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Remote Sens. 2014, 6(3), 1803-1826;

Burned Area Detection and Burn Severity Assessment of a Heathland Fire in Belgium Using Airborne Imaging Spectroscopy (APEX)

Ecosystem Management Research Group, University of Antwerp, Universiteitsplein 1, Wilrijk B-2610, Belgium
Department of Geography, UGent, Ghent University, Krijgslaan 281, S8, Gent B-9000, Belgium
Remote Sensing Department, Flemish Institute for Technological Research (VITO), Boeretang 200, Mol B-2400, Belgium
Department of Earth System Science, UCI, University of California, Irvine, 2224 Croul Hall, Irvine 92697, CA, USA
INBO, Research Institute for Nature and Forest, Kliniekstraat 25, Brussel B-1070, Belgium
Author to whom correspondence should be addressed.
Received: 29 December 2013 / Revised: 8 February 2014 / Accepted: 10 February 2014 / Published: 27 February 2014
(This article belongs to the Special Issue Quantifying the Environmental Impact of Forest Fires)
PDF [1748 KB, uploaded 19 June 2014]


Uncontrolled, large fires are a major threat to the biodiversity of protected heath landscapes. The severity of the fire is an important factor influencing vegetation recovery. We used airborne imaging spectroscopy data from the Airborne Prism Experiment (APEX) sensor to: (1) investigate which spectral regions and spectral indices perform best in discriminating burned from unburned areas; and (2) assess the burn severity of a recent fire in the Kalmthoutse Heide, a heathland area in Belgium. A separability index was used to estimate the effectiveness of individual bands and spectral indices to discriminate between burned and unburned land. For the burn severity analysis, a modified version of the Geometrically structured Composite Burn Index (GeoCBI) was developed for the field data collection. The field data were collected in four different vegetation types: Calluna vulgaris-dominated heath (dry heath), Erica tetralix-dominated heath (wet heath), Molinia caerulea (grass-encroached heath), and coniferous woodland. Discrimination between burned and unburned areas differed among vegetation types. For the pooled dataset, bands in the near infrared (NIR) spectral region demonstrated the highest discriminatory power, followed by short wave infrared (SWIR) bands. Visible wavelengths performed considerably poorer. The Normalized Burn Ratio (NBR) outperformed the other spectral indices and the individual spectral bands in discriminating between burned and unburned areas. For the burn severity assessment, all spectral bands and indices showed low correlations with the field data GeoCBI, when data of all pre-fire vegetation types were pooled (R2 maximum 0.41). Analysis per vegetation type, however, revealed considerably higher correlations (R2 up to 0.78). The Mid Infrared Burn Index (MIRBI) had the highest correlations for Molinia and Erica (R2 = 0.78 and 0.42, respectively). In Calluna stands, the Char Soil Index (CSI) achieved the highest correlations, with R2 = 0.65. In Pinus stands, the Normalized Difference Vegetation Index (NDVI) and the red wavelength both had correlations of R2 = 0.64. The results of this study highlight the superior performance of the NBR to discriminate between burned and unburned areas, and the disparate performance of spectral indices to assess burn severity among vegetation types. Consequently, in heathlands, one must consider a stratification per vegetation type to produce more reliable burn severity maps. View Full-Text
Keywords: heathland; Composite Burn Index (CBI); GeoCBI; separability index; burn severity map; spectral indices; hyperspectral heathland; Composite Burn Index (CBI); GeoCBI; separability index; burn severity map; spectral indices; hyperspectral
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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Schepers, L.; Haest, B.; Veraverbeke, S.; Spanhove, T.; Vanden Borre, J.; Goossens, R. Burned Area Detection and Burn Severity Assessment of a Heathland Fire in Belgium Using Airborne Imaging Spectroscopy (APEX). Remote Sens. 2014, 6, 1803-1826.

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