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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. Extreme Winds Alter Influence of Fuels and Topography on Megafire Burn Severity in Seasonal Temperate Rainforests under Record Fuel Aridity

   https://doi.org/10.3390/fire5020041  

   Evers, Cody ; Holz, Andrés ; Busby, Sebastian ; Nielsen-Pincus, Max ( April 2022 , Fire)

   Nearly 0.8 million hectares of land were burned in the North American Pacific Northwest (PNW) over two weeks under record-breaking fuel aridity and winds during the extraordinary 2020 fire season, representing a rare example of megafires in forests west of the Cascade Mountains. We quantified the relative influence of weather, vegetation, and topography on patterns of high burn severity (>75% tree mortality) among five synchronous megafires in the western Cascade Mountains. Despite the conventional wisdom in climate-limited fire regimes that regional drivers (e.g., extreme aridity, and synoptic winds) overwhelm local controls on vegetation mortality patterns (e.g., vegetation structure and topography), we hypothesized that local controls remain important influences on burn severity patterns in these rugged forested landscapes. To study these influences, we developed remotely sensed fire extent and burn severity maps for two distinct weather periods, thereby isolating the effect of extreme east winds on drivers of burn severity. Our results confirm that wind was the major driver of the 2020 megafires, but also that both vegetation structure and topography significantly affect burn severity patterns even under extreme fuel aridity and winds. Early-seral forests primarily concentrated on private lands, burned more severely than their older and taller counterparts, over the entire megafire event regardless of topography. Meanwhile, mature stands burned severely only under extreme winds and especially on steeper slopes. Although climate change and land-use legacies may prime temperate rainforests to burn more frequently and at higher severities than has been historically observed, our work suggests that future high-severity megafires are only likely to occur during coinciding periods of heat, fuel aridity, and extreme winds. 

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3. Relationships between Wildfire Burn Severity, Cavity-Nesting Bird Assemblages, and Habitat in an Eastern Ponderosa Pine Forest

   https://doi.org/10.1674/0003-0031-181.1.1  

   Keele, Emma C. ; Donovan, Victoria M. ; Roberts, Caleb P. ; Nodskov, Sarah M. ; Wonkka, Carissa L. ; Allen, Craig R. ; Powell, Larkin A. ; Wedin, David A. ; Angeler, David G. ; Twidwell, Dirac ( January 2019 , The American Midland Naturalist)
4. Assessing Wildfire Burn Severity and Its Relationship with Environmental Factors: A Case Study in Interior Alaska Boreal Forest

   https://doi.org/10.3390/rs13101966  

   Smith, Christopher W ; Panda, Santosh K ; Bhatt, Uma S ; Meyer, Franz J ; Badola, Anushree ; Hrobak, Jennifer L ( May 2021 , Remote Sensing)

   null (Ed.)

   In recent years, there have been rapid improvements in both remote sensing methods and satellite image availability that have the potential to massively improve burn severity assessments of the Alaskan boreal forest. In this study, we utilized recent pre- and post-fire Sentinel-2 satellite imagery of the 2019 Nugget Creek and Shovel Creek burn scars located in Interior Alaska to both assess burn severity across the burn scars and test the effectiveness of several remote sensing methods for generating accurate map products: Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), and Random Forest (RF) and Support Vector Machine (SVM) supervised classification. We used 52 Composite Burn Index (CBI) plots from the Shovel Creek burn scar and 28 from the Nugget Creek burn scar for training classifiers and product validation. For the Shovel Creek burn scar, the RF and SVM machine learning (ML) classification methods outperformed the traditional spectral indices that use linear regression to separate burn severity classes (RF and SVM accuracy, 83.33%, versus NBR accuracy, 73.08%). However, for the Nugget Creek burn scar, the NDVI product (accuracy: 96%) outperformed the other indices and ML classifiers. In this study, we demonstrated that when sufficient ground truth data is available, the ML classifiers can be very effective for reliable mapping of burn severity in the Alaskan boreal forest. Since the performance of ML classifiers are dependent on the quantity of ground truth data, when sufficient ground truth data is available, the ML classification methods would be better at assessing burn severity, whereas with limited ground truth data the traditional spectral indices would be better suited. We also looked at the relationship between burn severity, fuel type, and topography (aspect and slope) and found that the relationship is site-dependent. 

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5. Measuring collagen injury depth for burn severity determination using polarization sensitive optical coherence tomography

   https://doi.org/10.1038/s41598-022-14326-3  

   Cannon, Taylor M. ; Uribe-Patarroyo, Néstor ; Villiger, Martin ; Bouma, Brett E. ( June 2022 , Scientific Reports)

   Determining the optimal treatment course for a dermatologic burn wound requires knowledge of the wound’s severity, as quantified by the depth of thermal damage. In current clinical practice, burn depth is inferred based exclusively on superficial visual assessment, a method which is subject to substantial error rates in the classification of partial thickness (second degree) burns. Here, we present methods for direct, quantitative determination of the depth extent of injury to the dermal collagen matrix using polarization-sensitive optical coherence tomography (PS-OCT). By visualizing the depth-dependence of the degree of polarization of light in the tissue, rather than cumulative retardation, we enable direct and volumetric assessment of local collagen status. We further augment our PS-OCT measurements by visualizing adnexal structures such as hair follicles to relay overall dermal viability in the wounded region. Our methods, which we have validated ex vivo with matched histology, offer an information-rich tool for precise interrogation of burn wound severity and healing potential in both research and clinical settings.

    

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