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Fire risk evaluation using multicriteria analysis—a case study

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Abstract

Forest fires are one of the major causes of ecological disturbance and environmental concerns in tropical deciduous forests of south India. In this study, we use fuzzy set theory integrated with decision-making algorithm in a Geographic Information Systems (GIS) framework to map forest fire risk. Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy) and consists of a rule base, membership functions, and an inference procedure. We used satellite remote sensing datasets in conjunction with topographic, vegetation, climate, and socioeconomic datasets to infer the causative factors of fires. Spatial-level data on these biophysical and socioeconomic parameters have been aggregated at the district level and have been organized in a GIS framework. A participatory multicriteria decision-making approach involving Analytical Hierarchy Process has been designed to arrive at a decision matrix that identified the important causative factors of fires. These expert judgments were then integrated using spatial fuzzy decision-making algorithm to map the forest fire risk. Results from this study were quite useful in identifying potential “hotspots” of fire risk, where forest fire protection measures can be taken in advance. Further, this study also demonstrates the potential of multicriteria analysis integrated with GIS as an effective tool in assessing “where and when” forest fires will most likely occur.

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References

  • Ahmed, T. R. N., Rao, K. G., & Murthy, J. S. R. (2000). GIS based fuzzy membership model for cropland suitability analysis. Agricultural Systems, 63, 75–95. doi:10.1016/S0308-521X(99)00036-0.

    Article  Google Scholar 

  • Albini, F. A. (1976). Estimating wildfire behavior and effects. Gen.Tech.Rep.INT.30 (92 p). Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station.

  • Allgower, B., Carlson, J. D., & Wagtendonk, J. W. V. (2003). Introduction to fire danger rating and remote sensing—will remote sensing enhance wildland fire danger rating? In E. Chuvieco (Eds.), Wild land fire danger estimation and mapping. The role of remote sensing data. New Jersey: World Scientific.

    Google Scholar 

  • Andreae, M. O., & Merlet, P. (2001). Emission of trace gases and aerosols from biomass burning. Global Biogeochemical Cycles, 15, 955–966. doi:10.1029/2000GB001382.

    Article  CAS  Google Scholar 

  • Bellman, R. E., & Zadeh, L. A. (1970). Decision making in a fuzzy environment. Management Science, 17, 141–164. doi:10.1287/mnsc.17.4.B141.

    Article  Google Scholar 

  • Beven, K., & Kirby, M. J. (1979). A physically based variable contributing area model of basin Hydrology. Hydrological Sciences Bulletin, 24, 303–325.

    Article  Google Scholar 

  • Bhatt, B. P., & Todaria, N. P. (1992). Fuel wood characteristics of some Indian mountain species. Forest Ecology and Management, 47, 363–366. doi:10.1016/0378-1127(92)90285-H.

    Article  Google Scholar 

  • Bonazountas, M., Kallidromitou, D., Kassomenos, P. A., & Passas, N. (2005). Fire risk analysis. Human and Ecological Risk Assessment, 11, 617–626. doi:10.1080/10807030590949717.

    Article  Google Scholar 

  • Brauer, M. (1999). Health impacts of biomass air pollution. In K. T. Goh, D. Schwela, J. G. Goldammar, & O. Simpson (Eds.), Health guidelines for vegetation fire events—background papers (pp. 189–254). Singapore: WHO.

    Google Scholar 

  • Burrough, P. A., MacMillan, R. A., & Deursen, W. (1992). Fuzzy classification methods for determining land suitability from soil profile observations. Journal of Soil Science, 43, 193–210. doi:10.1111/j.1365-2389.1992.tb00129.x.

    Article  Google Scholar 

  • Canadian Forest Service (1997). A wildfire threat rating system for the MacGregor Model Forest. Fianl Report MMF Practices-3015, Canada.

  • Charnpratheep, K., Zhou, O., & Garner, B. (1997). Preliminary landfill site screening using fuzzy geographical information systems. Waste Management & Research, 15, 197–215.

    CAS  Google Scholar 

  • Chen, S. J., & Hwang, C. L. (1992). Fuzzy multiple attribute decision-making. Berlin: Springer.

    Google Scholar 

  • Chuvieco, E., & Congalton, R. G. (1989). Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sensing of Environment, 29, 147–159. doi:10.1016/0034-4257(89)90023-0.

    Article  Google Scholar 

  • Chuvieco, E., Allgower, B., & Salas, J. (2003a). Integration of physical and human factors in fire danger assessment. In E. Chuvieco (Ed.), Wild land fire danger estimation and mapping. The role of remote sensing data. New Jersey: World Scientific.

