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Spatio-Temporal Analysis of Forest Fire Risk and Danger Using LANDSAT Imagery

Bülent SağlamArtvin Çoruh University, Faculty of Forestry, 08000 Artvin- TurkeyErtuğrul BılgılıKaradeniz Technical University, Faculty of Forestry 61080, Trabzon-TurkeyBahar DincdurmazKaradeniz Technical University, Faculty of Forestry 61080, Trabzon-TurkeyAli Ihsan KadiogulariKaradeniz Technical University, Faculty of Forestry 61080, Trabzon-TurkeyÖmer KüçükKastamonu University, Faculty of Forestry 37100, Kastamonu-Turkey
2008en
ABI

Аннотация

Computing fire danger and fire risk on a spatio-temporal scale is of crucial importance in fire management planning, and in the simulation of fire growth and development across a landscape. However, due to the complex nature of forests, fire risk and danger potential maps are considered one of the most difficult thematic layers to build up. Remote sensing and digital terrain data have been introduced for efficient discrete classification of fire risk and fire danger potential. In this study, two time-series data of Landsat imagery were used for determining spatio-temporal change of fire risk and danger potential in Korudag forest planning unit in northwestern Turkey. The method comprised the following two steps: (1) creation of indices of the factors influencing fire risk and danger; (2) evaluation of spatio-temporal changes in fire risk and danger of given areas using remote sensing as a quick and inexpensive means and determining the pace of forest cover change. Fire risk and danger potential indices were based on species composition, stand crown closure, stand development stage, insolation, slope and, proximity of agricultural lands to forest and distance from settlement areas. Using the indices generated, fire risk and danger maps were produced for the years 1987 and 2000. Spatio-temporal analyses were then realized based on the maps produced. Results obtained from the study showed that the use of Landsat imagery provided a valuable characterization and mapping of vegetation structure and type with overall classification accuracy higher than 83%.

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