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Remote Sens. 2018, 10(10), 1645; https://doi.org/10.3390/rs10101645

Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard

1
Escuela Politécnica Superior de Ingeniería, Universidad de Santiago de Compostela, Campus Universitario s/n, 27002 Lugo, Spain
2
Escuela de Ingeniería Agraria y Forestal, Universidad de León, Avda. Astorga s/n, 24401 Ponferrada, Spain
3
Escuela Politécnica de Mieres, Universidad de Oviedo, C/Gonzalo Gutiérrez de Quirós s/n, 33600 Mieres, Spain
4
Escuela Superior y Técnica de Ingenieros de Minas, Universidad de León, Avda. Astorga s/n, 24401 Ponferrada, Spain
5
Department of Forest Ecosystems and Society (FES), Oregon State University, 321 Richardson Hall, Corvallis, OR 97331, USA
6
Rocky Mountain Research Station, USFS, 507 25th Street, Ogden, UT 84401, USA
7
Centro de Investigación Forestal de Lourizán, P.O. Box 127, 36080 Pontevedra, Spain
*
Author to whom correspondence should be addressed.
Received: 12 September 2018 / Revised: 12 October 2018 / Accepted: 14 October 2018 / Published: 16 October 2018
(This article belongs to the Special Issue Remote Sensing of Wildfire)
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Abstract

Background: Crown fires are often intense and fast spreading and hence can have serious impacts on soil, vegetation, and wildlife habitats. Fire managers try to prevent the initiation and spread of crown fires in forested landscapes through fuel management. The minimum fuel conditions necessary to initiate and propagate crown fires are known to be strongly influenced by four stand structural variables: surface fuel load (SFL), fuel strata gap (FSG), canopy base height (CBH), and canopy bulk density (CBD). However, there is often a lack of quantitative data about these variables, especially at the landscape scale. Methods: In this study, data from 123 sample plots established in pure, even-aged, Pinus radiata and Pinus pinaster stands in northwest Spain were analyzed. In each plot, an intensive field inventory was used to characterize surface and canopy fuels load and structure, and to estimate SFL, FSG, CBH, and CBD. Equations relating these variables to Sentinel-2A (S-2A) bands and vegetation indices were obtained using two non-parametric techniques: Random Forest (RF) and Multivariate Adaptive Regression Splines (MARS). Results: According to the goodness-of-fit statistics, RF models provided the most accurate estimates, explaining more than 12%, 37%, 47%, and 31% of the observed variability in SFL, FSG, CBH, and CBD, respectively. To evaluate the performance of the four equations considered, the observed and estimated values of the four fuel variables were used separately to predict the potential type of wildfire (surface fire, passive crown fire, or active crown fire) for each plot, considering three different burning conditions (low, moderate, and extreme). The results of the confusion matrix indicated that 79.8% of the surface fires and 93.1% of the active crown fires were correctly classified; meanwhile, the highest rate of misclassification was observed for passive crown fire, with 75.6% of the samples correctly classified. Conclusions: The results highlight that the combination of medium resolution imagery and machine learning techniques may add valuable information about surface and canopy fuel variables at large scales, whereby crown fire potential and the potential type of wildfire can be classified. View Full-Text
Keywords: surface fuel load; fuel strata gap; canopy bulk density; canopy base height; multivariate adaptive regression splines; random forest surface fuel load; fuel strata gap; canopy bulk density; canopy base height; multivariate adaptive regression splines; random forest
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Arellano-Pérez, S.; Castedo-Dorado, F.; López-Sánchez, C.A.; González-Ferreiro, E.; Yang, Z.; Díaz-Varela, R.A.; Álvarez-González, J.G.; Vega, J.A.; Ruiz-González, A.D. Potential of Sentinel-2A Data to Model Surface and Canopy Fuel Characteristics in Relation to Crown Fire Hazard. Remote Sens. 2018, 10, 1645.

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