Open AccessEvaluating Models and Effective Factors Obtained from Remote Sensing (RS) and Geographic Information System (GIS) in the Prediction of Forest Fire Risk, Structured Review
Author(s) -
Akram Karimi,
Sara Abdollahi,
Kaveh Ostad–Ali–Askari,
Vijay P. Singh,
Saeid Eslamian,
Ali Heidarian,
Mohsen Nekooei,
Hossein Gholami,
Sona Pazdar
Publication year - 2021
Publication title -
journal of geography and cartography
Language(s) - English
Resource type - Journals
ISSN - 2578-1979
DOI - 10.24294/jgc.v3i1.618
Subject(s) - analytic hierarchy process , geographic information system , zoning , computer science , remote sensing , environmental science , environmental resource management , data mining , risk analysis (engineering) , geography , civil engineering , operations research , engineering , business
Fire is a phenomenon occurs in most parts of the world and causes severe financial losses and sometimes, irreparable damages. Many parameters are involved in the occurrence of a fire; some of which are constant over time (at least in a fire cycle), but the others are dynamic and vary over time. Unlike the earthquake, the disturbance of fire depends on a set of physical, chemical, and biological relations. Monitoring the changes to predict the occurrence of fire is efficient in forest management.Method: In this research, the Persian and English databases were structurally searched using the keywords of fire risk modeling, fire risk, fire risk prediction, and remote sensing and the reviewed papers that reviewed predicted the fire risk in the field of Remote Sensing and Geographic Information System were retrieved. Then, the modeling and zoning data of fire risk prediction were extracted and analyzed in a descriptive manner. Accordingly, the study was conducted in 1995-2017. Findings: Fuzzy Analytic Hierarchy Process(AHP) zoning method was more practical among the applied methods and the plant moisture stress measurement was the most efficient among the remote sensing indices.Discussion and Conclusion: The findings of the study indicate that RS and GIS are an effective tool in the study of fire risk prediction.