RT info:eu-repo/semantics/article
T1 Mapping Soil Burn Severity at Very High Spatial Resolution from Unmanned Aerial Vehicles
A1 Beltrán Marcos, David
A1 Suárez-Seoane, Susana
A1 Fernández Guisuraga, José Manuel
A1 Fernández García, Víctor
A1 Pinto Prieto, Rayo
A1 García Llamas, Paula
A1 Calvo Galván, María Leonor
A2 Ecologia
K1 Ecología. Medio ambiente
K1 Ingeniería forestal
K1 Ceniza
K1 Suelo
K1 Índices de agua
K1 2417.13 Ecología Vegetal
K1 2511.06 Conservación de Suelos
AB 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.
PB MDPI
SN 1999-4907
LK http://hdl.handle.net/10612/12947
UL http://hdl.handle.net/10612/12947
NO Artículo
DS BULERIA. Repositorio Institucional de la Universidad de León
RD 27-abr-2024