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

C Alina Cansler,Donald Mckenzie

doi:10.3390/rs4020456

C Alina Cansler, Donald Mckenzie

Open Access

https://doi.org/10.3390/rs4020456

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Journal: Remote sensing	Publication Date: Feb 9, 2012
Citations: 158	License type: CC BY 3.0

Affiliation: University of Washington, US Forest Service

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.

Highlights

The large size, inaccessibility, and spatial variability of wildland fires have caused multispectral satellite data to become a common tool for mapping fire locations and effects
We evaluate the applicability of methodological refinements to field-based and remotely sensed indices of burn severity developed in other locations, to a new location, the northern Cascade Range of Washington, USA
We developed empirical relationships between the two remotely sensed indices of burn severity and field data, and based on these regressions, evaluated the hypothesis that Composite Burn Index (CBI) would predict Relative differenced Normalized Burn Ratio (RdNBR) better than differenced Normalized Burn Ratio (dNBR), in terms of variance explained and classification accuracy, thereby confirming that RdNBR provides a better surrogate for the fire effects quantified by CBI

Summary

Introduction

The large size, inaccessibility, and spatial variability of wildland fires have caused multispectral satellite data to become a common tool for mapping fire locations and effects. We compare two remotely sensed indices of burn severity; the differenced Normalized Burn Ratio (dNBR) and the Relative differenced Normalized Burn Ratio (RdNBR), a refinement of the dNBR which had stronger correlations with field-based assessments of burn severity and higher classification accuracy when evaluated in other regions [1,2]. We compare three methods of measuring burn severity in the field: the Composite Burn Index,. (CBI), which was developed to validate and produce regionally-based classification schemes of remotely sensed burn severity data [3], and two variations of a methodical refinement of the CBI based on the Geometrically structure Composite Burn Index (GeoCBI), which has been used successfully in Spain and Portugal [4,5]. Some information is lost in the classification process, categorical images facilitate: (1) visual interpretation of images; (2) comparisons of multiple fires or fires from multiple regions; (3) analysis of the spatial context and spatial pattern of severity; (4) spatially explicit predictions of the impacts of the fire (i.e., soil erosion, water quality, succession, and carbon emissions); and (5) targeted management responses to those impacts

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