Postfire soil burn severity mapping with hyperspectral image unmixing

Robichaud, Peter R.; Lewis, Sarah A.; Laes, Denise; Hudak, Andrew T.; Kokaly, Raymond F.; Zamudio, J.A.

doi:10.1016/j.rse.2006.11.027

Cited by 143 publications

(89 citation statements)

References 33 publications

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“…Greater uncertainty in the low-and moderate-severity classes (relative to high-severity) results from fire effects on surface vegetation and substrate being obscured by live canopy cover (Miller and Thode, 2007;Meigs et al, 2009Meigs et al, , 2011Miller et al, 2009a), and this uncertainty may contribute to difficulty resolving differences in C stocks and losses among severity classes. Landsat-based indices of fire effects correlate well with forest overstory characteristics, and can show relatively good relationships with understory vegetation and soil severity when canopy severity is also high; however, the correlations with surface severity weaken when there is high post-fire live vegetation cover Robichaud et al, 2007).…”

Section: Evaluation Of Contrasting Fire Severity Metricsmentioning

confidence: 98%

“…Efforts to develop and apply standard indices for characterizing fire severity are relatively recent and include remote sensing approaches Miller and Thode, 2007;Robichaud et al, 2007) as well as field classification of composite [e.g., Composite Burn Index (CBI), ] and stratrum-specific impacts (NPS, 2003;Keeley, 2009;Jain et al, 2012). Field indices classify fire severity based on the extent of organic matter loss or decomposition (i.e., using metrics such as tree crown scorch, tree mortality, woody fuel consumption, loss of soil organic horizons, etc.…”

Section: Introductionmentioning

confidence: 99%

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Quantifying Changes in Total and Pyrogenic Carbon Stocks Across Fire Severity Gradients Using Active Wildfire Incidents

et al. 2018

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Positive feedbacks between wildfire emissions and climate are expected to increase in strength in the future; however, fires not only release carbon (C) from terrestrial to atmospheric pools, they also produce pyrogenic C (PyC) which contributes to longer-term C stability. Our objective was to quantify wildfire impacts on total C and PyC stocks in California mixed-conifer forest, and to investigate patterns in C and PyC stocks and changes across gradients of fire severity, using metrics derived from remote sensing and field observations. Our unique study accessed active wildfires to establish and measure plots within days before and after fire, prior to substantial erosion. We measured pre-and post-fire aboveground forest structure and woody fuels to calculate aboveground biomass, C and PyC, and collected forest floor and 0-5 cm mineral soil samples. Immediate tree mortality increased with severity, but overstory C loss was minimal and limited primarily to foliage. Fire released 85% of understory and herbaceous C (comprising <1.0% of total ecosystem C). The greatest C losses occurred from downed wood and forest floor pools (19.3 ± 5.1 Mg ha −1 and 25.9 ± 3.2 Mg ha −1 , respectively). Tree bark and downed wood contributed the greatest PyC gains (1.5 ± 0.3 Mg ha −1 and 1.9 ± 0.8 Mg ha −1 , respectively), and PyC in tree bark showed non-significant positive trends with increasing severity. Overall PyC losses of 1.9 ± 0.3 Mg ha −1 and 0.5 ± 0

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Section: Evaluation Of Contrasting Fire Severity Metricsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Quantifying Changes in Total and Pyrogenic Carbon Stocks Across Fire Severity Gradients Using Active Wildfire Incidents

et al. 2018

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“…dNBR can be considered a consistent method for burn severity assessment, due to its proven relationship with field severity metrics. Empirical models have shown strong relationships (r 2 > 0.6-0.7) between dNBR and specific parameters of burn severity, such as ash cover percentage, tree mortality, or twig diameter [30,[34][35][36][37], or field indices, such as the Composite Burn Index (CBI) [31,[38][39][40][41][42]. Moreover, the bi-temporal approach, where values of the post-fire image are subtracted from values of the pre-fire image, is considered the best approach to detect change caused by fire.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Relationship between Land Surface Temperature and Wildfire Severity in a Series of Landsat Images

et al. 2014

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Abstract:The paper assesses spatio-temporal patterns of land surface temperature (LST) and fire severity in the Las Hurdes wildfire of Pinus pinaster forest, which occurred in July 2009, in Extremadura (Spain), from a time series of fifteen Landsat 5 TM images corresponding to 27 post-fire months. The differenced Normalized Burn Ratio (dNBR) was used to evaluate burn severity. The mono-window algorithm was applied to estimate LST from the Landsat thermal band. The burned zones underwent a significant increase in LST after fire. Statistically significant differences have been detected between the LST within regions of burn severity categories. More substantial changes in LST are observed in zones of greater fire severity, which can be explained by the lower emissivity of combustion products found in the burned area and changes in the energy balance related to vegetation removal. As time progresses over the 27 months after fire, LST differences decrease due to vegetation regeneration. The differences in LST and Normalized Difference Vegetation Index (NDVI) values between burn severity categories in each image are highly correlated (r = 0.84). Spatial patterns of severity and post-fire LST obtained from Landsat time series enable an evaluation of the relationship between these variables to predict the natural dynamics of burned areas.

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“…The main advantage of these simulation models is that their performance is site-independent which greatly enhances their applicability and inter-comparability over a wide range of ecosystems [18,20]. Spectral mixture analysis (SMA) applied to post-fire images have resulted in fractional ground cover measures closely related to burning efficiency, usually implementing at least the green vegetation and charred soil endmembers [21][22][23]. SMA proved to be efficient in detecting the charcoal signal even in lightly burned areas that kept a strong vegetation signal.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Spectral Indices for Assessing Fire Severity in Chaparral Ecosystems (Southern California) Using MODIS/ASTER (MASTER) Airborne Simulator Data

2011

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Abstract:Wildland fires are a yearly recurring phenomenon in many terrestrial ecosystems. Accurate fire severity estimates are of paramount importance for modeling fire-induced trace gas emissions and rehabilitating post-fire landscapes. We used high spatial and high spectral resolution MODIS/ASTER (MASTER) airborne simulator data acquired over four 2007 southern California burns to evaluate the effectiveness of 19 different spectral indices, including the widely used Normalized Burn Ratio (NBR), for assessing fire severity in southern California chaparral. Ordinal logistic regression was used to assess the goodness-of-fit between the spectral index values and ordinal field data of severity. The NBR and three indices in which the NBR is enhanced with surface temperature or emissivity data revealed the best performance. Our findings support the operational use of the NBR in chaparral ecosystems by Burned Area Emergency Rehabilitation (BAER) projects, and demonstrate the potential of combining optical and thermal data for assessing fire severity. Additional testing in more burns, other ecoregions and different vegetation types is required to fully understand how (thermally enhanced) spectral indices relate to fire severity.

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Postfire soil burn severity mapping with hyperspectral image unmixing

Cited by 143 publications

References 33 publications

Quantifying Changes in Total and Pyrogenic Carbon Stocks Across Fire Severity Gradients Using Active Wildfire Incidents

Quantifying Changes in Total and Pyrogenic Carbon Stocks Across Fire Severity Gradients Using Active Wildfire Incidents

Analysis of the Relationship between Land Surface Temperature and Wildfire Severity in a Series of Landsat Images

Evaluating Spectral Indices for Assessing Fire Severity in Chaparral Ecosystems (Southern California) Using MODIS/ASTER (MASTER) Airborne Simulator Data

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