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 Gümüldür 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.

中文翻译：

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林业烧毁面积测绘无监督分类方法的比较分析

由于地理位置，高温和低湿度，森林火灾在土耳其造成了很多破坏。准确确定烧毁的森林面积对于正确的损害评估研究，火灾风险计算以及森林更新过程的审查至关重要。在这项研究中，我们比较了使用K均值从中等分辨率卫星图像中提取燃烧区域的无监督分类方法（以前尚未用于映射燃烧区域的方法）的性能。在这方面，使用Landsat 8影像确定了2016年库姆卢卡和阿德拉桑地区，2017年阿兰亚和居米尔德地区以及2018年雅典地区受火灾影响的区域。为此，Canopy，M树，分层聚类算法，并使用文献中经常使用的学习矢量量化来确定燃烧区域，并将获得的结果与从K均值获得的结果进行比较。结果表明，无监督分类方法可用于绘制燃烧区域图。分层聚类和K均值算法在绘制研究中使用的大多数区域的燃烧区域图时提供了最准确的结果。