Evaluating the potential of LiDAR data for fire damage assessment: A radiative transfer model approach

Garcı́a, Mariano; North, Peter; Viana-Soto, Alba; Stavros, Natasha; Rosette, J.; Martín, M. Pilar; Franquesa, Magí; González-Cascón, Rosario; Riaño, David; Becerra, Javier; Zhao, Kunmin

doi:10.1016/j.rse.2020.111893

Cited by 22 publications

(14 citation statements)

References 66 publications

(30 reference statements)

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“…Noise within remote sensing analysis can result from imperfections in image pre-processing and slight deviations in phenology between near-anniversary date image acquisitions. Garcia et al [44] recently developed a new metric, the waveform area relative change (WARC), from light detection and ranging (LiDAR) imagery. The WARC proxy captures changes in vegetation structure as well as changes in soil and leaf colour and was successfully validated over the King fire.…”

Section: Discussionmentioning

confidence: 99%

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

Gerrevink

Veraverbeke

2021

Remote Sensing

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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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Section: Discussionmentioning

confidence: 99%

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

Gerrevink

Veraverbeke

2021

Remote Sensing

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“…This performance is similar to our relationship between the hyperspectral dNBR and the GeoCBI. Furthermore, light detection and ranging (LiDAR) imagery can be used to derive the waveform area relative change (WARC) metric [61]. This newly developed fire severity assessment technique capitalizes upon changes in vegetation structure among changes in leaf and soil colors.…”

Section: Discussionmentioning

confidence: 99%

“…The WARC metric has successfully been validated over the King fire. Synergistic use of spectral indices and LiDAR could improve the assessment of post-fire damages and contribute to a more detailed long-term fire severity evaluation [61].…”

Section: Discussionmentioning

confidence: 99%

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

Gerrevink

Veraverbeke

2021

Remote Sensing

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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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“…These simulations can provide a cost-effective alternative to field surveys while improving the control in the experiments [25]. Radiative Transfer Models (RTM) approaches are subject to an appropriate model parameterization that also requires in situ data, however they offer a better universality than empirical approaches [26].…”

Section: Introductionmentioning

confidence: 99%

“…Previous forest parameter simulations conducted with 3D RTM capable of simulating the LiDAR response, such as FLIGHT [27] and Discrete Anisotropic Radiative Transfer (DART) [28] models, have mainly focused on large-footprint waveform LiDAR acquisitions from satellite [29,30] or onboard aircraft [24,26,31]. However, few investigations have examined small-footprint discrete-return measurements due to the computational demands of simulating multi-pulse ALS acquisitions over complex forested landscapes.…”

Section: Introductionmentioning

confidence: 99%

Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping

et al. 2021

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Fuel type is one of the key factors for analyzing the potential of fire ignition and propagation in agricultural and forest environments. The increase of three-dimensional datasets provided by active sensors, such as LiDAR (Light Detection and Ranging), has improved the classification of fuel types through empirical modelling. Empirical methods are site and sensor specific while Radiative Transfer Models (RTM) approaches provide broader universality. The aim of this work is to analyze the suitability of Discrete Anisotropic Radiative Transfer (DART) model to replicate low density small-footprint Airborne Laser Scanning (ALS) measurements and subsequent fuel type classification. Field data measured in 104 plots are used as ground truth to simulate LiDAR response based on the sensor and flight characteristics of low-density ALS data captured by the Spanish National Plan for Aerial Orthophotography (PNOA) in two different dates (2011 and 2016). The accuracy assessment of the DART simulations is performed using Spearman rank correlation coefficients between the simulated metrics and the ALS-PNOA ones. The results show that 32% of the computed metrics overpassed a correlation value of 0.80 between simulated and ALS-PNOA metrics in 2011 and 28% in 2016. The highest correlations were related to high height percentiles, canopy variability metrics as for example standard deviation and Rumple diversity index, reaching correlation values over 0.94. Two metric selection approaches and Support Vector Machine classification method with variants were compared to classify fuel types. The best-fitted classification model, trained with the DART simulated sample and validated with ALS-PNOA data, was obtained using Support Vector Machine method with radial kernel. The overall accuracy of the classification after validation was 88% and 91% for the 2011 and 2016 years, respectively. The use of DART demonstrates its value for simulating generalizable 3D data for fuel type classification providing relevant information for forest managers in fire prevention and extinction.

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Evaluating the potential of LiDAR data for fire damage assessment: A radiative transfer model approach

Cited by 22 publications

References 66 publications

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

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

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

Assessing the Potential of the DART Model to Discrete Return LiDAR Simulation—Application to Fuel Type Mapping

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