<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE ArticleSet PUBLIC "-//NLM//DTD PubMed 2.7//EN" "https://dtd.nlm.nih.gov/ncbi/pubmed/in/PubMed.dtd">
<ArticleSet>
<Article>
<Journal>
				<PublisherName>Urmia University</PublisherName>
				<JournalTitle>Forest Research and Development</JournalTitle>
				<Issn>2476-3551</Issn>
				<Volume>6</Volume>
				<Issue>1</Issue>
				<PubDate PubStatus="epublish">
					<Year>2020</Year>
					<Month>05</Month>
					<Day>21</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Forest fire hazard zone mapping in Ilam county forests</ArticleTitle>
<VernacularTitle>Forest fire hazard zone mapping in Ilam county forests</VernacularTitle>
			<FirstPage>135</FirstPage>
			<LastPage>152</LastPage>
			<ELocationID EIdType="pii">120830</ELocationID>
			
<ELocationID EIdType="doi">10.30466/jfrd.2020.120830</ELocationID>
			
			<Language>FA</Language>
<AuthorList>
<Author>
					<FirstName>Sara </FirstName>
					<LastName>Polat</LastName>
<Affiliation>Master of Forestry Student, Faculty of Natural Resource and Environment, Malayer University, Malayer, I. R. Iran.</Affiliation>

</Author>
<Author>
					<FirstName>Farhad </FirstName>
					<LastName>Ghasemi Aghbash</LastName>
<Affiliation>Assistant Professor, Department of Rangeland and Watershed Management, Faculty of Natural Resource and Environment, Malayer University, Malayer, I. R. Iran.</Affiliation>

</Author>
<Author>
					<FirstName>Ali </FirstName>
					<LastName>Mahdavi</LastName>
<Affiliation>Associate Professor, Department of Forestry, Faculty of Natural Resource, Ilam University, Ilam, I. R. Iran.</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2018</Year>
					<Month>10</Month>
					<Day>07</Day>
				</PubDate>
			</History>
		<Abstract>Several factors play an important role in the occurrence of fire in forests. So accurate prediction of the time and place of the fire is difficult, but using the GIS, it is possible to identify points of high fire risk. The purpose of this study was to investigate the possibility of preparing a fire hazard zonation map for Ilam district. Socioeconomic, climatic, topographic and vegetation factors were considered as suitable criteria for assessing the status of fire risk of these forests and were evalutaed Using satellite imagery data, geographic information system and neural network system. According to the results, the variable of distance from cities with a weight of 100% is the most influential variable in creating fire and height variable with a weight of 1.5% as the least important variable in the occurrence of forest fire in study area. Based on the results of the assessment of the accuracy of the fire hazard zonation map using the error matrix, it was found that the produced map with a general accuracy of 73.73% is highly true. Also, based on Kappa agreement coefficient (0.77) between the map and actual fire data, it can be concluded that the map prepared with the actual data was highly adapted.</Abstract>
			<OtherAbstract Language="FA">Several factors play an important role in the occurrence of fire in forests. So accurate prediction of the time and place of the fire is difficult, but using the GIS, it is possible to identify points of high fire risk. The purpose of this study was to investigate the possibility of preparing a fire hazard zonation map for Ilam district. Socioeconomic, climatic, topographic and vegetation factors were considered as suitable criteria for assessing the status of fire risk of these forests and were evalutaed Using satellite imagery data, geographic information system and neural network system. According to the results, the variable of distance from cities with a weight of 100% is the most influential variable in creating fire and height variable with a weight of 1.5% as the least important variable in the occurrence of forest fire in study area. Based on the results of the assessment of the accuracy of the fire hazard zonation map using the error matrix, it was found that the produced map with a general accuracy of 73.73% is highly true. Also, based on Kappa agreement coefficient (0.77) between the map and actual fire data, it can be concluded that the map prepared with the actual data was highly adapted.</OtherAbstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Forest fire</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Ilam</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Neural network</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Zagros</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Zoning</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://jfrd.urmia.ac.ir/article_120830_390247b28323020605a9fdd0da188ebd.pdf</ArchiveCopySource>
</Article>
</ArticleSet>
