IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v2y2020i2p3-58d338921.html
   My bibliography  Save this article

A Comparison of the Qualitative Analytic Hierarchy Process and the Quantitative Frequency Ratio Techniques in Predicting Forest Fire-Prone Areas in Bhutan Using GIS

Author

Listed:
  • Kinley Tshering

    (Forest Fire Management Section, Department of Forests and Park Services, Thimphu 11001, Bhutan)

  • Phuntsho Thinley

    (Ugyen Wangchuck Institute for Conservation and Environment, Lamaigoenpa, Bumthang 32001, Bhutan
    Ecosystem Management, University of New England, Armidale NSW 2351, Australia)

  • Mahyat Shafapour Tehrany

    (Department of Geodesy, Kandilli Observatory and Earthquake Research Institute, Bogazici University, 34680 Cengelkoy-Istanbul, Turkey)

  • Ugyen Thinley

    (College of Natural Resources, Royal University of Bhutan, Lobesa, Punakha 13001, Bhutan)

  • Farzin Shabani

    (ARC Centre of Excellence for Australian Biodiversity and Heritage, Global Ecology, College of Science and Engineering, Flinders University, Adelaide, SA GPO Box 2100, Australia
    Department of Biological Sciences, Macquarie University, Sydney NSW 2109, Australia)

Abstract

Forest fire is an environmental disaster that poses immense threat to public safety, infrastructure, and biodiversity. Therefore, it is essential to have a rapid and robust method to produce reliable forest fire maps, especially in a data-poor country or region. In this study, the knowledge-based qualitative Analytic Hierarchy Process (AHP) and the statistical-based quantitative Frequency Ratio (FR) techniques were utilized to model forest fire-prone areas in the Himalayan Kingdom of Bhutan. Seven forest fire conditioning factors were used: land-use land cover, distance from human settlement, distance from road, distance from international border, aspect, elevation, and slope. The fire-prone maps generated by both models were validated using the Area Under Curve assessment method. The FR-based model yielded a fire-prone map with higher accuracy (87% success rate; 82% prediction rate) than the AHP-based model (71% success rate; 63% prediction rate). However, both the models showed almost similar extent of ‘very high’ prone areas in Bhutan, which corresponded to coniferous-dominated areas, lower elevations, steeper slopes, and areas close to human settlements, roads, and the southern international border. Moderate Resolution Imaging Spectroradiometer (MODIS) fire points were overlaid on the model generated maps to assess their reliability in predicting forest fires. They were found to be not reliable in Bhutan, as most of them overlapped with fire-prone classes, such as ‘moderate’, ‘low’, and ‘very low’. The fire-prone map derived from the FR model will assist Bhutan’s Department of Forests and Park Services to update its current National Forest Fire Management Strategy.

