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

Abstract

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 1804 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 (R 2 maximum 0.41). Analysis per vegetation type, however, revealed considerably higher correlations (R 2 up to 0.78). The Mid Infrared Burn Index (MIRBI)had the highest correlations for Molinia and Erica (R 2 = 0.78 and 0.42, respectively).In Calluna stands, the Char Soil Index (CSI) achieved the highest correlations, with R 2 = 0.65. In Pinus stands, the Normalized Difference Vegetation Index (NDVI) and the red wavelength both had correlations of R 2 = 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.

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

“…Among them, MIRBI, BAI, and Greenness are the best when averaging all vegetation types, while NDVI and NBR get better scores in isolated vegetation types. Other studies obtained similar results, e.g., better discrimination capabilities of BAI [18,22,28], Greenness [24,62], or MIRBI in shrub-savannah ecosystems [25,27,52]. 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.…”

“…Initially designed for burned area extraction, NBR is the most popular spectral index used for burn severity assessments with different sensors in several ecosystems around the world. In numerous comparative analyses, NBR proved to be one of the most efficient SI [14,19,21,23,28]. In time series analyses, NBR showed good correlations with field-based composite burn index scores several years after a fire, thus representing an efficient tool for vegetation recovery monitoring [29,30].…”

“…Several authors working in these vegetation types have reported its good performance, often superior to the widely used NBR and other SI [25,28].…”

Exploring the Relationship between Burn Severity Field Data and Very High Resolution GeoEye Images: The Case of the 2011 Evros Wildfire in Greece

“…NDVI is a commonly used index for post-fire vegetation response in the literature [2,33,43] and is based on the absorption and reflection characteristics of plants in the red and near-infrared spectral regions [12]. In addition to NDVI, previous work [44] has proposed the use of the Normalized Burn Ratio (NBR) [26], and the differenced Normalized Burn Ratio (dNBR) [26] for detecting and monitoring post-fire vegetation changes over time.…”

“…First, we computed the mean value of each spectral band (i.e., blue, green, red, near-infrared). Then, since various studies have shown that the variation of the vegetation cover after a severe fire event is highly correlated with the magnitude of the burn severity [12,33,41] we computed the mean Normalized Difference Vegetation Index (NDVI) [42] per sampling plot. We applied the same procedure to both post-fire images (i.e., GeoEye time1 and GeoEye time2 ).…”