Remote Sensing of Fire Severity: Assessing the Performance of the Normalized Burn Ratio

Abstract

Several studies have used satellite data to map different levels of fire severity present within burned areas. Increasingly, fire severity has been estimated using a spectral index called the normalized burn ratio (NBR). This letter assesses the performance of the NBR against ideal requirements of a spectral index designed to measure fire severity. According to index theory, the NBR would be optimal for quantifying fire severity if the trajectory in spectral feature space caused by different levels of severity occurred perpendicular to the NBR isolines. We assess how well NBR meets this condition using reflectance data sensed before and shortly after fires in the South African savanna, Australian savanna, Russian Federation boreal forest, and South American tropical forest. Although previous studies report high correlation between fire severity measured in the fieldand satellite-derived NBR, our results do not provide evidence that the performance of the NBR is optimal in describing fire severity shortly after fire occurrence. Spectral displacements due to burning occur in numerous directions relative to the NBR index isolines, suggesting that the NBR may not be primarily and consistently sensitive to fire severity. Findings suggest that the development of the next generation of methods to estimate fire severity remotely should incorporate knowledge of how fires of different severity displace the position of prefire vegetation in multispectral space.

Evaluating the Best Spectral Indices for the Detection of Burn Scars at Several Post-Fire Dates in a Mountainous Region of Northwest Yunnan, China

“…Although NBR is often considered the best SI for burned area mapping short time after fire and therefore widely used for burn severity assessments [7,19,21,23,[27][28][29], our results demonstrated that in certain vegetation types other SI would offer a better option. The limitations of NBR for immediate post-fire assessment were already pointed out by Roy et al [76].…”

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

“…Band 7 is sensitive to the cellulose content and water content of the plants, and increases with greater cover of soil, ash, or carbon [7]. In forested systems Band 7 increases with higher burn severity [7] but may decrease with burn severity in grass dominated systems [22]. In forested systems recent burns have negative NBR values (R4 < R7) whereas unburned vegetated has strongly positive NBR values (R4 > R7).…”

“…Overall, correlations with field-based data and classification accuracies of the indices are good, but do seem to vary among regions; of the 26 studies using dNBR reviewed by French et al [9] the average classification accuracy was 73% but accuracies varied from 50 to 95%. Also, because the two spectral bands do not change at the same rate with increased burn severity, NBR, by itself, does not meet the criteria for being an "optimal index" for assessing burn severity [22]. Other remote-sensing methods, such as spectral-mixture analysis, have also shown promise for detecting burn severity and post-fire effects [26], but field evaluation of these methods have been limited in geographic scope compared to dNBR and RdNBR.…”

Analysis and Assessment of the Spatial and Temporal Distribution of Burned Areas in the Amazon Forest

“…However, remote sensing methods have limitations that can cause errors in final burned area products [19]. Major factors include the presence of clouds, which significantly reduces the ability to detect a fire hotspot due to the attenuation of the spectral radiance emitted by the flaming and smoldering phases in the biomass burning process [20], the lack of data on the moment of fire occurrence and, especially, the incompatibility of the spatial resolutions of some sensors, making these sensors unsuitable for the identification of fires [21][22][23].…”