during emer- gency responses. When implemented, this model has the potential to minimize the loss of human life, property, resources, and ecosystems in California. 

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4. Experimental warming has limited impacts on post‐fire succession across a burn severity gradient

   https://doi.org/10.1111/jvs.13248  

   Taber, Ethan M.; Mitchell, Rachel M. (March 2024, Journal of Vegetation Science)

   Abstract QuestionsAnthropogenic climate change is causing increases in the severity of wildland fire in many parts of the world. At the same time, post‐fire succession is occurring under new, warmer temperatures that are projected to continue increasing. Despite this, the combined effects of uncharacteristically high burn severity and increased ambient temperature on post‐fire community composition remain poorly understood. We ask how post‐fire forest understorey community composition and species richness are influenced by (1) burn severity, (2) experimental warming, and (3) years since fire. LocationMuseum Fire Scar,Pinus ponderosaforest, Arizona, United States. MethodsWe established 120 1‐m²quadrats in unburned, low‐ and high‐severity locations nine months after a mixed‐severity fire. Half of the plots were subject to experimental warming via open‐top warming chambers that elevated daytime temperatures by 1.079°C, simulating near‐term anthropogenic warming. Plant composition data were collected annually for three years. Relationships between community composition, burn severity, and experimental warming were analyzed using repeated‐measures PERMANOVA and linear mixed‐effects models. ResultsComposition differed significantly according to burn severity, time since fire, and their interaction, while experimental warming did not significantly influence composition. Species richness significantly increased in burned areas compared to unburned control within two years of fire. ConclusionsOur results suggest that near‐term temperature increases, driven by anthropogenic climate change, will have little effect on community composition relative to fire severity. High‐severity fire drove large, rapid changes in plant composition compared to unburned controls, favoring exotic annuals in a historically perennial‐dominated plant community. 

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5. Effects of burn severity on organic nitrogen and carbon chemistry in high-elevation forest soils

   https://doi.org/10.1016/j.seh.2023.100023  

   Roth, Holly K; McKenna, Amy M; Simpson, Myrna J; Chen, Huan; Srikanthan, Nivetha; Fegel, Timothy S; Nelson, Amelia R; Rhoades, Charles C; Wilkins, Michael J; Borch, Thomas (September 2023, Soil & Environmental Health)