Estimating burn severity from Landsat dNBR and RdNBR indices across western Canada

• Published in: Remote sensing of environment Vol. 114; no. 9; pp. 1896 - 1909
• Main Authors: Soverel, Nicholas O., Perrakis, Daniel D.B., Coops, Nicholas C.
• Format: Journal Article
• Language: English
• Published: New York, NY Elsevier Inc 15.09.2010; Elsevier
• Subjects: Accuracy; Algorithms; Animal, plant and microbial ecology; Applied geophysics; Biological and medical sciences; Burn severity; Burns; Canada; CBI; dNBR; Earth sciences; Earth, ocean, space; Estimates; Estimating; Exact sciences and technology; Fire; Fire ecology; Fires; Forest; Fundamental and applied biological sciences. Psychology; General aspects. Techniques; Internal geophysics; Landsat; Landscapes; National parks; Parks Canada; RdNBR; Regional; Remote sensing; Teledetection and vegetation maps; Canada; Western Canada; Canada; Parks Canada; Fire ecology; dNBR; Fire; Burn severity; Landsat; Forest; CBI; RdNBR; Remote sensing; experimental studies; algorithms; Environmental factor; accuracy; Research; North America; Abiotic factor; frequency; Result; evaluation; 2007; Capacity; imagery; Ratio; Biotic factor; processes; Space remote sensing; Prediction; utilization; Perturbation; Landsat satellite; Geographical division; fires; Vegetation fire; Community; national parks
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2010.03.013

National parks in western Canada experience wildland fire events at differing frequencies, intensities, and burn severities. These episodic disturbances have varying implications for various biotic and abiotic processes and patterns. To predict burn severity, the differenced Normalized Burn Ratio (dNBR) algorithm, derived from Landsat imagery, has been used extensively throughout the wildland fire community. In Canada, few accuracy assessments have been undertaken to compare the accuracy of the dNBR algorithm to its relative form (RdNBR). To investigate the accuracies of these two algorithms in Canada's National Parks, we hypothesized that RdNBR would outperform dNBR in two specific applications based on former research by Miller and Thode (2007). The first was the capacity of the RdNBR to produce more accurate results than dNBR over a wide range of fires and secondly in pre-fire landscapes with low canopy closure and high heterogeneity. To investigate these questions, dNBR and RdNBR indices were extracted from Landsat imagery and compared to the measurements of the Composite Burn Index (Key & Benson, 2006). Following this, best fit models were developed and statistically tested at the individual, regional, overall, and vegetative levels. We then developed confusion matrices to assess the relative strength and weakness of each model. As an additional means of comparing model accuracy, we tested Hall et al.'s (2008) non-linear model in estimating burn severity for the study's western boreal region and individual fires. The results indicate that across all fires, the RdNBR-derived model did not estimate burn severity more accurately than dNBR (65.2% versus 70.2% classification accuracy, respectively) nor in the heterogeneous and low canopy cover landscapes. In addition, we conclude that RdNBR is no more effective than dNBR at the regional, individual, and fine-scale vegetation levels. The Hall et al. (2008) model was found to estimate burn severity in the western boreal region with a higher overall kappa than both the dNBR and RdNBR study models. The results herein support the continued research and pursuit of developing regional remote sensing derived models in western Canada.