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How Robust Are Burn Severity Indices When Applied in a New Region? Evaluation of Alternate Field-Based and Remote-Sensing Methods

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Cansler, C. Alina, McKenzie, Donald·2012·Remote Sensing

DOI 10.3390/rs4020456

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Abstract

Remotely sensed indices of burn severity are now commonly used by researchers and land managers to assess fire effects, but their relationship to field-based assessments of burn severity has been evaluated only in a few ecosystems. This analysis illustrates two cases in which methodological refinements to field-based and remotely sensed indices of burn severity developed in one location did not show the same improvement when used in a new location. We evaluated three methods of assessing burn severity in the field: the Composite Burn Index (CBI)—a standardized method of assessing burn severity that combines ecologically significant variables related to burn severity into one numeric site index—and two modifications of the CBI that weight the plot CBI score by the percentage cover of each stratum. Unexpectedly, models using the CBI had higher R2 and better classification accuracy than models using the weighted versions of the CBI. We suggest that the weighted versions of the CBI have lower accuracies because weighting by percentage cover decreases the influence of the dominant tree stratum, which should have the strongest relationship to optically sensed reflectance, and increases the influence of the substrates strata, which should have the weakest relationship with optically sensed reflectance in forested ecosystems. Using a large data set of CBI plots (n = 251) from four fires and CBI scores derived from additional field-based assessments of burn severity (n = 388), we predicted two metrics of image-based burn severity, the Relative differenced Normalized Burn Ratio (RdNBR) and the differenced Normalized Burn Ratio (dNBR). Predictive models for RdNBR showed slightly better classification accuracy than for dNBR (overall accuracy = 62%, Kappa = 0.40, and overall accuracy = 59%, Kappa= 0.36, respectively), whereas dNBR had slightly better explanatory power, but strong differences were not apparent. RdNBR may provide little or no improvement over dNBR in systems where pre-fire reflectance is not highly variable, but may be more appropriate for comparing burn severity among regions.

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Authors: Cansler, C. A., & McKenzie, D.
Affiliations: University of Washington (US)
US Forest Service (US)
Corresponding author: Cansler, C. Alina
ORCID: Cansler, C. Alina
McKenzie, Donald
Venue: Remote Sensing
Publisher: MDPI AG
Published: 2012-02-09
Volume: 4
Issue: 2
Pages: 456-483
ISSN: 2072-4292 (ISSN-L)
DOI: 10.3390/rs4020456
OpenAlex: W1967049547
Cited by: 165
References: 39
Citations by year: 2026: 14 · 2025: 8 · 2024: 14 · 2023: 15 · 2022: 17 · 2021: 13 · 2020: 13 · 2019: 14 · 2018: 12 · 2017: 12 · 2016: 12 · 2015: 10 · 2014: 5 · 2013: 4 · 2012: 2
FWCI: 5.58
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/3.0/ (vor)
Locations: Remote Sensing (publishedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics: Physical Sciences › Environmental Science › Global and Planetary Change › Fire effects on ecosystems
Physical Sciences › Environmental Science › Ecology › Rangeland and Wildlife Management
Health Sciences › Medicine › Surgery › Burn Injury Management and Outcomes
Keywords: burn severity, Composite Burn Index, dNBR, fire effects

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How Robust Are Burn Severity Indices When Applied in a New Region? Evaluation of Alternate Field-Based and Remote-Sensing Methods

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