    Google Scholar 

  • Chuvieco, E., Agaudo, I., Cocero, D., & Riano, D. (2003b). Design of an empirical index to estimate fuel moisture content from NOAA-AVHRR analysis in forest fire danger studies. International Journal of Remote Sensing, 24, 1621–1637. doi:10.1080/01431160210144660.

    Article  Google Scholar 

  • Covington, W. W., & Moore, M. M. (1994). South-western ponderosa pine forest structure: Changes since Euro-American settlement. Journal of Forestry, 92, 39–47.

    Google Scholar 

  • D’Ercole, C., Groves, D. I., & Knox-Robinson, M. (2000). Using fuzzy logic in a GIS environment to enhance conceptually based prospectivity analysis of Mississipi Valley-Type mineralization. Australian Journal of Earth Sciences, 47, 913–927. doi:10.1046/j.1440-0952.2000.00821.x.

    Google Scholar 

  • Dixon, B. (2005). Groundwater vulnerability mapping: A GIS and fuzzy rule based integrated tool. Applied Geography (Sevenoaks, England), 25, 327–347. doi:10.1016/j.apgeog.2005.07.002.

    Article  Google Scholar 

  • Dubois, D., & Prade, M. (1979). Fuzzy Sets and Systems: Theory and Applications. New York: Academic Press.

    Google Scholar 

  • Eastman, J. R., & Jiang, H. (1995). Fuzzy measures in multicriteria evaluation. In Proceedings, second international symposium on spatial accuracy assessment in natural resources and environmental studies, May 21–23 (pp. 527–534). Fort Collins, Colorado.

  • Eastman, J. R., Jin, W., Kyem, P. A. K., & Toledano, J. (1995). Raster procedures for multi-criteria/ multi-objective decisions. Photogrammetric Engineering and Remote Sensing, 61, 539–547.

    Google Scholar 

  • FAO (Food and Agricultural Organization) (1986). Wild land fire management terminology. Report number 70.FAO Forestry Paper, Roma.M-99. ISBN 92-5-0024207.

  • Fishburn, P. C. (1967). Additive utilities with incomplete product set: Applications to priorities and assignments. Baltimore, MD, USA: Operations research society of America (ORSA).

  • Giupponi, C., Mysiak, J., Fassio, A., & Cogan, V. (2004). MULINODSS: A computer tool for sustainable use of water resources at the catchment scale. Mathematics and Computers in Simulation, 64, 13–24. doi:10.1016/j.matcom.2003.07.003.

    Article  Google Scholar 

  • Goldammer, J. G. (1999). Forests on fire. Science, 284, 1782–1783. doi:10.1126/science.284.5421.1782a.

    Article  Google Scholar 

  • Goncalves, Z. J., & Lourenco, L. (1990). Meteorological index of forest fire risk in the Portuguese mainland territory. In Proceedings of the international conference on forest fire research (B07, pp. 1–14). Coimbra.

  • Gregoire, J. M., Tansey, K., & Silva, J. M. N. (2003). The GBA, 2000 initiative: Developing a global burned area database from SPOT-Vegetation imagery. International Journal of Remote Sensing, 24, 1369–1376. doi:10.1080/0143116021000044850.

    Article  Google Scholar 

  • Hardy, C. C., Ottmar, R. D., Peterson, J. L., Core, J. E., & Seamon, P. (Eds.) (2001). Smoke management guide for prescribed and wild land fire: 2001 edition PMS 964 420-2. NFES 1279. Boise, ID: National Wildfire Coordination Group (226 p).

  • Hearn, P., Jr., Hare, T., Schruben, P., Sherrill, D., LaMar, C., & Tsushima, P. (2001). Global GIS database: digital atlas of South Asia. US: Geological Survey. Digital Data Series DDS-62-C.

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

    Article  Google Scholar 

  • Heywood, I., Oliver, J., & Tomlinson, S. (1995). Building an exploratory multi-criteria modeling environment for spatial decision support. In P. Fisher (Ed.), Innovations in GIS 2 (pp. 127–136). London: Taylor and Francis.

    Google Scholar 

  • Hill, M. J., Braaten, R., Veitch, S. M., Lees, B. G., & Sharma, S. (2005). Multi-criteria decision analysis in spatial decision support: The ASSESS analytic hierarchy process and the role of quantitative methods and spatially explicit analysis. Environmental Modelling & Software, 20, 955–976. doi:10.1016/j.envsoft.2004.04.014.

