Classifying and Mapping Wildfire Severity

Abstract

This study evaluates six different approaches to classifying and mapping fire severity using multi-temporal Landsat Thematic Mapper data. The six approaches tested include: two based on temporal image differencing and ratioing between pre-fire and post-fire images, two based on principal component analysis of pre-and post-fire imagery, and two based on artificial neural networks, one using just postfire imagery and the other both pre-and post-fire imagery. Our results demonstrated the potential value for any of these methods to provide quantitative fire severity maps, but one of the image differencing methods (ND4/7) provided a flexible, robust, and analytically simple approach that could be applied anywhere in the Continental U.S.Based on the results of this test, the ND4/7 was implemented operationally to classify and map fire severity over

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

“…In forested systems, dNBR and RdNBR provide more accurate measurements of burn severity than many other indices that use Landsat TM or ETM+ data, such as the differenced normalized vegetation index (dNDVI) or indices based on principal component analysis or machine-learning algorithms based on the reflectance of all the Landsat bands [6,24], although single-date indices using band ratios, tassel-cap transformations, and spectral mixture analysis have also been successfully used to map burn severity [9,25,26]. When tested in mixed-conifer forests of the Sierra Nevada with the 224 spectral bands available from the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) sensor, the bands used in the dNBR calculation were among the four most sensitive bands to changes in surface spectral reflectance after fire [27].…”

“…When tested in mixed-conifer forests of the Sierra Nevada with the 224 spectral bands available from the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) sensor, the bands used in the dNBR calculation were among the four most sensitive bands to changes in surface spectral reflectance after fire [27]. Brewer et al [24] compared six approaches of classifying and mapping fire severity in the Rocky Mountains (USA) with Landsat TM data to a "control" method of photo-interpretation and field data, and found the dNBR to be the most accurate and consistent index. 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%.…”

Combination of Landsat and Sentinel-2 MSI data for initial assessing of burn severity

“…Accurate knowledge of fire-damaged areas is fundamental for fire management, planning and monitoring vegetation restoration (Brewer et al, 2005). Satellite-based data, particularly Landsat data, is becoming key information to map damaged area (both burned area and burn severity level) accurately and quasi-immediately after fire (Chen et al, 2015;Fang and Yang, 2014;Quintano et al, 2013).…”

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

“…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.…”

“…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]. Numerous studies have demonstrated the usefulness of the index in the North American boreal and temperate regions [11,31,[36][37][38], however, far fewer studies have assessed its effectiveness in California chaparral shrublands [9,20,39], an ecosystem which is highly sensitive to burning [39][40][41]. The few studies in the California chaparral shrublands demonstrated that the NBR is reasonably well related to fire severity, however, none of them conducted an inter-indices comparison.…”