Performance of Burn-Severity Metrics and Classification in Oak Woodlands and Grasslands

Michael Stambaugh,Lyndia Hammer,Ralph Godfrey

doi:10.3390/rs70810501

Michael Stambaugh, Lyndia Hammer + Show 1 more

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https://doi.org/10.3390/rs70810501

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Abstract

Burn severity metrics and classification have yet to be tested for many eastern U.S. deciduous vegetation types, but, if suitable, would be valuable for documenting and monitoring landscape-scale restoration projects that employ prescribed fire treatments. Here we present a performance analysis of the Composite Burn Index (CBI) and its relationship to spectral data (differenced Normalized Burn Ratio (dNBR) and its relative form (RdNBR)) across an oak woodland - grassland landscape in southwestern Oklahoma, USA. Correlation and regression analyses were used to compare CBI strata, assess models describing burn severity, and determine thresholds for burn severity classes. Confusion matrices were used to assess burn severity classification accuracy. Our findings suggest that dNBR and RdNBR, thresholded using total CBI, can produce an accurate burn severity map in oak woodlands, particularly from an initial assessment period. Lower accuracies occurred for burn severity classifications of grasslands and raises questions related to definitions and detection of burn severity for grasslands, particularly in transition to more densely treed structures such as savannas and woodlands.

Highlights

Despite burn severity metrics and classification through remote sensing being used throughout the world [1,2,3,4], they have yet to be described for many eastern U.S vegetation types, such as deciduous forests
Plots were stratified by severity classes prior to sampling (i.e., 30 each of unburned, low, moderate, and high severity as classified in the differenced NBR (dNBR) extended assessment (EA) (MTBS)), based on plot Composite Burn Index (CBI) scores of burned plots, we sampled 12 low severity plots (CBI ≤ 1.25), 52 moderate severity plots (CBI = 1.26 to 2.25), and 27 high severity plots (CBI > 2.26)
In a longleaf pine (Pinus palustris) ecosystem, Picotte and Robertson [31] note that some burn severity effects evident on the ground at the time of CBI sampling one year post fire were no longer detected by the EA imagery and as a result compared the Initial Assessment (IA) and the EA with imagery taken at an intermediate time frame of three months

Summary

Introduction

Despite burn severity metrics and classification through remote sensing being used throughout the world [1,2,3,4], they have yet to be described for many eastern U.S vegetation types, such as deciduous forests. The ability to describe, quantify, and remotely sense burn severity in this region would be useful for documentation, monitoring, and prioritization of landscape-scale restoration projects that include prescribed burning [5]. Research on fire regime characteristics and post-fire effects has included increased emphasis on understanding the importance of the full range of burn severities in forest ecosystems [6,7,8]. Burn severity describes fire effects on above-ground vegetation and soil organic matter [12,13]. From field measures to remote sensing and modeling, assessing burn severity presents diverse challenges [14]

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