Multispectral and LiDAR data fusion for fuel type mapping using Support Vector Machine and decision rules

This paper presents a method for mapping fuel types using LiDAR and multispectral data. A two-phase classification method is proposed to discriminate the fuel classes of the Prometheus classification system, which is adapted to the ecological characteristics of the European Mediterranean basin. The first step mapped the main fuel groups, namely grass, shrub and tree, as well as non-fuel classes. This phase was carried out using a Support Vector Machine (SVM) classification combining LiDAR and multispectral data. The overall accuracy of this classification was 92.8% with a kappa coefficient of 0.9. The second phase of the proposed method focused on discriminating additional fuel categories based on vertical information provided by the LiDAR measurements. Decision rules were applied to the output of the SVM classification based on the mean height of LiDAR returns and the vertical distribution of fuels, described by the relative LiDAR point density in different height intervals. The final fuel type classification yielded an overall accuracy of 88.24% with a kappa coefficient of 0.86. Some confusion was observed between fuel types 7 (dense tree cover presenting vertical continuity with understory vegetation) and 5 (trees with less than 30% of shrub cover) in some areas covered by Holm oak, which showed low LiDAR pulses penetration so that the understory vegetation was not correctly sampled.

Research highlights
► Fuel types were mapped fusing multispectral and LiDAR data.
► A two-phase approach was proposed. The first phase mapped the main fuel groups using Support Vector Machine. The second phase applied decision rules to discriminate additional fuel categories based on vertical information provided by the LiDAR measurements.
► The final fuel type classification yielded an overall accuracy of 88.24% with a kappa coefficient of 0.86.