    Article  Google Scholar 

  • Janssen, R., & Herwijnen van, M. (1994). DEFINITE. A system to support decisions on a FINITE set of alternatives. User Manual (219 pp). Dordrecht: Kluwer Academic.

    Google Scholar 

  • Kahraman, C., Ruan, D., & Dogan, I. (2003). Fuzzy group decision-making for facility location selection. Information Sciences, 157, 135–153. doi:10.1016/S0020-0255(03)00183-X.

    Article  Google Scholar 

  • Kangas, J., Store, R., Leskinen, P., & Mehta"talo, L. (2000). Improving the quality of landscape ecological forest planning by utilizing advanced decision-support tools. Forest Ecology and Management, 132, 157–171. doi:10.1016/S0378-1127(99)00221-2.

    Article  Google Scholar 

  • Karacapilidis, N., & Pappis, C. (2000). Computer supported collaborative argumentation and fuzzy similarity measures in multiple-criteria decision making. Computers & Operations Research, 27, 653–671. doi:10.1016/S0305-0548(99)00111-2.

    Article  Google Scholar 

  • Kazana, V., Fawcett, R. H., & Mutch, W. E. S. (2003). A decision support modelling framework for multiple use forest management: The Queen Elizabeth Forest case study in Scotland. European Journal of Operational Research, 148, 102–115. doi:10.1016/S0377-2217(02)00348-X.

    Article  Google Scholar 

  • Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives: Preferences and value tradeoffs. New York: Wiley.

    Google Scholar 

  • Kushla, J. D., & Ripple, W. J. (1997). The role of terrain in a fire mosaic of a temperate coniferous forest. Forest Ecology and Management, 95, 97–107. doi:10.1016/S0378-1127(97)82929-5.

    Article  Google Scholar 

  • Lark, R. M., & Bolam, H. C. (1997). Uncertainty in prediction and interpretation of spatially variable data on soils. Geoderma, 77, 263–282. doi:10.1016/S0016-7061(97)00025-6.

    Article  Google Scholar 

  • Lentile, L. B., Holden, Z. A., Smith, A. M. S., Falkowski, M. J., Hudak, A. T., Morgan, P., et al. (2006). Remote sensing techniques to assess active fire characteristics and post-fire effects. International Journal of Wildland Fire, 15, 319–345. doi:10.1071/WF05097.

    Article  Google Scholar 

  • Leung, L. C., & Cao, D. (2001). On the efficacy of modeling multi-attribute decision making problem using AHP and Sinarchy. 2001. European Journal of Operational Research, 132(1), 39–49. doi:10.1016/S0377-2217(00)00111-9.

    Article  Google Scholar 

  • Malczewski, J. (1999). GIS and multicriteria decision analysis. New York: Wiley.

    Google Scholar 

  • Malczewski, J. (2002). Fuzzy screening for land suitability analysis. Geographical and Environmental Modelling, 6, 27–39. doi:10.1080/13615930220127279.

    Article  Google Scholar 

  • Malins, D., & Metternicht, G. (2006). Assessing the spatial extent of dryland salinity through fuzzy modeling. Ecological Modelling, 193, 387–411. doi:10.1016/j.ecolmodel.2005.08.044.

    Article  Google Scholar 

  • Maselli, F., Romanelli, S., Bottai, L., & Zipoli, G. (2003). Use of NOAA-AVHRR NDVI images for the estimation of dynamic fire risk in Mediterranean areas. Remote Sensing of Environment, 86, 187–197. doi:10.1016/S0034-4257(03)00099-3.

    Article  Google Scholar 

  • Mazzetto, F., & Bonera, R. (2003). MEACROS: A tool for multi-criteria evaluation of alternative cropping systems. European Journal of Agronomy, 18, 379–387. doi:10.1016/S1161-0301(02)00127-2.

    Article  Google Scholar 

  • Merril, D. F., & Alexander, M. E. (Eds.). (1987). Glossary of forest fire management terms (4th ed.). Ottawa, Ontario: National Research Council of Canada. Canadian Committee on Forest Fire management. Publication NRCC No.26516.

  • Mitra, B., Scott, H. D., Dixonand, J. C., & McKimmey, J. M. (1998). Applications of fuzzy logic to the prediction of soil erosion in a large watershed. Geoderma, 86, 183–209. doi:10.1016/S0016-7061(98)00050-0.

