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

2021 ◽  
Vol 13 (22) ◽  
pp. 4611
Author(s):  
Max J. van Gerrevink ◽  
Sander Veraverbeke
Keyword(s):  
Spectral Index ◽  
Field Data ◽  
Near Infrared ◽  
Environmental Changes ◽  
Fire Severity ◽  
Hyperspectral Data ◽  
Landsat 8 ◽  
Fire Emissions ◽  
Differenced Normalized Burn Ratio ◽  
Normalized Burn Ratio

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.

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

2021 ◽  
Vol 13 (4) ◽  
pp. 695
Author(s):  
Max J. van Gerrevink ◽  
Sander Veraverbeke
Keyword(s):  
Spectral Index ◽  
Field Data ◽  
Vegetation Index ◽  
Fire Regime ◽  
Fire Severity ◽  
Spectral Space ◽  
Mid Infrared ◽  
Fire Emissions ◽  
Differenced Normalized Burn Ratio ◽  
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.

Evaluating the near and mid infrared bi-spectral space for assessing fire severity and comparison with the differenced normalized burn ratio

2021 ◽  
Author(s):  
Max van Gerrevink ◽  
Sander Veraverbeke
Keyword(s):  
Spectral Index ◽  
Field Data ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Fire Regime ◽  
Fire Severity ◽  
Spectral Space ◽  
Mid Infrared ◽  
Fire Emissions

<p>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 (dNDVI<sub>MID</sub>), 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 dNDVI<sub>MID</sub> approaches. The results yielded an R<sup>2</sup> of 0.68 based on a saturated growth model for the best performing dNBR index, whereas the performance of the dNDVI<sub>MID </sub>indices was clearly lower with an R<sup>2</sup> = 0.61 for the best performing dNDVI<sub>MID </sub>index. The dNBR also outperformed the dNDVI<sub>MID</sub> 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 dNDVI<sub>MID, </sub>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<sub>MID</sub> approach yielded comparable relationships with the GeoCBI over the two fires. This suggests that the dNDVI<sub>MID</sub> 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. </p>

Fire and burn severity assessment: Calibration of Relative Differenced Normalized Burn Ratio (RdNBR) with field data

2019 ◽  
Vol 235 ◽  
pp. 342-349 ◽  
Author(s):  
Adrián Cardil ◽  
Blas Mola-Yudego ◽  
Ángela Blázquez-Casado ◽  
José Ramón González-Olabarria
Keyword(s):  
Field Data ◽  
Burn Severity ◽  
Severity Assessment ◽  
Differenced Normalized Burn Ratio ◽  
Normalized Burn Ratio

scholarly journals LANDSAT-BASED DETECTION AND SEVERITY ANALYSIS OF BURNED SUGARCANE PLOTS IN TARLAC, PHILIPPINES USING DIFFERENCED NORMALIZED BURN RATIO (dNBR)

2016 ◽  
Vol XLII-4/W1 ◽  
pp. 173-179 ◽  
Author(s):  
A. B. Baloloy ◽  
A. C. Blanco ◽  
B. S. Gana ◽  
R. C. Sta. Ana ◽  
L. C. Olalia
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
The Philippines ◽  
Local Economy ◽  
Burn Severity ◽  
Burned Area ◽  
Landsat 8 ◽  
Potential Yield ◽  
Differenced Normalized Burn Ratio ◽  
Normalized Burn Ratio

The Philippines has a booming sugarcane industry contributing about PHP 70 billion annually to the local economy through raw sugar, molasses and bioethanol production (SRA, 2012). Sugarcane planters adapt different farm practices in cultivating sugarcane, one of which is cane burning to eliminate unwanted plant material and facilitate easier harvest. Information on burned sugarcane extent is significant in yield estimation models to calculate total sugar lost during harvest. Pre-harvest burning can lessen sucrose by 2.7% - 5% of the potential yield (Gomez, et al 2006; Hiranyavasit, 2016). This study employs a method for detecting burn sugarcane area and determining burn severity through Differenced Normalized Burn Ratio (dNBR) using Landsat 8 Images acquired during the late milling season in Tarlac, Philippines. Total burned area was computed per burn severity based on pre-fire and post-fire images. Results show that 75.38% of the total sugarcane fields in Tarlac were burned with post-fire regrowth; 16.61% were recently burned; and only 8.01% were unburned. The monthly dNBR for February to March generated the largest area with low severity burn (1,436 ha) and high severity burn (31.14 ha) due to pre-harvest burning. Post-fire regrowth is highest in April to May when previously burned areas were already replanted with sugarcane. The maximum dNBR of the entire late milling season (February to May) recorded larger extent of areas with high and low post-fire regrowth compared to areas with low, moderate and high burn severity. Normalized Difference Vegetation Index (NDVI) was used to analyse vegetation dynamics between the burn severity classes. Significant positive correlation, rho = 0.99, was observed between dNBR and dNDVI at 5% level (p = 0.004). An accuracy of 89.03% was calculated for the Landsat-derived NBR validated using actual mill data for crop year 2015-2016.

