Validación de los índices de teledetección dNBR y RdNBR para determinar la severidad del fuego en el incendio forestal de Oia-O Rosal (Pontevedra) en 2013

Abstract

<p>Fire severity evaluation and mapping following wildfire is an essential task for post-fire rehabilitation activities and forest management planning. For that purpose, some spectral indexes are used to quantify the changes caused by fire, being Landsat satellite one of the most frequently used. Even though Galicia is the Spanish region with the highest number of fires in the country, the information on fire severity estimation through satellite imagery is scarce. In the present study, the capacity of dNBR (differenced Normalized Burn Ratio) and RdNBR (Relative difference Normalized Burn Ratio), through Landsat 8 imagery processing, are compared for the first time in Galicia to test both indexes with field data following the methodology from CBI (Composite Burn Index) in Oia-O Rosal (Pontevedra) wildfire occurred in the summer of 2013. The results indicate that the models for dNBR and RdNBR estimation according to CBI were similar, explaining a 69 and 73% of variability, respectively. These models allow to obtain a new fire severity thresholds for dNBR and RdNBR for the burned area. Although, both indexes showed a similar and quite high overall accuracy in the classification of the different fire severity classes (75% y 83% for dNBR and RdNBR, respectively), RdNBR was slightly more accurate than dNBR. Additionally, the dNBR-based fire severity map significantly underestimated the high fire severity area, compared with RdNBR. Those preliminary results can be useful to evaluate fire severity spatial distribution, in wildfires in Galicia although new data will be necessary before an operational tool to be available.</p>

Relationship between Soil Burn Severity in Forest Fires Measured In Situ and through Spectral Indices of Remote Detection

“…The R 2 coefficient obtained are lower (range 0.56 to 0.72 for dNBR and range 0.47 to 0.56 for dNDVI) than obtained by Mallinis et al, 2018 (range 0.83 to 0.86 for dNBR and range 0.78 to 0.8 for dNDVI) [57]. In other previous studies [28,34], despite the use of other remote-sensing data (for example, Landsat 7 or Landsat TM), the R 2 similarity obtained is greater (0.69 for dNBR and 0.46 for dNDVI, respectively).…”

“…The use of a statistical analysis based on a potential regression is due to the low linear relationship between remote-sensing data and field measurements as found in previous published research [28,33]. This fact is due to different factors such as saturation in the SWIR bands and the NIR bands values decreasing (especially in high-severity areas) [57].…”

“…First, this study incorporates as an innovation the field data protocol proposed by Vega et al, 2013 [52], focused on the evaluation of soil burn severity in areas where vegetation was completely consumed by fire. Therefore, the analysis differs from previous studies using the Composite Burn Index (CBI) or a modified version (GeoCBI), which combine vegetation and soil severities measures [28][29][30][31][32][33][54][55][56][57].…”

“…Soil burn severity is an essential component of fire severity and its assessment is critical to determine the hydrological response following fire [26,27]. The capability of remoted-sensed spectral indexes to reflect different degrees of fire severity has been repeatedly shown, e.g., [28][29][30][31][32][33][34]. However, their suitability for evaluation of soil burn severity has been questioned [35][36][37].…”

Mapping fire severity levels of burned areas in Galicia (NW Spain) by Landsat images and the dNBR index: preliminary results about the influence of topographical, meteorological and fuel factors on the highest severity level

“…These thresholds are based on the existing relationship between the Composite Burning Index (CBI, Key and Benson 2006) and the dNBR for several wildfires that occurred in Galicia in 2013: unburned (dNBR<12.3); low (12.3≤dNBR<262.4); moderate (262.4≤dNBR<577.2) and high (577.2≤dNBR). Likewise, a level of canopy damage was assigned to each tree-covered pixel according to Arellano (2014) and Arellano et al (2017). The four levels of canopy damage considered are shown in Table 1.…”