Evaluating the Hyperspectral Sensitivity of the Differenced Normalized Burn Ratio for Assessing Fire Severity

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.

Characterization of vegetation structural changes using multi-temporal LiDAR and its relationship with severity in Calcena wildfire

La caracterización de los cambios estructurales y presencia de huecos tras el fuego puede proporcionar información muy relevante para comprender los efectos ecológicos de los incendios en ecosistemas mediterráneos. En el presente estudio se caracterizan estas variables tras el incendio de Calcena en masas forestales de pinar y encinar, y su relación con la severidad del mismo. Dicho incendio calcinó 4.573 hectáreas en 2012 afectando de forma parcial al Parque Natural de la Dehesa del Moncayo localizado en Aragón (España). Para ello se hace uso de información multi-temporal Light Detection and Ranging (LiDAR) de las coberturas de 2011 y 2016 del Plan Nacional de Ortofotografía Aérea (PNOA), así como imágenes Landsat 7 para estimar la severidad del incendio mediante el índice differenced Normalized Burn Ratio (dNBR). Se evalúan los cambios estructurales producidos utilizando métricas LiDAR pre y post-incendio, así como la distribución de los huecos en el dosel forestal, su tamaño, número y frecuencia, y se analizan sus correlaciones con la severidad del incendio. La severidad fue predominantemente baja (42.32 %) o mediabaja (30.38 %), y produjo una disminución de la altura, de la densidad del dosel forestal y de la diversidad estructural. El tamaño de los huecos se incrementó tras el incendio, reduciéndose el número de huecos pequeños e incrementándose aquellos de tamaño intermedio en torno a 0.2 ha. Los cambios en las métricas LiDAR relacionadas con la altura, variabilidad de la altura en el perfil vertical, y densidad del dosel forestal presentaron las mayores correlaciones, indicando que son las que sufren mayores modificaciones. Los resultados muestran el interés de utilizar los datos LiDAR para caracterizar cambios estructurales y apoyar decisiones en la gestión silvícola.

Evaluating the Differenced Normalized Burn Ratio for Assessing Fire Severity Using Sentinel-2 Imagery in Northeast Siberian Larch Forests

Fire severity is a key fire regime characteristic with high ecological and carbon cycle relevance. Prior studies on boreal forest fires primarily focused on mapping severity in North American boreal forests. However, the dominant tree species and their impacts on fire regimes are different between North American and Siberian boreal forests. Here, we used Sentinel-2 satellite imagery to test the potential for using the most common spectral index for assessing fire severity, the differenced Normalized Burn Ratio (dNBR), over two fire scars and 37 field plots in Northeast Siberian larch-dominated (Larix cajanderi) forests. These field plots were sampled into two different forest types: (1) dense young stands and (2) open mature stands. For this evaluation, the dNBR was compared to field measurements of the Geometrically structured Composite Burn Index (GeoCBI) and burn depth. We found a linear relationship between dNBR and GeoCBI using data from all forest types (R2 = 0.42, p < 0.001). The dNBR performed better to predict GeoCBI in open mature larch plots (R2 = 0.56, p < 0.001). The GeoCBI provides a holistic field assessment of fire severity yet is dominated by the effect of fire on vegetation. No significant relationships were found between GeoCBI components (overall and substrate stratum) and burn depth within our fires (p > 0.05 in all cases). However, the dNBR showed some potential as a predictor for burn depth, especially in the dense larch forests (R2 = 0.63, p < 0.001). In line with previous studies in boreal North America, the dNBR correlated reasonably well with field data of aboveground fire severity and showed some skills as a predictor of burn depth. More research is needed to refine spaceborne fire severity assessments in the larch forests of Northeast Siberia, including assessments of additional fire scars and integration of dNBR with other geospatial proxies of fire severity.

Evaluating the differenced Normalized Burn Ratio for assessing fire severity using Sentinel-2 imagery in Northeast Siberian larch forests

&lt;p&gt;Fire severity is a key fire regime characteristic with high ecological and carbon cycle relevance. Broadly defined, fire severity is a measure of the immediate impacts of a fire on the landscape, including the destruction and combustion of live vegetation and dead organic matter. Prior studies on boreal forest fires have mainly focused on mapping severity in North America&amp;#8217;s boreal forests. However, the dominant tree species and their impacts on fire regimes are strikingly different between boreal North America and Siberia. Here we used Sentinel-2 satellite imagery to test the potential for using the most common spectral index for assessing fire severity, the differenced Normalized Burn Ratio (dNBR), over two fire scars and 41 field plots in Northeast Siberia. These field plots, sampled in the summer of 2019, corresponded to three different forest types: dense larch-dominated (Larix cajanderii) forest, open larch-dominated forest and open forest with a mixture of larch and pine (Pinus sylvestris). For this evaluation, the dNBR was compared to field measurements of the Geo Composite Burn Index (GeoCBI) and burn depth. The dNBR performed better when the field data were grouped by forest type (e.g. GeoCBI- dNBR R&lt;sup&gt;2&lt;/sup&gt; = 0.38 (p &lt; 0.01) for all plots and 0.49 (p &lt; 0.001) for open larch forest). The GeoCBI provides a holistic field assessment of fire severity, yet it is dominated by the effect of fire on vegetation. Nevertheless, the GeoCBI correlated reasonably well with the depth of burning in the organic soil (R&lt;sup&gt;2&lt;/sup&gt; = 0.11, p &lt; 0.05 for all plots). This relationship also varied among forest types, and was the highest for the dense larch forests (burn depth- GeoCBI R&lt;sup&gt;2 &lt;/sup&gt;= 0.27, p &lt; 0.05). The dNBR showed some potential as a predictor for burn depth, especially in the dense larch forests (burn depth- dNBR R&lt;sup&gt;2&lt;/sup&gt; = 0.31, p &lt; 0.05). This is line with previous studies in boreal North America. More research is needed to refine spaceborne fire severity assessments in the larch forests of Northeast Siberia, including assessments of additional fire scars and integration of dNBR with other geospatial proxies of fire severity.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

Evaluating the Near and Mid Infrared Bi-Spectral Space for Assessing Fire Severity and Comparison with the Differenced Normalized Burn Ratio

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 (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 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 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 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 median 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.

