Fire severity represents fire-induced environmental changes and is an important variable for modeling fire emissions and planning post-fire rehabilitation. Remotely sensed fire severity is traditionally evaluated using the differenced normalized burn ratio (dNBR) derived from multispectral imagery. This spectral index is based on bi-temporal differenced reflectance changes caused by fires in the near-infrared (NIR) and short-wave infrared (SWIR) spectral regions. Our study aims to evaluate the spectral sensitivity of the dNBR using hyperspectral imagery by identifying the optimal bi-spectral NIR SWIR combination. This assessment made use of a rare opportunity arising from the pre- and post-fire airborne image acquisitions over the 2013 Rim and 2014 King fires in California with the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. The 224 contiguous bands of this sensor allow for 5760 unique combinations of the dNBR at a high spatial resolution of approximately 15 m. The performance of the hyperspectral dNBR was assessed by comparison against field data and the spectral optimality statistic. The field data is composed of 83 in situ measurements of fire severity using the Geometrically structured Composite Burn Index (GeoCBI) protocol. The optimality statistic ranges between zero and one, with one denoting an optimal measurement of the fire-induced spectral change. We also combined the field and optimality assessments into a combined score. The hyperspectral dNBR combinations demonstrated strong relationships with GeoCBI field data. The best performance of the dNBR combination was derived from bands 63, centered at 0.962 µm, and 218, centered at 2.382 µm. This bi-spectral combination yielded a strong relationship with GeoCBI field data of R² = 0.70 based on a saturated growth model and a median spectral index optimality statistic of 0.31. Our hyperspectral sensitivity analysis revealed optimal NIR and SWIR bands for the composition of the dNBR that are outside the ranges of the NIR and SWIR bands of the Landsat 8 and Sentinel-2 sensors. With the launch of the Precursore Iperspettrale Della Missione Applicativa (PRISMA) in 2019 and several planned spaceborne hyperspectral missions, such as the Environmental Mapping and Analysis Program (EnMAP) and Surface Biology and Geology (SBG), our study provides a timely assessment of the potential and sensitivity of hyperspectral data for assessing fire severity.

Fire severity, defined as the degree of environmental change caused by a fire, is a critical fire regime attribute of interest to fire emissions modelling and post-fire rehabilitation planning. Remotely sensed fire severity is traditionally assessed by the differenced normalized burn ratio (dNBR). This spectral index captures fire-induced reflectance changes in the near infrared (NIR) and short-wave infrared (SWIR) spectral regions. This study evaluates a spectral index based on a band combination including the NIR and mid infrared (MIR) spectral regions, the differenced normalized difference vegetation index with mid infrared (dNDVI_MID), to assess fire severity. This evaluation capitalized upon the unique opportunity stemming from the pre- and post-fire airborne acquisitions over the Rim (2013) and King (2014) fires in California with the MODIS/ASTER Airborne Simulator (MASTER) instrument. The field data consist of 85 Geometrically structured Composite Burn Index (GeoCBI) plots. In addition, six different index combinations, respectively three with a NIR–SWIR combination and three with a NIR–MIR combination, were evaluated based on the optimality of fire-induced spectral displacements. The optimality statistic ranges between zero and one, with values of one representing pixel displacements that are unaffected by noise. The results show that the dNBR demonstrated a stronger relationship with GeoCBI field data when field measurements over the two fire scars were combined than the dNDVI_MID approaches. The results yielded an R² of 0.68 based on a saturated growth model for the best performing dNBR index, whereas the performance of the dNDVI_MID indices was lower with an R² = 0.61 for the best performing dNDVI_MID index. The dNBR also outperformed the dNDVI_MID in terms of spectral optimality across both fires. The best performing dNBR index yielded median optimality statistics of 0.56 over the Rim and 0.60 over the King fire. The best performing dNDVI_MID index recorded optimality values of 0.49 over the Rim and 0.46 over the King fire. We also found that the dNBR approach led to considerable differences in the form of the relationship with the GeoCBI between the two fires, whereas the dNDVI_MID approach yielded comparable relationships with the GeoCBI over the two fires. This suggests that the dNDVI_MID approach, despite its slightly lower performance than the dNBR, may be a more robust method for estimating and comparing fire severity over large regions. This premise needs additional verification when more air- or spaceborne imagery with NIR and MIR bands will become available with a spatial resolution that allows ground truthing of fire severity.

