Carbone, A.; Spiller, D.; Laneve, G. Fuel Type Mapping Using a CNN-Based Remote Sensing Approach: A Case Study in Sardinia. Fire2023, 6, 395.
Carbone, A.; Spiller, D.; Laneve, G. Fuel Type Mapping Using a CNN-Based Remote Sensing Approach: A Case Study in Sardinia. Fire 2023, 6, 395.
Carbone, A.; Spiller, D.; Laneve, G. Fuel Type Mapping Using a CNN-Based Remote Sensing Approach: A Case Study in Sardinia. Fire2023, 6, 395.
Carbone, A.; Spiller, D.; Laneve, G. Fuel Type Mapping Using a CNN-Based Remote Sensing Approach: A Case Study in Sardinia. Fire 2023, 6, 395.
Abstract
Accurate fuel mapping plays a crucial role in fire detection and management strategies. This paper presents a method for discriminating between wildfire fuel types by exploiting together remote sensing data and Convolutional Neural Networks (CNN). Specially, a CNN-based classification approach that leverages Sentinel-2 imagery is exploited to accurately classify fuel types into seven preliminary main classes (conifers, broadleaf, shrubs, grass, bare soil, urban areas, and water bodies) with an high accuracy of 0.99$\%$. To further refine the fuel mapping results, subclasses were generated from the seven principals by using biomass and bioclimatic maps. These additional maps provide complementary information about vegetation density and climatic conditions, respectively. By incorporating this information, we align our fuel type classification with the widely used Scott/Burgan fuel classification system. This refinement step allows for a more detailed and comprehensive assessment of fuel types, enhancing the accuracy and effectiveness of fire management efforts, which can be utilized by fire management agencies, policymakers, and researchers for improved fire behavior prediction and mitigation practices. The proposed approach presents a valuable tool for enhancing fire management, contributing to more effective wildfire prevention and mitigation efforts.
Keywords
Fuel Mapping; Sentinel 2; CNN Classification
Subject
Engineering, Civil Engineering
Copyright:
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.