    Article  Google Scholar 

  • Moore, I. D., Grayson, R. B., & Ladson, A. R. (1991). Terrain based catchment partitioning and runoff prediction using vector elevation data. Water Resources Research, 27, 1177–1191. doi:10.1029/91WR00090.

    Article  Google Scholar 

  • Morgan, P., Hardy, C. C., Swetnam, T., Rollins, M. G., & Long, L. G. (2001). Mapping fire regimes across time and space: Understanding coarse and fine-scale fire patterns. International Journal of Wildland Fire, 10, 329–342. doi:10.1071/WF01032.

    Article  Google Scholar 

  • Nadeau, L. B., & Englefield, P. (2006). Fine-resolution mapping of wildfire fuel types for Canada: Fuzzy logic modeling for an Alberta pilot area. Environmental Monitoring and Assessment, 120, 127–152. doi:10.1007/s10661-005-9053-0.

    Article  CAS  Google Scholar 

  • Opperhuizen, A., & Hutzinger, D. (1982). Multi-criteria analysis and risk assessment. Chemosphere, 11, 675–678. doi:10.1016/0045-6535(82)90178-3.

    Article  CAS  Google Scholar 

  • Perry, D. A. (1998). The scientific basis of forestry. Annual Review of Ecology and Systematics, 29, 435–466. doi:10.1146/annurev.ecolsys.29.1.435.

    Article  Google Scholar 

  • Pomerol, J. C., & Barba-Romero, S. (2000). Multicriterion decision in management: Principles and management. Boston: Kluwer Academic.

    Google Scholar 

  • Ramanathan, R. (2001). A note on the use of the analytic hierarchy process for environmental impact assessment. Journal of Environmental Management, 63, 27–35. doi:10.1006/jema.2001.0455.

    Article  CAS  Google Scholar 

  • Rashed, T., & Weeks, J. (2003). Assessing vulnerability to earthquake hazards through spatial multi-criteria analysis of urban areas. International Journal of GIS, 17, 547–576.

    Google Scholar 

  • Ren, F. (1997). A training model for GIS application in land resource allocation. ISPRS Journal of Photogrammetry and Remote Sensing, 52, 261–265. doi:10.1016/S0924-2716(97)00021-X.

    Article  Google Scholar 

  • Robinson, V. (2003). A perspective on the fundamentals of Fuzzy sets and their use in geographic information systems. Transactions in GIS, 7, 3–30. doi:10.1111/1467-9671.00127.

    Article  Google Scholar 

  • Rothermel, R. C. (1983). How to predict the spread and intensity of forest and Range fires (40 p). Gen.Tech.Rep.INT-143. USDA Forest Service. Intermountain Forest and Range Experiment Station.

  • Roy, B. (1991). The outranking approach and the foundations of ELECTRE methods. Theory and Decision, 31, 49–73. doi:10.1007/BF00134132.

    Article  Google Scholar 

  • Saaty, T. L. (1980). The analytic hierarchy process: Planning, priority setting and resource allocation. New York: McGraw-Hill.

    Google Scholar 

  • Saaty, T. L. (1994). Highlights and critical points in the theory and application of the analytic hierarchy process. European Journal of Operational Research, 74, 426–447. doi:10.1016/0377-2217(94)90222-4.

    Article  Google Scholar 

  • Saaty, T. L. (2000). Fundamentals of decision making and priority theory with the analytic hierarchy process. Pittsburg: RWS Publications.

    Google Scholar 

  • Saaty, T. L., & Vargas, L. G. (1993). Experiments on rank preservation and reversal in relative measurement. Mathematical and Computer Modelling, 17, 13–18. doi:10.1016/0895-7177(93)90171-T.

    Article  Google Scholar 

  • Sadiq, R., & Husain, T. (2005). A fuzzy-based methodology for aggregative environmental risk assessment: A case study of drilling waste. Environmental Modelling & Software, 20, 33–46. doi:10.1016/j.envsoft.2003.12.007.

    Article  Google Scholar 

  • Sasikala, K. R., & Petrou, M. (2001). Generalized fuzzy aggregation in estimating the risk of desertification of a burned forest. Fuzzy Sets and Systems, 118, 121–137. doi:10.1016/S0165-0114(99)00064-0.

    Article  Google Scholar 

  • Sicat, R. S., Carranza, E. J. M., & Nidumolu, U. B. (2005). Fuzzy modeling of farmer’s knowledge for land suitability classification. Agricultural Systems, 83, 49–95. doi:10.1016/j.agsy.2004.03.002.

