Akademik Veri Yönetim Sistemi
Journal of Disaster and Risk, cilt.6, sa.1, ss.206-225, 2023 (Scopus, TRDizin)
Forest fires are part of natural processes that affect ecosystems all around the world. Fire affects biophysical processes at different spatio-temporal scales, from micro-scale impacts to broad landscape patterns and processes. In order to implement post-fire decision-making processes, managers should be able to adequately characterize burned areas. This is possible by determining fire severity, which is considered as the degree of fire-induced ecological change in both vegetation and soil and is one of the most important components of the fire regime. Fire severity can be classified based on visual observation of the degree of fuels consumed and the amount of char on plant and soil surfaces that remained unconsumed after the fire. Fire severity is generally classified as unburned, low, moderate, and high. Assessing post-fire damage of large areas can take a lot of effort, money, and time. Therefore, after large fires, remote sensing methods are often used to determine the extent of fire damage to the ecosystems. Fire severity classifications are usually expressed in terms of spectral indices derived from optical remote sensing data or by using maps derived from active remote sensing methods such as SAR and LiDAR. Fire severity classification maps allow to determine the impact of forest fires on soil, water, ecosystem flora and fauna, and the atmosphere. They can therefore promote a more sustainable ecosystem-based planning of burned/unburned areas. In this review, information about the concept of fire severity and fire severity classification is given. For future studies on this topic, the literature was reviewed and summarized and its advantages and disadvantages were discussed.