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

dc.contributor.authorSaglam, Bülent
dc.contributor.authorBilgili, Ertugrul
dc.contributor.authorDincdurmaz, Bahar
dc.contributor.authorKadiogulari, Ali Ihsan
dc.contributor.authorKücük, Ömer
dc.date.accessioned2023-04-09T01:02:24Z
dc.date.accessioned2023-04-12T00:26:14Z
dc.date.available2023-04-09T01:02:24Z
dc.date.available2023-04-12T00:26:14Z
dc.date.issued2008-06-20T00:00:00Z
dc.description.abstractComputing 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%.
dc.identifier.doi10.3390/s8063970
dc.identifier.issn1424-8220
dc.identifier.pubmed27879918
dc.identifier.urihttps://hdl.handle.net/20.500.12597/3639
dc.language.isoen
dc.relation.ispartofSensors (Basel, Switzerland)
dc.subjectGIS
dc.subjectfire danger
dc.subjectfire risk
dc.subjectforest fire management
dc.subjectlandsat
dc.subjectspatial analysis
dc.titleSpatio-Temporal Analysis of Forest Fire Risk and Danger Using LANDSAT Imagery.
dc.typeJournal Article
dspace.entity.typePubmed
oaire.citation.issue6
oaire.citation.volume8

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