Comparison of burn severity assessments using Differenced Normalized Burn Ratio and ground data

Abstract

Burn severity can be mapped using satellite data to detect changes in forest structure and moisture content caused by fires. The 2001 Leroux fire on the Coconino National Forest, Arizona, burned over 18 pre-existing permanent 0.1 ha plots. Plots were re-measured following the fire. Landsat 7 ETM+ imagery and the Differenced Normalized Burn Ratio (ΔNBR) were used to map the fire into four severity levels immediately following the fire (July 2001) and 1 year after the fire (June 2002). Ninety-two Composite Burn Index (CBI) plots were compared to the fire severity maps. Pre- and post-fire plot measurements were also analysed according to their imagery classification. Ground measurements demonstrated differences in forest structure. Areas that were classified as severely burned on the imagery were predominantly Pinus ponderosa stands. Tree density and basal area, snag density and fine fuel accumulation were associated with severity levels. Tree mortality was not greatest in severely burned areas, indicating that the ΔNBR is comprehensive in rating burn severity by incorporating multiple forest strata. While the ΔNBR was less accurate at mapping perimeters, the method was reliable for mapping severely burned areas that may need immediate or long-term post-fire recovery.

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

“…We found that the high-and moderate-severity classes had the highest classification accuracies, whereas the classification accuracy for the low-severity class was poor. Other studies have also found that the high-severity class can be classified with the greatest accuracy [1,45]. Also, RdNBR class thresholds for the high-severity class are more consistent across studies in different regions than dNBR class thresholds (Table 13).…”

“…Previous studies have used CBI as a response variable [12,27,45] and as a predictor variable [1,2,46], but because regression analysis typically implies a causal relationship between one or more predictors and a response, we use CBI as a predictor because burn severity causes changes in reflectance, not the other way around. This allows us to use the variable that has the greatest certainty associated with its meaning (CBI) to predict the variable that has no inherent ecological meaning (dNBR or RdNBR).…”

Evaluating Spectral Indices for Assessing Fire Severity in Chaparral Ecosystems (Southern California) Using MODIS/ASTER (MASTER) Airborne Simulator Data

“…Consequently, there is need to independently validate the approach for specific regions and vegetation types [13,26,34,35] to determine if the technique is capable of inferring fire/burn severity from satellite imagery [31]. The Landsat NBR is used as a post-fire management tool in the USA and Canada, e.g., as operationally used by the Burned Area Emergency Rehabilitation (BAER) teams in the conterminous USA [12].…”

“…Severity, in contrast, is more general in gauging the fire impact. This impact can be described as: (i) the amount of damage [3][4][5]; (ii) the physical, chemical and biological changes [6][7][8][9][10]; or (iii) the degree of alteration [11,12] that fire causes to an ecosystem. In this context, the terms fire severity and burn severity are often used interchangeably [2], however, Lentile et al [13] and Veraverbeke et al [14], suggest a clear distinction between both terms by considering the fire disturbance continuum [15], which addresses three different temporal fire effects phases: before, during and after the fire.…”

Caracterización de la situación posincendio en el área afectada por el incendio de 2005 en el Parque Nacional de Torres del Paine (Chile) a partir de imágenes multiespectrales

“…De la diferencia entre el NBR anterior y posterior al incendio se obtuvo una imagen donde se destaca el área recorrida por el fuego respecto a la zona no afectada, que se digitalizó directamente sobre la imagen del índice delta NBR (Cocke et al 2005), mediante un realce de color. Los valores obtenidos en esta imagen se consideraron como la medida de perímetro más fiable de acuerdo a los resultados de Key & Benson (2001.…”