Remote Sensing (Jun 2021)

Evaluating the Differenced Normalized Burn Ratio for Assessing Fire Severity Using Sentinel-2 Imagery in Northeast Siberian Larch Forests

  • Clement J. F. Delcourt,
  • Alisha Combee,
  • Brian Izbicki,
  • Michelle C. Mack,
  • Trofim Maximov,
  • Roman Petrov,
  • Brendan M. Rogers,
  • Rebecca C. Scholten,
  • Tatiana A. Shestakova,
  • Dave van Wees,
  • Sander Veraverbeke

DOI
https://doi.org/10.3390/rs13122311
Journal volume & issue
Vol. 13, no. 12
p. 2311

Abstract

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Fire severity is a key fire regime characteristic with high ecological and carbon cycle relevance. Prior studies on boreal forest fires primarily focused on mapping severity in North American boreal forests. However, the dominant tree species and their impacts on fire regimes are different between North American and Siberian boreal forests. Here, we used Sentinel-2 satellite imagery to test the potential for using the most common spectral index for assessing fire severity, the differenced Normalized Burn Ratio (dNBR), over two fire scars and 37 field plots in Northeast Siberian larch-dominated (Larix cajanderi) forests. These field plots were sampled into two different forest types: (1) dense young stands and (2) open mature stands. For this evaluation, the dNBR was compared to field measurements of the Geometrically structured Composite Burn Index (GeoCBI) and burn depth. We found a linear relationship between dNBR and GeoCBI using data from all forest types (R2 = 0.42, p 2 = 0.56, p p > 0.05 in all cases). However, the dNBR showed some potential as a predictor for burn depth, especially in the dense larch forests (R2 = 0.63, p < 0.001). In line with previous studies in boreal North America, the dNBR correlated reasonably well with field data of aboveground fire severity and showed some skills as a predictor of burn depth. More research is needed to refine spaceborne fire severity assessments in the larch forests of Northeast Siberia, including assessments of additional fire scars and integration of dNBR with other geospatial proxies of fire severity.

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