Abstract
Fire severity is a key factor for management of post-fire vegetation regeneration strategies because it quantifies the impact of fire, describing the amount of damage. Several indices have been developed for estimation of fire severity based on terrestrial observation by satellite imagery. In order to avoid the implicit limitations of this kind of data, this work employed an Unmanned Aerial Vehicle (UAV) carrying a high-resolution multispectral sensor including green, red, near-infrared, and red edge bands. Flights were carried out pre- and post-controlled fire in a Mediterranean forest. The products obtained from the UAV-photogrammetric projects based on the Structure from Motion (SfM) algorithm were a Digital Surface Model (DSM) and multispectral images orthorectified in both periods and co-registered in the same absolute coordinate system to find the temporal differences (d) between pre- and post-fire values of the Excess Green Index (EGI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Red Edge (NDRE) index. The differences of indices (dEGI, dNDVI, and dNDRE) were reclassified into fire severity classes, which were compared with the reference data identified through the in situ fire damage location and Artificial Neural Network classification. Applying an error matrix analysis to the three difference of indices, the overall Kappa accuracies of the severity maps were 0.411, 0.563, and 0.211 and the Cramer’s Value statistics were 0.411, 0.582, and 0.269 for dEGI, dNDVI, and dNDRE, respectively. The chi-square test, used to compare the average of each severity class, determined that there were no significant differences between the three severity maps, with a 95% confidence level. It was concluded that dNDVI was the index that best estimated the fire severity according to the UAV flight conditions and sensor specifications.
Highlights
One of the factors with the most influence on Mediterranean forest ecosystems is wildfires, as they damage the vegetation layer, modifying the water and sediment patterns and nutrient cycling [1,2,3]
The Excess Green Index (EGI) index in the domain of the visible spectrum and the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge (NDRE) indices in the domain of the multispectral spectrum were applied to fire severity mapping based on Unmanned Aerial Vehicle (UAV) photogrammetric products and evaluated
Regarding the d-EGI, d-NDVI, and d-NDRE comparison, no significant differences were obtained between their classes but the similarity between d-NDVI and the reference data was higher
Summary
One of the factors with the most influence on Mediterranean forest ecosystems is wildfires, as they damage the vegetation layer, modifying the water and sediment patterns and nutrient cycling [1,2,3]. Fire severity is a key factor for the management of post-fire vegetation regeneration strategies [4,5]. Because it quantifies the impact of fire, describing the amount of damage inside the boundaries of a wildfire. Several indirect estimations of burn and fire severity have been developed based on remote sensing imagery. While the concept of burn severity includes both shortand long-term impacts of fire on an ecological system, fire severity only quantifies the short-term effects of fire in the immediate post-fire context [6]. The indirect estimation methods can be divided into three groups: spectral unmixing [7,8], simulation techniques [9] and spectral indices [10,11]
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