Fire-severity classification across temperate Australian forests: random forests versus spectral index thresholding

An alternative spectral index for rapid fire severity assessments

Remote Sensing of Environment ◽

10.1016/j.rse.2012.02.025 ◽

2012 ◽

Vol 123 ◽

pp. 72-80 ◽

Cited By ~ 29

Author(s):

S. Veraverbeke ◽

S. Hook ◽

G. Hulley

Keyword(s):

Spectral Index ◽

Fire Severity

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Evaluation of Spectral Indices for Assessing Fire Severity in Australian Temperate Forests

Remote Sensing ◽

10.3390/rs10111680 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1680 ◽

Cited By ~ 18

Author(s):

Bang Tran ◽

Mihai Tanase ◽

Lauren Bennett ◽

Cristina Aponte

Keyword(s):

Forest Type ◽

Fire Severity ◽

Spectral Indices ◽

Forest Types ◽

South Eastern ◽

Severity Classification ◽

Eastern Australia ◽

Regeneration Strategies ◽

Open Forests ◽

South Eastern Australia

Spectral indices derived from optical remote sensing data have been widely used for fire-severity classification in forests from local to global scales. However, comparative analyses of multiple indices across diverse forest types are few. This represents an information gap for fire management agencies in areas like temperate south-eastern Australia, which is characterised by a diversity of natural forests that vary in structure, and in the fire-regeneration strategies of the dominant trees. We evaluate 10 spectral indices across eight areas burnt by wildfires in 1998, 2006, 2007, and 2009 in south-eastern Australia. These wildfire areas encompass 13 forest types, which represent 86% of the 7.9M ha region’s forest area. Forest types were aggregated into six forest groups based on their fire-regeneration strategies (seeders, resprouters) and structure (tree height and canopy cover). Index performance was evaluated for each forest type and forest group by examining its sensitivity to four fire-severity classes (unburnt, low, moderate, high) using three independent methods (anova, separability, and optimality). For the best-performing indices, we calculated index-specific thresholds (by forest types and groups) to separate between the four severity classes, and evaluated the accuracy of fire-severity classification on independent samples. Our results indicated that the best-performing indices of fire severity varied with forest type and group. Overall accuracy for the best-performing indices ranged from 0.50 to 0.78, and kappa values ranged from 0.33 (fair agreement) to 0.77 (substantial agreement), depending on the forest group and index. Fire severity in resprouter open forests and woodlands was most accurately mapped using the delta Normalised Burnt ratio (dNBR). In contrast, dNDVI (delta Normalised difference vegetation index) performed best for open forests with mixed fire responses (resprouters and seeders), and dNDWI (delta Normalised difference water index) was the most accurate for obligate seeder closed forests. Our analysis highlighted the low sensitivity of all indices to fire impacts in Rainforest. We conclude that the optimal spectral index for quantifying fire severity varies with forest type, but that there is scope to group forests by structure and fire-regeneration strategy to simplify fire-severity classification in heterogeneous forest landscapes.

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Tree mortality based fire severity classification for forest inventories: A Pacific Northwest national forests example

Forest Ecology and Management ◽

10.1016/j.foreco.2015.10.015 ◽

2016 ◽

Vol 359 ◽

pp. 199-209 ◽

Cited By ~ 21

Author(s):

Thomas R. Whittier ◽

Andrew N. Gray

Keyword(s):

Pacific Northwest ◽

Tree Mortality ◽

Fire Severity ◽

National Forests ◽

Forest Inventories ◽

Severity Classification

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

Remote Sensing ◽

10.3390/rs13040695 ◽

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.

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Unitemporal approach to fire severity mapping using multispectral synthetic databases and Random Forests

Remote Sensing of Environment ◽

10.1016/j.rse.2020.112025 ◽

2020 ◽

Vol 249 ◽

pp. 112025

Author(s):

Raquel Montorio ◽

Fernando Pérez-Cabello ◽

Daniel Borini Alves ◽

Alberto García-Martín

Keyword(s):

Random Forests ◽

Fire Severity

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

Remote Sensing ◽

10.3390/rs13224611 ◽

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.

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

10.5194/egusphere-egu21-2238 ◽

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

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&#160;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.&#160;

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