    Article  Google Scholar 

  • Stolle, F., & Lambin, E. F. (2003). Interprovincial and interannual differences in the causes of land-use fires in Sumatra, Indonesia. Environmental Conservation, 30(4), 375–387. doi:10.1017/S0376892903000390.

    Article  Google Scholar 

  • Tangestani, M. H. (2004). Landslide susceptibility mapping using the fuzzy gamma approach in a GIS, Kakan catchment area, southwest Iran. Australian Journal of Earth Sciences, 51, 439–450. doi:10.1111/j.1400-0952.2004.01068.x.

    Article  Google Scholar 

  • Tansey, K., Gregoire, J. M., Binaghi, L. E., et al. (2004). A global inventory of burned areas at 1 km resolution for the year 2000 derived from SPOT vegetation data. Climatic Change, 67, 345–377. doi:10.1007/s10584-004-2800-3.

    Article  CAS  Google Scholar 

  • Taylor, S. W., & Alexander, M. E. (2006). Science, technology, and human factors in fire danger rating: The Canadian experience. International Journal of Wildland Fire, 15, 121–135. doi:10.1071/WF05021.

    Article  Google Scholar 

  • Triantaphyllou, E., & Lin, C. T. (1996). Development and evaluation of five fuzzy multi-attribute decision making methods. International Journal of Approximate Reasoning, 14, 281–310.

    Article  Google Scholar 

  • Van Wagner, C. E. (1993). Prediction of crown fire behavior in two stands of Jack Pine. Canadian Journal of Forest Research, 18, 818–820.

    Google Scholar 

  • Veitch, S. M., & Bowyer, J. K. (1996). ASSESS: A system for selecting suitable sites. In S. Morain, & S. Lopez Baros (Eds.), Raster imagery in geographic information systems (pp. 495). Santa Fe: One Word Press.

    Google Scholar 

  • Wang, Y. M., Elhag, T. M. S., & Hau, Z. S. (2006). A modified fuzzy logarithmic least squares method for fuzzy analytic hierarchy process. Fuzzy Sets and Systems, 157(23), 3055–3071.

    Article  Google Scholar 

  • Wu, C. R., Lin, C. T., & Chen, H. C. (2007). Optimal selection of location for Taiwanese hospitals to ensure a competitive advantage by using the analytic hierarchy process and sensitivity analysis. Building and Environment, 42, 1431–1444.

    Article  Google Scholar 

  • Yanar, T. A., & Akyurek, A. (2006). The enhancement of the cell-based GIS analyses with fuzzy processing capabilities. Information Sciences, 176, 1067–1085. doi:10.1016/j.ins.2005.02.006.

    Article  Google Scholar 

  • Yen, J. (1999). Fuzzy logic-a modern perspective. IEEE Transactions on Knowledge and Data Engineering, 11, 153–165. doi:10.1109/69.755624.

    Article  Google Scholar 

  • Ying, M. W., Liu, J., & Elhag, T. M. S. (2008). An integrated AHP-DEA methodology for bridge risk assessment. Computers and Industrial Engineering, 54(3), 513–525.

    Article  Google Scholar 

  • Zadeh, L. A. (1965). Fuzzy sets. Information and Content, 8, 338–356. doi:10.1016/S0019-9958(65)90241-X.

    Article  Google Scholar 

  • Zeng, T. Q., & Zhou, Q. (2001). Optimal spatial decision making using GIS: A prototype of a real estate geographical information system (REGIS). International Journal of Geographical Information Science, 15, 307–321. doi:10.1080/136588101300304034.

    Article  Google Scholar 

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  1. Agroecosystem Management Program, Ohio Agricultural Research and Development Center (OARDC), The Ohio State University, 201 Thorne Hall, 1680 Madison Avenue, Wooster, OH, 44691, USA

    Krishna Prasad Vadrevu

  2. Department of Mathematics, Kent State University, Kent, OH, USA

    Anuradha Eaturu

  3. Department of Space-Government of India, Atmospheric Science Section, National Remote Sensing Center, Balanagar, India

    K. V. S. Badarinath

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  1. Krishna Prasad Vadrevu
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Vadrevu, K.P., Eaturu, A. & Badarinath, K.V.S. Fire risk evaluation using multicriteria analysis—a case study. Environ Monit Assess 166, 223–239 (2010). https://doi.org/10.1007/s10661-009-0997-3

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