National fuel type mapping methodology using geographic object based image analysis and Landsat 8 Oli imagery

Abstract

A key issue in modern fire management planning is the accurate fuel type mapping, required at many different spatial and temporal scales. Fuel type classification is critical for improving fire prevention schemes, developing accurate fire dispersion models and designing effective measures for mitigating the impacts of a potential wildfire event on the ecosystem. Remote sensing offers the potential to provide spatially distributed information on fuel types and it has been widely used both at regional and local scale. The aim of this study is to develop and evaluate a fuel type mapping methodology on a national level, based on the geographic object-oriented classification approach and Landsat-8 OLI imagery. The proposed methodology was developed in the case study of Chalkidiki in the northern part of Greece and it was further tested for its transferability in the regional unit of Preveza and the whole administrative region of Attica. The classification scheme was determined taking under consideration the existing fuel models (Prometheus, FBP etc) and fuel type products (JRC FUELMAP, LIFE10 ArcFUEL) so as the final fuel type map could be easily adjusted and/or compared. Fifty-four Landsat-8 OLI images for summer and for winter season, covering the national territory, were acquired and several features (vegetation indices, textural and spectral features) were calculated for both seasons. The optimized feature selection for the discrimination of each fuel type category was empirically obtained. Results showcase the effectiveness of the employed object-oriented classification approach in obtaining highly accurate fuel type maps. Specifically, the generated fuel map of Chalkidiki exhibited an overall accuracy of 89.47%, with a Kappa Index of Agreement (KIA) equal to 0.844. The application of the model to the two other regions resulted in overall accuracies of 80.30% (KIA=0.706) for Attica and 91.74% (KIA=0.867) for Preveza. Summarizing, the results proved the good transferability properties of the proposed methodology enabling the implementation of the model across the country.