Reducing the climate change-induced risk of uncontrollable fires in landscapes under nature management, with severe impacts on landscape and society, is particularly urgent in densely-populated and fragmented areas. Reducing fire risk in such areas requires active involvement of a wide diversity of stakeholders. This research letter investigates stakeholders’ needs with regard to fire risk reduction in the Veluwe area in the Netherlands. This densely populated landscape is a popular tourist attraction, and it is one of the most fire-prone landscapes of the Netherlands, with abundant fuels and human ignition sources. We draw upon seven in-depth qualitative interviews with key stakeholders in the Veluwe area, which we situate in a wider review of existing literature. Our analysis demonstrates that the rising incidence of uncontrollable fires poses four types of new challenges to these stakeholders in the Veluwe area. First, stakeholders express the need to reshape existing policy tools and develop novel ones that create synergies between existing policy-priorities (e.g. biodiversity conservation) and fire risk reduction. Second, stakeholders argue for a critical rethinking of the value of landscapes in society, and the diverse roles that fire may play in landscape management research and practice. Third, developing such policy tools requires new modalities and platforms for multi-stakeholder and multi-level collaboration, which are currently lacking because the current and expected future risk of uncontrollable fire is unprecedented. And fourth, the development of effective policy tools requires new knowledge that is interdisciplinary, sensitive towards the local social and ecological characteristics of the area, and which approaches current fire risk challenges and their possible solutions dynamically. While our stakeholder analysis is specific to the Veluwe area in the Netherlands, our findings are also likely to be relevant to other fire-prone nature areas in fragmented landscapes, particularly in Northwestern Europe.

Fire severity, defined as the degree of environmental change caused by a fire, is a critical fire regime attribute of interest to fire emissions modelling and post-fire rehabilitation planning. Remotely sensed fire severity is traditionally assessed by the differenced normalized burned ratio (dNBR). This spectral index captures fire-induced reflectance changes in the near infrared (NIR) and short-wave infrared (SWIR) spectral regions. This study evaluates a spectral index based on a band combination including the NIR and mid infrared (MIR) spectral regions, the differenced normalized difference vegetation index (dNDVIMID), to assess fire severity. This evaluation capitalized upon the unique opportunity stemming from the pre- and post-fire airborne acquisitions over the Rim (2013) and King (2014) fires in California with the MODIS/ASTER (MASTER) instrument. The field data consists of 85 Geometrically structured Composite Burn Index (GeoCBI) plots. In addition, six different index combinations, respectively three with a NIR-SWIR combination and three with a NIR-MIR combination, were evaluated based on the optimality of fire-induced spectral displacements. The optimality statistic ranges between zero and one, with values of one representing pixel displacements that are unaffected by noise. Results show that the dNBR demonstrated a stronger relationship with GeoCBI field data when field measurements over the two fire scars were combined than the dNDVIMID approaches. The results yielded an R2 of 0.68 based on a saturated growth model for the best performing dNBR index, whereas the performance of the dNDVIMID indices was clearly lower with an R2 = 0.61 for the best performing dNDVIMID index. The dNBR also outperformed the dNDVIMID in terms of spectral optimality across both fires. The best performing dNBR index yielded the optimality statistics of 0.56 over the Rim and 0.60 over the King fire. The best performing dNDVIMID, index recorded optimality values of 0.49 over the Rim and 0.46 over the King fire. We also found that the dNBR approach led to considerable differences in the form of the relationship with the GeoCBI between the two fires, whereas the dNDVIMID approach yielded comparable relationships with the GeoCBI over the two fires. This suggests that the dNDVIMID approach, despite its slightly lower performance than the dNBR, may be a more robust method for estimating and comparing fire severity over large regions. This premise needs additional verification when more air- or spaceborne imagery with NIR and MIR bands will become available with a spatial resolution that allows ground truthing of fire severity. 

The boreal forest biome is warming rapidly, impacting disturbance regimes and global climate. Boreal forest fires have intensified, initiating both climate warming (positive) and climate cooling (negative) impacts across spatial and temporal scales. Here we estimate climate impacts from boreal fires in Alaska and western Canada between 2001 and 2019 using integrated net radiative forcing metrics combining greenhouse gas and aerosol emissions from combustion, vegetation recovery, greenhouse gas emissions from fire-induced permafrost thaw and changes in surface albedo over a 70-year period. We find that fires across Alaska contributed, on average, to net climate warming (0.35 ± 4.66 W m ⁻² of burned area; one standard deviation), while fires across Canada contributed to net cooling (−2.88 ± 4.17 W m ⁻² of burned area; one standard deviation). Climate-warming fires occur preferentially in dry, high-elevation, steep permafrost landscapes with high pre-fire black spruce coverage and combust more carbon per unit area. Climate-cooling fires are driven by longer spring snow exposure and occur more frequently in continental regions near the treeline. This fine-scale characterization of component and net radiative forcing advances our understanding of the biogeophysical impacts of fires on high-latitude climate and highlights the need to prioritize fire management in carbon-rich permafrost regions to curb long-term warming.