Next Article in Journal
Positioning Methods and the Use of Location and Activity Data in Forests
Previous Article in Journal
Whitebark Pine Recruitment in Sierra Nevada Driven by Range Position and Disturbance History
Open AccessArticle

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

1
Global Change Unit, Image Processing Laboratory, University of Valencia, E-46980 Paterna, Spain
2
Centro de Investigación Forestal de Lourizán, Xunta de Galicia, E-36156 Pontevedra, Spain
*
Author to whom correspondence should be addressed.
Forests 2019, 10(5), 457; https://doi.org/10.3390/f10050457
Received: 15 April 2019 / Revised: 22 May 2019 / Accepted: 23 May 2019 / Published: 26 May 2019
(This article belongs to the Section Forest Inventory, Quantitative Methods and Remote Sensing)
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. View Full-Text
Keywords: forest fires; Galicia; Sentinel-2; Landsat 8; spectral indices; severity degrees forest fires; Galicia; Sentinel-2; Landsat 8; spectral indices; severity degrees
Show Figures

Figure 1

MDPI and ACS Style

Sobrino, J.A.; Llorens, R.; Fernández, C.; Fernández-Alonso, J.M.; Vega, J.A. Relationship between Soil Burn Severity in Forest Fires Measured In Situ and through Spectral Indices of Remote Detection. Forests 2019, 10, 457.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop