Evaluation of afforestations for avalanche protection with orthoimages using the random forest algorithm

Abstract

Afforestations provide cost-effective and environmentally friendly protection against natural hazards, compared to technical measures. In Austria, more than 3000 afforestation sites for hazard protection covering 9000 ha have been established between 1906 and 2017, mainly for snow avalanche protection. The actual protective effect depends on avalanche predisposing factors and land cover, i.e. whether forest is present. In this study, predisposing factors and land cover classes were identified and analysed in selected afforestation sites. The protective effect of forest was attributed to the presence of forest cover and tree species. Using RGB images with a ground resolution of 20 × 20 cm, nine land cover categories have been distinguished by means of supervised classification with the random forest algorithm. Those land cover categories were classified with an overall accuracy of 0.87–0.98 and Kappa-values, ranging between 0.81 and 0.93. Images were filtered using a 3 pixel by 3 pixel majority filter, which assigns each cell in the output grid the most commonly occurring value in a moving window centred on each grid cell. This filter further increased the overall accuracy by removing noise pixels while preserving the fine elements of the classified grid. Our results indicate a protective effect for about half of the analysed afforestation sites. The dominance of the land use class “Meadow” at most sites with little avalanche protection effect suggests grazing as a limiting factor. The spatial information provided with the described method allows to identify critical areas in terms of avalanche protection even years after the initial afforestation.