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

Abstract

Forest fires in Galicia have become a serious environmental problem over the years. This is especially the case in the Pontevedra region, where in October 2017 large fires (>500 hectares) burned more than 15,000 Ha. In addition to the area burned being of relevance, it is also very important to know quickly and accurately the different severity degrees that soil has suffered in order to carry out an optimal restoration campaign. In this sense, the use of remote sensing with the Sentinel-2 and Landsat-8 satellites becomes a very useful resource due to the variations that appear in soil after a forest fire (changes in soil cover are noticeably appreciated with spectral information). To calculate these variations, the spectral indices NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) are used, both before and after the fire and their differences (dNBR and dNDVI, respectively). In addition, as a reference for a correct discrimination between severity degrees, severity data measured in situ after the fire are used to classified at 5 levels of severity and 6 levels of severity. Therefore, this study aims to establish a methodology, which relates remote-sensing data (spectral indices) and severity degrees measured in situ. The R2 statistic and the pixel classification accuracy results show the existing synergy of the Sentinel-2 dNBR index with the 5 severity degrees classification (R2 = 0.74 and 81% of global accuracy) and, for this case, the good applicability of remote sensing in the forest fire field.

Mapping Soil Burn Severity at Very High Spatial Resolution from Unmanned Aerial Vehicles

“…In this study, we evaluated for first time the potential of very high-resolution multispectral and RGB products obtained by UAVs to characterize soil burn severity. The accuracies reached in our work when predicting soil burn severity with UAVs were similar to or higher than those found in other studies related to total burn severity [10,24,82,83], demonstrating the potential of high-resolution multispectral imagery for soil burn severity assessment.…”

“…Although previous works have analyzed the potential of UAV-derived products in the fire ecology field [42,61], this study is pioneer in evidencing the usefulness of multispectral sensors on board UAVs for accurately mapping soil burn severity at very high spatial resolution, which is particularly relevant in postfire landscapes with a high fine-scale spatial heterogeneity [83,92,93]. However, future research should be conducted to validate the proposed methodology in other biomes, such as boreal forests, where fire disturbances can also originate substantial heterogeneity in soil burn severity at both plot and landscape scales [94].…”

Environmental Aftermath of the 2019 Stromboli Eruption

“…The Normalized Burn Ratio (NBR) index has been used to map the areas covered by fire, which has permitted to easily identify the areas covered by the fire and the degree of severity of a fire [10][11][12][13][14][15][16]. This index has been calculated on two Sentinel-2 images acquired on different dates before and after the wildfire (after a not excessively high number of days, especially if the area affected by the fire consists mainly of pasture or low bush).…”

Machine Learning Methods and Synthetic Data Generation to Predict Large Wildfires

“…The study area is located in southwestern Spain (37°28′42″ N, 6°54′19″ W, WGS-84), more specifically in western Andalusia, covering the province of Huelva. Every year, there are large recurrent wildfires in the area, defined, in this study, as those that exceed a burn area higher than 500 hectares [ 75 , 76 ]. While in Spain as a whole, the percentage of large wildfires is low, 0.48% in 2017, in Huelva, there is always at least one per year [ 8 ].…”