Comparative analysis of unsupervised classification methods for mapping burned forest areas

Abstract

Forest fires cause a lot of damage in Turkey, due to the geographical location, high temperatures, and low humidity levels. Accurate determination of burned forest areas is crucial for correct damage assessment studies, fire risk calculations, and review of the forest regeneration processes. In this study, we compare the performances of unsupervised classification methods (which have not been used to map burned areas before) of burned area extraction from medium resolution satellite images with K-means. In this regard, the areas affected by fire in the Kumluca and Adrasan regions in 2016, Alanya and Gumuldur regions in 2017 and Athens region in 2018 are determined using Landsat 8 images. For this purpose, Canopy, M-tree, a hierarchical clustering algorithm, and a learning vector quantization which are frequently used in the literature are applied to determine the burned area, and the results obtained are compared with the results obtained from K-means. The results show that unsupervised classification methods can be used to map burned areas. The hierarchical clustering and K-means algorithms provide the most accurate results in mapping burned areas in most of the regions used in the study.