The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
How Robust Are Burn Severity Indices When Applied in a New Region? Evaluation of Alternate Field-Based and Remote-Sensing Methods
Open accessAbstract
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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“Together, these datasets have revealed much greater heterogeneity in fire effects than previously characterized (Cansler & McKenzie, 2012;Collins et al., 2017).”
From Fire as a fundamental ecological process: Research advances and frontiers (2020) · cites this paper as Cansler & McKenzie, 2012
Citations
Cites
Fire intensity, fire severity and burn severity: a brief review and suggested usage
Keeley, J. E. (2009) · International Journal of Wildland Fire · doi:10.1071/wf07049 · Cited by 2,387
Miller, J. D., & Thode, A. E. (2007) · Remote Sensing of Environment · doi:10.1016/j.rse.2006.12.006 · Cited by 1,444
A Project for Monitoring Trends in Burn Severity
Eidenshink, J., Schwind, B., Brewer, K., Zhu, Z.-L., Quayle, B., & Howard, S. (2007) · Fire Ecology · doi:10.4996/fireecology.0301003 · Cited by 1,423
Remote sensing techniques to assess active fire characteristics and post-fire effects
Lentile, L. B., Holden, Z. A., Smith, A. M. S., Falkowski, M. J., Hudak, A. T., Morgan, P., Lewis, S. A., Gessler, P. E., & Benson, N. (2006) · International Journal of Wildland Fire · doi:10.1071/wf05097 · Cited by 1,002
FIREMON: Fire effects monitoring and inventory system
Lutes, D. C., Keane, R. E., Caratti, J. F., Key, C. H., Benson, N. C., Sutherland, S., & Gangi, L. J. (2006) · doi:10.2737/rmrs-gtr-164 · Cited by 854
Mapping burns and natural reforestation using thematic Mapper data
García, M. J. L., & Caselles, V. (1991) · Geocarto International · doi:10.1080/10106049109354290 · Cited by 743
Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data
Cocke, A. E., Fulé, P. Z., & Crouse, J. E. (2005) · International Journal of Wildland Fire · doi:10.1071/wf04010 · Cited by 498
Miller, J. D., Knapp, E. E., Key, C. H., Skinner, C. N., Isbell, C., Creasy, R. M., & Sherlock, J. W. (2009) · Remote Sensing of Environment · doi:10.1016/j.rse.2008.11.009 · Cited by 488
Comparison of AVIRIS and Landsat ETM+ detection capabilities for burn severity
van Wagtendonk, J. W., Root, R. R., & Key, C. H. (2004) · Remote Sensing of Environment · doi:10.1016/j.rse.2003.12.015 · Cited by 467
Remote Sensing of Fire Severity: Assessing the Performance of the Normalized Burn Ratio
Roy, D. P., Boschetti, L., & Trigg, S. N. (2006) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2005.858485 · Cited by 425
Research works printed but not linked: 4
- Cansler, C.A. (2011). M.S. Thesis, School of Forest Resources, University of Washington, Seattle, WA, USA.
- Franklin, J.F., and Dyrness, C.T. (1988). Natural Vegetation of Oregon and Washington, Oregon State University Press. [2nd ed.].
- Lyons-Tinsley, C., and Peterson, D.L. (2012). Forest Ecol. Manage, in press.
- Lillesand, T.M., Kiefer, R.W., and Chipman, J.W. (2007). Remote Sensing and Image Interpretation, John Wiley & Sons. [6th ed.]. Chapter 7.
Cited by
Sorted by citations
Fire as a fundamental ecological process: Research advances and frontiers
McLauchlan, K. K., Higuera, P. E., Miesel, J., Rogers, B. M., Schweitzer, J., Shuman, J. K., Tepley, A. J., Varner, J. M., Veblen, T. T., Adalsteinsson, S. A., et al. (2020) · Journal of Ecology · doi:10.1111/1365-2745.13403 · Cited by 729
“Together, these data-sets have revealed much greater heterogeneity in fire effects than previously characterized (Cansler & McKenzie, 2012; Collins et al., 2017).”
A New Metric for Quantifying Burn Severity: The Relativized Burn Ratio
Parks, S. A., Dillon, G. K., & Miller, C. (2014) · Remote Sensing · doi:10.3390/rs6031827 · Cited by 452
Challenges of assessing fire and burn severity using field measures, remote sensing and modelling
Morgan, P., Keane, R. E., Dillon, G. K., Jain, T. B., Hudak, A. T., Karau, E. C., Sikkink, P. G., Holden, Z. A., & Strand, E. K. (2014) · International Journal of Wildland Fire · doi:10.1071/wf13058 · Cited by 258
Chu, T., & Guo, X. (2013) · Remote Sensing · doi:10.3390/rs6010470 · Cited by 242
Cansler, C. A., & McKenzie, D. (2013) · Ecological Applications · doi:10.1890/13-1077.1 · Cited by 232
Self-citation
Metadata
- Authors
- Cansler, C. A., & McKenzie, D.
- Affiliations
- University of Washington (US)
- US Forest Service (US)
- Corresponding author
- Cansler, C. Alina
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2012-02-09
- Volume
- 4
- Issue
- 2
- Pages
- 456-483
- ISSN
- 2072-4292 (ISSN-L)
- 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
- 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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