scholarly journals Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform

2020 ◽  
Vol 9 (10) ◽  
pp. 564 ◽  
Author(s):  
Elgar Barboza Castillo ◽  
Efrain Y. Turpo Cayo ◽  
Cláudia Maria de Almeida ◽  
Rolando Salas López ◽  
Nilton B. Rojas Briceño ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Near Infrared ◽  
Google Earth ◽  
Landsat 8 ◽  
Peruvian Amazon ◽  
Great Loss ◽  
Mid Infrared ◽  
Normalized Burn Ratio ◽  
Burnt Areas ◽  
Sentinel 2

During the latest decades, the Amazon has experienced a great loss of vegetation cover, in many cases as a direct consequence of wildfires, which became a problem at local, national, and global scales, leading to economic, social, and environmental impacts. Hence, this study is committed to developing a routine for monitoring fires in the vegetation cover relying on recent multitemporal data (2017–2019) of Landsat-8 and Sentinel-2 imagery using the cloud-based Google Earth Engine (GEE) platform. In order to assess the burnt areas (BA), spectral indices were employed, such as the Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Mid-Infrared Burn Index (MIRBI). All these indices were applied for BA assessment according to appropriate thresholds. Additionally, to reduce confusion between burnt areas and other land cover classes, further indices were used, like those considering the temporal differences between pre and post-fire conditions: differential Mid-Infrared Burn Index (dMIRBI), differential Normalized Burn Ratio (dNBR), differential Normalized Burn Ratio 2 (dNBR2), and differential Near-Infrared (dNIR). The calculated BA by Sentinel-2 was larger during the three-year investigation span (16.55, 78.50, and 67.19 km2) and of greater detail (detected small areas) than the BA extracted by Landsat-8 (16.39, 6.24, and 32.93 km2). The routine for monitoring wildfires presented in this work is based on a sequence of decision rules. This enables the detection and monitoring of burnt vegetation cover and has been originally applied to an experiment in the northeastern Peruvian Amazon. The results obtained by the two satellites imagery are compared in terms of accuracy metrics and level of detail (size of BA patches). The accuracy for Landsat-8 and Sentinel-2 in 2017, 2018, and 2019 varied from 82.7–91.4% to 94.5–98.5%, respectively.

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

2021 ◽  
Vol 13 (12) ◽  
pp. 2311
Author(s):  
Clement J. F. Delcourt ◽  
Alisha Combee ◽  
Brian Izbicki ◽  
Michelle C. Mack ◽  
Trofim Maximov ◽  
...  
Keyword(s):  
Boreal Forests ◽  
Fire Severity ◽  
Siberian Larch ◽  
Fire Scars ◽  
Differenced Normalized Burn Ratio ◽  
Larch Forests ◽  
Normalized Burn Ratio ◽  
Burn Depth ◽  
Field Plots ◽  
Sentinel 2

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

2021 ◽  
Author(s):  
Alisha Combee ◽  
Clément J.F Delcourt ◽  
Brian Izbicki ◽  
Michelle C. Mack ◽  
Trofim C. Maximov ◽  
...  
Keyword(s):  
North America ◽  
Fire Severity ◽  
Forest Types ◽  
Fire Scars ◽  
Differenced Normalized Burn Ratio ◽  
Larch Forests ◽  
Normalized Burn Ratio ◽  
Burn Depth ◽  
Field Plots ◽  
Sentinel 2

&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;

Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data

2005 ◽  
Vol 14 (2) ◽  
pp. 189 ◽  
Author(s):  
Allison E. Cocke ◽  
Peter Z. Fulé ◽  
Joseph E. Crouse
Keyword(s):  
Pinus Ponderosa ◽  
Forest Structure ◽  
Tree Mortality ◽  
Fire Severity ◽  
Basal Area ◽  
Burn Severity ◽  
Burned Areas ◽  
Differenced Normalized Burn Ratio ◽  
Severity Levels ◽  
Normalized Burn Ratio

Burn severity can be mapped using satellite data to detect changes in forest structure and moisture content caused by fires. The 2001 Leroux fire on the Coconino National Forest, Arizona, burned over 18 pre-existing permanent 0.1 ha plots. Plots were re-measured following the fire. Landsat 7 ETM+ imagery and the Differenced Normalized Burn Ratio (ΔNBR) were used to map the fire into four severity levels immediately following the fire (July 2001) and 1 year after the fire (June 2002). Ninety-two Composite Burn Index (CBI) plots were compared to the fire severity maps. Pre- and post-fire plot measurements were also analysed according to their imagery classification. Ground measurements demonstrated differences in forest structure. Areas that were classified as severely burned on the imagery were predominantly Pinus ponderosa stands. Tree density and basal area, snag density and fine fuel accumulation were associated with severity levels. Tree mortality was not greatest in severely burned areas, indicating that the ΔNBR is comprehensive in rating burn severity by incorporating multiple forest strata. While the ΔNBR was less accurate at mapping perimeters, the method was reliable for mapping severely burned areas that may need immediate or long-term post-fire recovery.

scholarly journals Evaluating the Performance of Sentinel-2, Landsat 8 and Pléiades-1 in Mapping Mangrove Extent and Species

2018 ◽  
Vol 10 (9) ◽  
pp. 1468 ◽  
Author(s):  
Dezhi Wang ◽  
Bo Wan ◽  
Penghua Qiu ◽  
Yanjun Su ◽  
Qinghua Guo ◽  
...  
Keyword(s):  
Near Infrared ◽  
Environmental Changes ◽  
Feature Space ◽  
Mangrove Ecosystem ◽  
Recursive Feature Elimination ◽  
Landsat 8 ◽  
Mangrove Species ◽  
Goods And Services ◽  
Shortwave Infrared ◽  
Sentinel 2

Mapping mangrove extent and species is important for understanding their response to environmental changes and for observing their integrity for providing goods and services. However, accurately mapping mangrove extent and species are ongoing challenges in remote sensing. The newly-launched and freely-available Sentinel-2 (S2) sensor offers a new opportunity for these challenges. This study presents the first study dedicated to the examination of the potential of original bands, spectral indices, and texture information of S2 in mapping mangrove extent and species in the first National Nature Reserve for mangroves in Dongzhaigang, China. To map mangrove extent and species, a three-level hierarchical structure based on the spatial structure of a mangrove ecosystem and geographic object-based image analysis is utilized and modified. During the experiments, to conquer the challenge of optimizing high-dimension and correlated feature space, the recursive feature elimination (RFE) algorithm is introduced. Finally, the selected features from RFE are employed in mangrove species discriminations, based on a random forest algorithm. The results are compared with those of Landsat 8 (L8) and Pléiades-1 (P1) data and show that S2 and L8 could accurately extract mangrove extent, but P1 obviously overestimated it. Regarding mangrove species community levels, the overall classification accuracy of S2 is 70.95%, which is lower than P1 imagery (78.57%) and slightly higher than L8 data (68.57%). Meanwhile, the former difference is statistically significant, and the latter is not. The dominant species is extracted basically in S2 and P1 imagery, but for the occasionally distributed K. candel and the pioneer and fringe mangrove A. marina, S2 performs poorly. Concerning L8, S2, and P1, there are eight (8/126), nine (9/218), and eight (8/73) features, respectively, that are the most important for mangrove species discriminations. The most important feature overall is the red-edge bands, followed by shortwave infrared, near infrared, blue, and other visible bands in turn. This study demonstrates that the S2 sensor can accurately map mangrove extent and basically discriminate mangrove species communities, but for the latter, one should be cautious due to the complexity of mangrove species.

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