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

Remote Sensing

Lennert Schepers¹  Birgen Haest³  Sander Veraverbeke⁴  Toon Spanhove²  Jeroen Vanden Borre² 

[1] Ecosystem Management Research Group, University of Antwerp, Universiteitsplein 1, Wilrijk B-2610, Belgium;INBO, Research Institute for Nature and Forest, Kliniekstraat 25, Brussel B-1070, Belgium; E-Mails:;Remote Sensing Department, Flemish Institute for Technological Research (VITO), Boeretang 200, Mol B-2400, Belgium; E-Mail:;Department of Earth System Science, UCI, University of California, Irvine, 2224 Croul Hall, Irvine 92697, CA, USA; E-Mail:

关键词: heathland;    Composite Burn Index (CBI);    GeoCBI;    separability index;    burn severity map;    spectral indices;    hyperspectral;   

DOI  :  10.3390/rs6031803

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland