Evaluating Models and Effective Factors Obtained from Remote Sensing (RS) and Geographic Information System (GIS) in the Prediction of Forest Fire Risk, Structured Review

Akram Karimi, Sara Abdollahi, Kaveh Ostad-Ali-Askari, Vijay P. Singh, Saeid Eslamian, Ali Heidarian, Mohsen Nekooei, Hossein Gholami, Sona Pazdar

Abstract


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.

 


Keywords


Modeling; Risk Prediction; Fire; Fire Risk Modeling; Remote Sensing; Geographic Information System

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References


Adab H, Kanniah D, Solaimani K. GIS-based Probability Assessment of Fire Risk in Grassland and Forested Landscapes of Golestan Province, Iran, International Conference on Environmental and Computer Science IPCBEE. 19(2011) © (2011) IACSIT Press. https://www.researchgate.net/profile/Hamed_Adab

AdhamiMojaddar MH, Mousavir A, Honardoost F. Fire hazard zonation using GIS, AHP (case study: Caspian forests of northern Iran-Golestan province) 2012; 11: 52: 25-43 (In Persian).

Aghajani H, Fallah A, FazlollahEmadian S. Modelling and analyzing the surface fire behavior in Hyrcanian forest of Iran, Journal of Forest Science 2014; 60 (9): 353–362. www.agriculturejournals.cz/publicFiles/133156.pdf

Alonso-Betanzos A, Fontenla-Romero O, Guijarro-Berdiñas B, et al. An intelligent system for forest fire risk prediction and firefighting management in Galicia. Expert Systems with Applications 2003; 25 (4): 545-554.https://doi.org/10.1016/S0957-4174(03)00095-2

Anh Tuan, TRAN1, Ngoc Dat DINH1, DanhThanhHai, NGUYEN2 and Vivarad PHONEKEO37

Vazquez A, Moreno JM. Spatial distribution of forest fires in Sierra de Credos (Central Spain). Forest Ecology and M (2009). Fire risk evaluation using multicriteria analysis, a case study. Journal of Environment Monitoring Assessment 2001; 166: 223-239. https://doi.org/10.1016/S0378-1127(00)00436-9

Ardakani S, Voldazoj M, Mohamadzade A, et al. Spectroscopic Characterization of Fire and Field Objectives for Identification and Separation in Remote Sensing Data. PhD thesis Khaje-Naseerdin-Toosi University of Technology, Geo-geomechanics faculty, 2010.

Ardakani A, ValadaneZoj MJ, Mansourian A. 2009; The spatial analysis of fire across the country using RS and GIS. Journal of (In Persian) http://journals.msrt.ir/files/site1/rds_journals/911/article-911-444129.pdf

Behzadfar M, Vahid H. Fire risk zonation in forest areas of North Khorasan province. 2011; 10 p (In Persian) https://www.researchgate.net

Bernabeu P, Vergara L, Bosh I, et al. A prediction/detection scheme for automatic forest fire surveillance. Digital Signal Processing 2004; 14(5): 481-507. DOI: 10.1016/j.dsp.2004.06.003

BeygiHeydarhoo H, BanejShafiei A, Erfanian M. Evaluation of Fuzzy Linear Linear Combination Method in Forest Fire Risk Mapping (Case Study: Sardasht Forest, West Azarbaijan), 1393. Journal of Science and Technology of Wood and Forest 2014; 22(3): 2015. http://jwfst.gau.ac.ir

Boonchut P. Decision support for hazardous material routing, international institute for geo information science and earth observation (ITC) enschedethe netherlands MSc. Thesis March 2005. https://www.itc.nl/library/papers_2005/msc/upla/boonchut.pdf

Burgan RE, Klaver RW, Klaver JM. Fuel models and fire potential from satellite and surface observations. International Journal of Wildland Fire 1998; 8: 159–170. https://doi.org/10.1071/WF9980159

Canadian Forest Service. A wildfire threat rating system for the Mac GregorModelForest, Final Report MMF Practices-3015. Canada 1997; 231p. http://www.nrcan.gc.ca/forests/fire-insects-disturbances/fire/14470

Hoersch B, Braun G, Schmidt U. Relation between landform and vegetation in alpine regions of Wallis, Switzerland. A multiscale remote sensing and GIS approach. Computers, Environment and Urban Systems 2002; 26(2-3): 113-139. http://elib.dlr.de/376/

Chen W, Sakai K, Moriya L, et al. Estimation of vegetation in semi-arid sandy land based on multivariate statistical modeling using remote sensing data. Environmental Modeling & Assessment 2013; 18(5): 547-558.DOI: 10.1007/s10666-013-9359-1

Chuvieco E, Aguado I, Yebra M, et al. Development of a framework for fire risk assessment using remote sensing and geographic information system technologies. Ecological Modelling 2010; 221(1): 46–58. https://doi.org/10.1016/j.ecolmodel.2008.11.017

Chuvieco E, Congalton RG. Application of remote sensing and geographic information system to forest fire hazard mapping. Remote Sensing of Environment 1989; 29 (2): 147–159. https://doi.org/10.1016/0034-4257(89)90023-0

Chuvieco E, Agaudo I, Cocero D, et al. Design of an empirical index to estimate fuel moisturecontent from NOAA-AVHRR analysis in forest fire danger studies. International Journal of Remote Sensing 2003; 24 (8): 1621–1637.DOI:10.1080/01431160210144660.

Chuvieco E, Sandow Ch, Günther KP, et al. Global burned area mapping from European satellites: The ESA FIRE-CCI project. ISPRS Journal of Photogrammetry and Remote Sensing · July 2012. DOI: 10.5194/isprsarchives-XXXIX-B8-13-2012

Chuvieco E, Salas J. Mapping the spatial distribution of forest fire danger using GIS. International Journal of Geographic Information Systems 1996; 10(3): 333–345. https://doi.org/10.1080/02693799608902082

Chuvieco E, Congalton RG. Application of remote sensing and geographic information system to forest fire hazard mapping. Remote Sensing of Environment1989; 29 (2): 147–159. https://doi.org/10.1016/0034-4257(89)90023-0

Chuvieco E, Aguado I, Jurdao S, et al. Integrating geospatial information into fire risk assessment. International Journal of Wildland Fire 2012; 23(5): 606-619. DOI: 10.1071/WF12052

Darvishi L, Ghods-Khah M, Gholami V. Presentation of the regional model for the zoning of fire hazard in the forests of Dorood (Case study of Baabahr area). Journal of Research and Research on Protecting and Protecting Iranian forests and Rangelands 2013; 11(1): 10-20. DOI: http://dx.doi.org/10.22092/ijfrpr.2013.106396

Dong X, Li-Min D, Guo-fan S, et al. Forest fire risk zone mapping from 2005 satellite images and GIS for Baihe Forestry Bureau, Jilin, China. Journal of Forestry Research 2005; 16 (3): 169-174. https://doi.org/10.1007/BF02856809. FAO. International Forest Fire News. 1995; 16.

Fearnside PM. Deforestation in Brazilian Amazonia: History, rates, and consequences. Conservation Biology 2005; 19 (3): 680-688. DOI: 10.1111/j.1523-1739.2005. 00697.x

HajiMohammadi H, Bazajeed M, Qalahiri F, et al. Investigate the structure of the fire during the fire at Geospatial Space Magazine Quarterly Journal of Golestan University (In Persian), 2015. http://gps.gu.ac.ir/article_54249.html

Hernandez-Leal PA, Arbelo M, Gonzalez-Calvo A. Fire risk assessment using satellite data. Advances in Space Research 2006; 37(4): 741–746. DOI: 10.1016/j.asr.2004.12.053.

Huyen DThTh, Tuan VA. Applying GIS and multi criteria evaluation in forest fire risk zoning in son la province, Vietnam. International Symposium on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences 2008; 6p. http://wgrass.media.osakacu.ac.jp/gisideas10/papers/8918d883b5c5b166ca47d6733c18.pdf

Jahdi R, Darvishsefat A, Etemad V. Predicting forest fire spread using fire behavior model (Case study: Malekroud Forest-Siahkal). Iranian Journal of Forest and Poplar Research 2013; 5 (4): 419-430. www.IJF_Volume 5_Issue 4_Pages 419-430.pdf

Jaiswal RK, Mukherjee S, Raju KD, et al. Forest fire risk zone mapping from satellite imagery and GIS. International Journal of Applied Earth Observation and Geoformation 2002; 4(1): 1–10. https://doi.org/10.1016/S0303-2434(02)00006-5

Riva J, Pe´rez-Cabello F, Lana-Renault N, et al. Mapping wildfire occurrence at regional scale. Remote Sensing of Environment 2004; 92 (3): 363- 369 https://doi.org/10.1016/j.rse.2004.06.013

Khanmohammadi M, Rahimi M, D Kartoolinezhad. Wildfires Risk Assessment of North-East Hyrcanyan Forests of Iran by using Keetch-Byram and Mc-Arthur Indices 2016; 14(1): 48-57. DOI: 10.22092/ijfrpr.2016.107641

Lozano FJ, Suárez-Seoane S, Kelly M, et al. A multi-scale approach for modeling fire occurrence probability using satellite data and classification trees: A case studying a mountainous Mediterranean region. Remote Sensing of Environment 2008; 112 (3): 708-719. https://doi.org/10.1016/j.rse.2007.06.006

Makia M, Ishiahra M, Tamura M. Estimation of leaf water status to monitor the risk of forest fires by using remotely sensed data. Remote Sensing of Environment 2004; 90 (4): 441-450. https://doi.org/10.1016/j.rse.2004.02.002.

Malik T, Rabbani GH, Farooq M. Forest Fire Risk Zonation Using Remote Sensing and GIS Technology in Kansrao Forest Range of Rajaji National Park, Uttarakhand, India 2013. http://technical.cloud- International Journal of Advanced Remote Sensing and GIS journals.com/index.php/IJARSG/article/view/Tech-56

Merino-de-Miguel S, Huesca M, González-Alonso F. Modis reflectanceand active fire data for burn mapping and assessment at regional level. Ecological Modelling 2010; 221(1): 67-74. https://doi.org/10.1016/j.ecolmodel.2009.09.015

Mohammadi F, PirBavaghar M, Shabanian N. Forest fire risk zone modeling using logistic regression and GIS: An Iranian case study. Small-scale Forestry 2014; 13(1): 117–125. DOI:10.1007/S11842-013-9244-4

Mohammadi F, Shabani N, Pourhashemi M, et al. Preparation of forest fire hazard map using GIS and AHP in part of Pave forest. Journal of Forest and Poplar Research 2010; 18(4):586-569 (In Persian). http://www.sid.ir/fa/journal/ViewPaper.aspx?id=138718

Mohammadi F, Shabanian N, Pourhashemi M, et al. Risk zone mapping of forest fire using GIS and AHP in a part of Pave forests Iranian Journal of Forest and Poplar Research 2010; 18(4): 569-586. (In Persian). http://www.sid.ir/FileServer/JF/71913894206

Mosavari A, adhami. Fire hazard zonation using GIS, AHP case study - Caspian forests of northern Iran-Golestan province 2012; 11 p. (In Persian).

Mohammadinejad M, Tavakoli M. Survey of fire status in oak and wild pistachio (PistaciaatlanticaDesf) forest zone of Lorestan province. The first National Conference on Oak and wild pistachio (PistaciaatlanticaDesf) in Zagros 1998; 76-77. (In Persian).

Mohammadi F. Preparation of forest fire hazard map using satellite imagery and GIS in a part of Paveh forest. Kurdistan Natural Resources Faculty 2009; 69 p. (In Persian(.

Nepstad Daniel C. 2007. The Amazon's Vicious Cycles: Drought and Fire in the Greenhouse (PDF). World Wide Fund for Nature (WWF International). Retrieved 9 July 2009.

Murta A, Bozer R. Estimation of the burned area in forest fires using computational intelligence techniques. Procedia Computer Science 2012; 12 (2012): 282-285. https://doi.org/10.1016/j.procs.2012.09.070

Margarita H, Javier L. Forest Fire Potential Index for Navarra Autonomic Community (Spain) at Wildfire 2007. https://www.researchgate.net/publication/242232087_Forest_Fire_Potential_Index_for_Navarra_Autonomic_Community_Spain

Miller DE, Hays CR. 1995. Missouri Drought Response Plan. Water Resource Report. 1995; 44: 52.

https://dnr.mo.gov/pubs/WR44.pdf

Nasiri M. Investigating the wood fire resistance of various species of Northern Forests (Case Study: Hardwood Roads). Journal of Forest and Poplar Researches of Iran 2012; 20(3): 505-5013. DOI:10.22092/ijfpr.2012.107456

Prasad Vadrevu K, Badarinath KVS, Anuradha E. Spatial patterns in vegetation fires in the Indian region. Environmental Monitoring and Assessment 2008; 147(1-3): 1–13. DOI: 10.1007/s10661-007-0092-6

Razavian F, Nikoumaram H, Hashem S. Forest Fire Risk Assessment Using Fuzzy Logic, Second Conference on Environmental Planning and Management (In Persian) 2012. https://www.civilica.com/Paper-ESPME02-ESPME02_061.html

Riva J, Pe´rez-Cabello F, Lana-Renault N, et al. Mapping wildfire occurrence at regional scale. Remote Sensing of Environment 2004; 92(3): 363-369. https://doi.org/10.1016/j.rse.2004.06.0226

Roy PS. Forest fire and degradation assessment using satellite remote sensing and geographic information system. National Remote Sensing Agency, Hyderabad, 500037, India, Satellite Remote Sensing and GIS Applications in Agricultural Meteorology 2003; 361-400 http://www.wamis.org/agm/pubs/agm8/Paper-18.pdf

SadeghiKaji H. Assessment of fire risk and probability in the natural lands of Chaharmahal-va-Bakhtiari province, master's thesis of Shahre-kord University 2011; 86 (In Persian).

Saxena A, Chandra S, Srivastava P. Geospatial modeling for forest fire risk zonation in Himalayas and Siwaliks, India. Remote Sensing and GIS Applications to Forest Fire Management, Fire Effects Assessment 2005; 133-137

Sharma E, Chettri N. ICIMOD’s trans boundary biodiversity managementinitiative in the Hindu Kush-Himalayas. Mountain Research and Development 2005; 25(3): 278–281. DOI:10.1659/02764741(2005)025[0278: ITBMII]2.0.

Sharma D, Hoa V, Cuong PV, et al. Forest Fire Risk Zonation for Jammu District forest division using Remote Sensing and GIS. 7th FIG Regional Conference, Spatial Data Serving People: Land Governance and the Environment – Building the Capacity. Hanoi, Vietnam 2009; 19-22 October, 1-12 www.fig.net/resources/.../fig.../ts05a_sharma_etal_3661.pdf

Shorfi S, Kamrani H, Shorfi A. Investigation and zoning of susceptible forest areas using RS and GIS, National Conference on Forests of Central Zagros (In Persian) 2012. http://pdfarchive.ir/pack-25/Do_24013900119.pdf

Sivakumar MVK, Roy PS, Harmsen K, et al. Satellite Remote Sensing and GIS Ap- plications in Agricultural Meteorology. Geneva 2, Switzerland: World Me-teorologicalOrganisation 2003; 361−400. http://www.wamis.org/agm/pubs/agm8/WMO-TD1182.pdf

Sowmya SV, Somashekar RK. Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India. Journal of Environmental Biology November 2010; 31(6): 969-974. PMID:21506484

Stolle, F., Chomitz, K.M., Lambin, E.F. and T.P, Tomich. 2003. Land use and vegetation fires in Jambi Province, Sumatra, Indonesia. Forest Ecology and Management, 179 (1-3): 277–292. https://doi.org/10.1016/S0378-1127(02)00547-9

Taylor SW, Alexander ME. Science, technology, and human factors in fire danger rating: The Canadian experience. International Journal of Wildland Fire 2006; 15: 121–135. DOI:10.1071/WF05021WF05021.

Tuan A, Tran, Dinh ND, et al. Forest fire risk mapping by using satellite imagery and GIS for QungNinhprovince, Vietnam. 2008; 6. https://www.researchgate.net/publication/26087177

Van Wagner CE. Seasonal variation in moisture content of Eastern Canadian Tree foliage and the possible effect on crown fires. Departmental Publication number 1204, Forestry Branch, Canada 1967; 227 –229. www.cfs.nrcan.gc.ca/bookstore_pdfs/24741.pdf

Yin h, kong FH, Li XZ. RS and GIS-based forest fire zone mapping in dahinggan mountains. Chinese Geographical Science 2004; 14 (3): 251-257. DOI: 10.1007/s11769-003-0055-y

Zarekar A, Kazemi-Zamani B, Ghorbani S, et al. Mapping Spatial Distribution of Forest Fire using MCDM and GIS (Case Study: Three Forest Zones in Guilan Province) Iranian Journal of Forest and Poplar Research, Summer 2013; 21(2). DOI: http://dx.doi.org/10.22092/ijfpr.2013.3854




DOI: http://dx.doi.org/10.24294/jgc.v1i4.618

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