A flexible approach for predicting and mapping postfire wood borer attacks in black spruce and jack pine forests using the differenced normalized burn ratio (dNBR)

Postfire salvage logging is used to reduce economic losses; however, burned trees are rapidly colonized by wood-boring insects, which reduce the merchantable value of the wood. This study aims to predict wood borer (Monochamus Megerle in Dejean, 1821) attacks after wildfire as a function of rapidly available variables such as tree basal area, stem diameter, and burn severity using the differenced normalized burn ratio (dNBR). In 2011, we sampled 60 black spruce (Picea mariana (Mill.) Britton, Sterns & Poggenb.) or jack pine (Pinus banksiana Lamb.) plots in five burns from 2010 in the Haute-Mauricie region of Quebec, Canada. A 50 cm bole section was debarked on seven trees in each plot to estimate wood borer attack density. Wood borer attacks were more abundant in black spruce than in jack pine. As a continuous variable, dNBR unveiled a quadratic effect of burn severity on attack density in black spruce, which was higher at moderate burn severity. In jack pine, the highest levels of attack density were found at high burn severity. Models produced in this article will help forest managers to better prioritize areas for salvage logging and thus reduce economic losses due to wood borer activity.

Assessment of Burned Forest Area Severity and Postfire Regrowth in Chapada Diamantina National Park (Bahia, Brazil) Using dNBR and RdNBR Spectral Indices

Fire scar detection through orbital data can be done using specific techniques, such as the use of spectral indices like the normalized burn ratio (NBR), which are designed to help identify burnt areas as they have typical spectral responses. This paper aims to characterize burn severity and regrowth in areas hit by three fires in the Chapada Diamantina National Park (Bahia, Brazil) and its surrounding area through the differenced normalized burn ratio (dNBR) and relative differenced normalized burn ratio (RdNBR) spectral indices. The data acquired were pretreated and prepared adequately to calculate the indices. We conclude that for the study area, considering the limitations of fieldwork, the multitemporal index dNBR and the relative index RdNBR are important tools for classifying burnt areas and can be used to assess the regrowth of vegetation.

Evaluación y cartografiado de la severidad de los grandes incendios ocurridos en el distrito forestal XIV (Verín-Viana) entre 2006-2016.

La evaluación y el cartografiado de la severidad del fuego y del nivel de afectación de las copas en áreas arboladas resulta esencial para proponer y priorizar acciones de rehabilitación urgentes posteriores a los incendios. El principal objetivo de este estudio es el cartografiado y la obtención de mapas de niveles de severidad en los incendios ocurridos en el distrito forestal XIV (Verín-Viana) durante el período 2006-2016. También se ha realizado una primera aproximación hacia la búsqueda de relaciones entre las clases más altas de severidad del fuego y de daño a las copas y determinadas variables topográficas, meteorológicas y de combustibles. El estudio, basado en el cálculo de los índices dNBR (differenced Normalized Burn Ratio) y RdNBR (Relative difference Normaliced Burn Ratio) a partir de imágenes Landsat, discrimina cuatro clases de severidad de fuego y cuatro niveles de daño a las copas. Las variables que más explican el porcentaje de superficie quemada de la clase de severidad más elevada y el nivel más alto de daño a las copas fueron la velocidad del viento y el porcentaje del área quemada con pendiente entre el 30 y el 45%.Se observó que el dNBR estima una mayor superficie quemada en severidad moderada y baja en comparación con el RdNBR que estima una mayor superficie quemada con alta severidad.

Recovery of biological soil crust richness and cover 12–16 years after wildfires in Idaho, USA

Changing fire regimes in western North America may impact biological soil crust (BSC) communities that influence many ecosystem functions, such as soil stability and C and N cycling. However, longer-term effects of wildfire on BSC abundance, species richness, functional groups, and ecosystem functions after wildfire (i.e., BSC resilience) are still poorly understood. We sampled BSC lichen and bryophyte communities at four sites in Idaho, USA, within foothill steppe communities that included wildfires from 12 to 16 years old. We established six plots outside each burn perimeter and compared them with six plots of varying severity within each fire perimeter at each site. BSC cover was most strongly negatively impacted by wildfire at sites that had well-developed BSC communities in adjacent unburned plots. BSC species richness was estimated to be 65 % greater in unburned plots compared with burned plots, and fire effects did not vary among sites. In contrast, there was no evidence that vascular plant functional groups or fire severity (as measured by satellite metrics differenced normalized burn ratio (dNBR) or relativized differenced normalized burn ratio (RdNBR)) significantly affected longer-term BSC responses. Three large-statured BSC functional groups that may be important in controlling wind and water erosion (squamulose lichens, vagrant lichens, and tall turf mosses) exhibited a significant decrease in abundance in burned areas relative to adjacent unburned areas. The decreases in BSC cover and richness along with decreased abundance of several functional groups suggest that wildfire can negatively impact ecosystem function in these semiarid ecosystems for at least 1 to 2 decades. This is a concern given that increased fire frequency is predicted for the region due to exotic grass invasion and climate change.