Abstract
The evaluation of the effect of burn severity on forest soils is essential to determine the impact of wildfires on a range of key ecological processes, such as nutrient cycling and vegetation recovery. The main objective of this study was to assess the potentiality of different spectral products derived from RGB and multispectral imagery collected by unmanned aerial vehicles (UAVs) at very high spatial resolution for discriminating spatial variations in soil burn severity after a heterogeneous wildfire. In the case study, we chose a mixed-severity fire that occurred in the northwest (NW) of the Iberian Peninsula (Spain) in 2019 that affected 82.74 ha covered by three different types of forests, each dominated by Pinus pinaster, Pinus sylvestris, and Quercus pyrenaica. We evaluated soil burn severity in the field 1 month after the fire using the Composite Burn Soil Index (CBSI), as well as a pool of five individual indicators (ash depth, ash cover, fine debris cover, coarse debris cover, and unstructured soil depth) of easy interpretation. Simultaneously, we operated an unmanned aerial vehicle to obtain RGB and multispectral postfire images, allowing for deriving six spectral indices. Then, we explored the relationship between spectral indices and field soil burn severity metrics by means of univariate proportional odds regression models. These models were used to predict CBSI categories, and classifications were validated through confusion matrices. Results indicated that multispectral indices outperformed RGB indices when assessing soil burn severity, being more strongly related to CBSI than to individual indicators. The Normalized Difference Water Index (NDWI) was the best-performing spectral index for modelling CBSI (R2cv = 0.69), showing the best ability to predict CBSI categories (overall accuracy = 0.83). Among the individual indicators of soil burn severity, ash depth was the one that achieved the best results, specifically when it was modelled from NDWI (R2cv = 0.53). This work provides a useful background to design quick and accurate assessments of soil burn severity to be implemented immediately after the fire, which is a key factor to identify priority areas for emergency actions after forest fires.
Highlights
Moderate and high soil burn severity categories defined by the Composite Burn Soil Index (CBSI) in the field had a similar mean spectral signature through the four bands of the multispectral orthomosaic, as opposed to the spectral signature of the low soil burn severity category (Figure 3A)
Discussion erate and high categories found in the NIR region and the good performance of In this we evaluated for first time potential very high-resolution multhe Normalized Difference Water Index (NDWI) are two piecesstudy, of evidence that demonstrate thethe relevance ofof
This study constitutes an interesting contribution to fire ecology research and forest management, as it shows, for the first time, the ability of multispectral and RGB imagery collected with unmanned aerial vehicles (UAVs) technology to characterize soil burn severity in landscapes affected by mixed-severity wildfires
Summary
Wildfires are major drivers of forest functioning [1,2,3]. In the Mediterranean Basin, current shifts in fire regime parameters (e.g., frequency, intensity, and severity), associated with global change trends, might generate harsh ecological effects in forest ecosystems [4]. Relevant are the ecological consequences of burn severity, defined as the magnitude of the environmental change caused by fire [5]. Burn severity patterns might vary at different scales across the landscape due to environmental heterogeneity associated 4.0/). With differences in topography, moisture, vegetation diversity, and flammability. Complex and heterogeneous land mosaics can emerge after fire occurrence [6,7]
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