Skip to Main Content
Potential of Sentinel-2A data to model surface and canopy fuel characteristics in relation to crown fire hazardAuthor(s): Stefano Arellano-Perez; Fernando Castedo-Dorado; Carlos Antonio Lopez-Sanchez; Eduardo Gonzalez-Ferreiro; Zhiqiang Yang; Ramon Alberto Díaz-Varela; Juan Gabriel Alvarez-Gonzalez; Jose Antonio Vega; Ana Daria. Ruiz-Gonzalez
Source: Remote Sensing. 10: 1645.
Publication Series: Scientific Journal (JRNL)
Station: Rocky Mountain Research Station
Download Publication (1.0 MB)
DescriptionBackground: 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.
- You may send email to email@example.com to request a hard copy of this publication.
- (Please specify exactly which publication you are requesting and your mailing address.)
- We recommend that you also print this page and attach it to the printout of the article, to retain the full citation information.
- This article was written and prepared by U.S. Government employees on official time, and is therefore in the public domain.
CitationArellano-Perez, Stefano; Castedo-Dorado, Fernando; Lopez-Sanchez, Carlos Antonio; Gonzalez-Ferreiro, Eduardo; Yang, Zhiqiang; Díaz-Varela, Ramon Alberto; Alvarez-Gonzalez, Juan Gabriel; Vega, Jose Antonio; Ruiz-Gonzalez, Ana Daria. 2018. Potential of Sentinel-2A data to model surface and canopy fuel characteristics in relation to crown fire hazard. Remote Sensing. 10: 1645.
Keywordssurface fuel load, fuel strata gap, canopy bulk density, canopy base height, multivariate adaptive regression splines, random forest
- Influence of Crown Biomass Estimators and Distribution on Canopy Fuel Characteristics in Ponderosa Pine Stands of the Black Hills
- Estimating canopy bulk density and canopy base height for interior western US conifer stands
- Effects of alternative treatments on canopy fuel characteristics in five conifer stands
XML: View XML