{"title":"Modeling and mapping forest fire risk in the region of Aures (Algeria)","authors":"S. Rahmani, Hassen Benmassoud","doi":"10.15291/geoadria.2846","DOIUrl":null,"url":null,"abstract":"The objective of this study is to modeling and mapping the Forest fire risk in the region of Aures situated in Northeast of Algeria, through the application of multi-criteria analysis methods to integrate geographic information systems (GIS) and remote sensing. The methodology is based on a weighted linear combination of three parameters that influence the initiation and propagation of a forest fire. These are vegetation, topography and anthropogenic index. The result is a vulnerability map classified into four classes according to pixel values. very high risk class forms 18,28% of the study area, high risk class forms 42,42%, moderate risk class forms 5,24% and 34,05% of the area is low risk.","PeriodicalId":42640,"journal":{"name":"Geoadria","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2020-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.15291/geoadria.2846","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoadria","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15291/geoadria.2846","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GEOGRAPHY","Score":null,"Total":0}
引用次数: 3
Abstract
The objective of this study is to modeling and mapping the Forest fire risk in the region of Aures situated in Northeast of Algeria, through the application of multi-criteria analysis methods to integrate geographic information systems (GIS) and remote sensing. The methodology is based on a weighted linear combination of three parameters that influence the initiation and propagation of a forest fire. These are vegetation, topography and anthropogenic index. The result is a vulnerability map classified into four classes according to pixel values. very high risk class forms 18,28% of the study area, high risk class forms 42,42%, moderate risk class forms 5,24% and 34,05% of the area is low risk.