Emergency Management

Emergency Management

Analysis of the potential fire hazard scenarios using GIS and RS: A case study of Lordegan forests

Document Type : Original Article

Authors
negar hamedi 1
ali esmaeily 2
Hassan faramarzi 3
1 student
2 Assistant Professor Remote Sensing Engineering, Graduate University of Advanced Technology, Kerman
3 Forestry Department faculty of natural resources tarbiat modres university. nour, mazandran, iran.
Abstract
Forest fire has many side effects on forest and land function. Among them are loss of biodiversity, reduction of economic
value of forests and climate change in large scale. The aim of this study was to evaluate the changes in forest fires
potentials. To achieve this goal, the required digital layers and data were obtained from the relevant websites and organizations
as well s through field surveys in the area of the study. After preparing the data by assuming the occurred fire, the
layers were entered in a fuzzy process using Analytical Network Process (ANP) and Ordered Weighted Average (OWA)
method. For this purpose, Zagros fire forests (Lordegan city) were examined using Landsat and MODIS images and considering
the factors affecting fire (topographical factors, human, climate and vegetation). In ANP procedure, the largest
weighs were assigned to the distance of residential areas from roads, GVMI index and maximum daily air temperature
factors in their magnitude order. OWA method was also used to create the hazard prediction model. Based on the results,
the fire hazard map was prepared in four classifications: very low, low, medium, and high. An accuracy assessment was
also performed using the relative operating characteristics. Of the six scenarios applied, the low-risk scenario and a small
compensation amount of ROC = 0.702 were evaluated as the best model to predict the risk of forest fires. Due the high
accuracy and precision of the model obtained, it can be used to contain the fires that will occur in the future.
Keywords
fire forest
remote sensing
ANP
OWA
ROC
1. Podur J, Martell D.L, Knight K. (2002). Statistical quality
control analysis of forest fire activity in Canada.
Canadian Journal Forest Research, 32, 195–205.
2. Somashekar R, Ravikumar P, Mohankumar C,Prakash
K, Nagaraja B. (2009). Burnt area mapping of Bandipur
National Park, India using IRS1C/1D LISS III
data, Journal of the Indian Society of Remote Sensing,
37, 37-50.
3. Chuvieco E, Congalton R.G. (1989). Application of
remote sensing and geographic information systems
to forest fire hazard mapping. Remote Sensing of the
Environment, 29, 147-159.
4. Zeng T, Hudson J, Kay S, Laginestra E. (2003). A fuzzy
GIS approach to fire risk assessment: a case study
of Sydney Olympic park, Australia, Spatial Sciences
Conferences, 1-20.
5. Erten E, Kurgun V, Musaolu N. (2005). Forest fire risk
zone mapping from satellite imagery and risk:A recent
case study from Mt, Carmel (Israel), Forest
Ecology and Management, 262, 1184–1188.
6. Sowmya S. V, Somashekar R. K. (2010). Application of
remote sensing and geographical information system
in mapping forest fire risk zone at Bhadra wildlife
sanctuary, India, Journal of Environmental Biology,
31(6), 969-974.
7. Lakshmi B. (2012). Wildfire hazard prediction: A Fuzzy
Model for Sensor Embedded Intelligence, Master of
Engineering, A thesis submitted to Auckland University
of Technology.
8. اسکندری، سعیده ) 1394 (. ارزیابی پتانسیل خطر آت شسوزی جنگل با
استفاده از مدل Dong . مجله ی آمایش جغرافیایی فضا، 15 ، 210 - 195 .
9. Eastman, J. R. (1988). Idrisi: A Geographic analysis system
for research applications, The Operational Geographer,
15,17-21.
10. Isaaks E. H, Srinivasta R. M. (1989). Applied
Geostatistics. Oxford University Press: Oxford.
11. Saaty T. (1980). The analytic hierarchy process: planning,
priority setting, resource allocation, New
York;London: McGraw-Hill International Book Co.
12. Saaty T. (2006). Decision making with the analytic
network process: economic, political, social and technological
applications with benefits, opportunities,
costs and risks,New York: Springer.
13. Garcia M, Javier F, Jeronimo A, Pablo A, Rocio P.
(2008). Farmland appraisal based on the analytic network
Process, Journal of Global Optimization, 42,143-
155.
14. Malczewski J. (2006). GIS-based multicriteria decision
analysis: a survey of the literature.International
Journal of Geographical Information Science, 20 (7),
703–726.
15. Jiang H, Eastman R.J. (2000). Application of fuzzy
measures in multi-criteriaevaluation inGIS.International
Journal of Geographical Information Systems, 14,
173–184.
16. Rinner C,Malczewski J. (2000). Web-enabled spatial
decision analysis using Ordered Weighted Averaging
(OWA).Journal of Geographical Systems,385-403.
17. Malczewski, J. (2006). Integrating multicriteria analysis
and geographic information systems: the ordered
weighted averaging (OWA) approach. International
Journal Environmental Technology and Management,
6(1/2): 7–19.
18. Rossiter, D.G., Loza, A. (2010). Analyzing land cover
change with logistic regression in R, Technical Report
ITC, Enschede, 71.
19. Gil Pontius R, Schneider L. (2001). Land-cover
change model validation by an ROC method for the
Ipswich watershed, Massachusetts, USA, Agriculture,
Ecosystems and Environment, 85,239–248.
20. Etter A, McAlpine C.,Wilson K, Phinn S, Possingham
H. (2006). Regional patterns of agricultural land use
and deforestation in Colombia, Agric. Ecosystems &
Environment, 114: 369-386.

  • Receive Date 09 December 2016
  • Revise Date 29 October 2018
  • Accept Date 17 December 2019