Suggested Citation

  • Kinley Tshering & Phuntsho Thinley & Mahyat Shafapour Tehrany & Ugyen Thinley & Farzin Shabani, 2020. "A Comparison of the Qualitative Analytic Hierarchy Process and the Quantitative Frequency Ratio Techniques in Predicting Forest Fire-Prone Areas in Bhutan Using GIS," Forecasting, MDPI, vol. 2(2), pages 1-23, March.
  • Handle: RePEc:gam:jforec:v:2:y:2020:i:2:p:3-58:d:338921
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/2/2/3/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2571-9394/2/2/3/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rodeano Roslee & Alvyn Clancey Mickey & Norbert Simon & Mohd. Norazman Norhisham, 2017. "Landslide susceptibility analysis lsa using weighted overlay method wom along the genting sempah to bentong highway pahang," Malaysian Journal of Geosciences (MJG), Zibeline International Publishing, vol. 1(2), pages 13-19, September.
    2. Hamid Pourghasemi & Biswajeet Pradhan & Candan Gokceoglu, 2012. "Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 63(2), pages 965-996, September.
    3. Gianluigi Busico & Elisabetta Giuditta & Nerantzis Kazakis & Nicolò Colombani, 2019. "A Hybrid GIS and AHP Approach for Modelling Actual and Future Forest Fire Risk Under Climate Change Accounting Water Resources Attenuation Role," Sustainability, MDPI, vol. 11(24), pages 1-20, December.
    4. Sturtevant, Brian R. & Scheller, Robert M. & Miranda, Brian R. & Shinneman, Douglas & Syphard, Alexandra, 2009. "Simulating dynamic and mixed-severity fire regimes: A process-based fire extension for LANDIS-II," Ecological Modelling, Elsevier, vol. 220(23), pages 3380-3393.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hazem Ghassan Abdo & Hussein Almohamad & Ahmed Abdullah Al Dughairi & Motirh Al-Mutiry, 2022. "GIS-Based Frequency Ratio and Analytic Hierarchy Process for Forest Fire Susceptibility Mapping in the Western Region of Syria," Sustainability, MDPI, vol. 14(8), pages 1-20, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Scheller, Robert & Kretchun, Alec & Hawbaker, Todd J. & Henne, Paul D., 2019. "A landscape model of variable social-ecological fire regimes," Ecological Modelling, Elsevier, vol. 401(C), pages 85-93.
    2. Txomin Bornaetxea & Juan Remondo & Jaime Bonachea & Pablo Valenzuela, 2023. "Exploring available landslide inventories for susceptibility analysis in Gipuzkoa province (Spain)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(3), pages 2513-2542, September.
    3. Gökhan Demir & Mustafa Aytekin & Aykut Akgün & Sabriye İkizler & Orhan Tatar, 2013. "A comparison of landslide susceptibility mapping of the eastern part of the North Anatolian Fault Zone (Turkey) by likelihood-frequency ratio and analytic hierarchy process methods," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(3), pages 1481-1506, February.
    4. Rui Yuan & Jing Chen, 2022. "A hybrid deep learning method for landslide susceptibility analysis with the application of InSAR data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 114(2), pages 1393-1426, November.
    5. Viet-Ha Nhu & Ataollah Shirzadi & Himan Shahabi & Sushant K. Singh & Nadhir Al-Ansari & John J. Clague & Abolfazl Jaafari & Wei Chen & Shaghayegh Miraki & Jie Dou & Chinh Luu & Krzysztof Górski & Binh, 2020. "Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms," IJERPH, MDPI, vol. 17(8), pages 1-30, April.
    6. Sadhan Malik & Subodh Chandra Pal & Biswajit Das & Rabin Chakrabortty, 2020. "Assessment of vegetation status of Sali River basin, a tributary of Damodar River in Bankura District, West Bengal, using satellite data," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(6), pages 5651-5685, August.
    7. Kibeom Kwon & Minkyu Kang & Dongku Kim & Hangseok Choi, 2023. "Prioritization of Hazardous Zones Using an Advanced Risk Management Model Combining the Analytic Hierarchy Process and Fuzzy Set Theory," Sustainability, MDPI, vol. 15(15), pages 1-15, August.
    8. Gianluigi Busico & Maria Margarita Ntona & Sílvia C. P. Carvalho & Olga Patrikaki & Konstantinos Voudouris & Nerantzis Kazakis, 2021. "Simulating Future Groundwater Recharge in Coastal and Inland Catchments," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3617-3632, September.
    9. Nzotcha, Urbain & Kenfack, Joseph & Blanche Manjia, Marceline, 2019. "Integrated multi-criteria decision making methodology for pumped hydro-energy storage plant site selection from a sustainable development perspective with an application," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 930-947.
    10. Majid Mohammady & Hamid Reza Pourghasemi & Mojtaba Amiri, 2019. "Assessment of land subsidence susceptibility in Semnan plain (Iran): a comparison of support vector machine and weights of evidence data mining algorithms," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 99(2), pages 951-971, November.
    11. Viet-Tien Nguyen & Trong Hien Tran & Ngoc Anh Ha & Van Liem Ngo & Al-Ansari Nadhir & Van Phong Tran & Huu Duy Nguyen & Malek M. A. & Ata Amini & Indra Prakash & Lanh Si Ho & Binh Thai Pham, 2019. "GIS Based Novel Hybrid Computational Intelligence Models for Mapping Landslide Susceptibility: A Case Study at Da Lat City, Vietnam," Sustainability, MDPI, vol. 11(24), pages 1-24, December.
    12. Neshat, Aminreza & Pradhan, Biswajeet & Dadras, Mohsen, 2014. "Groundwater vulnerability assessment using an improved DRASTIC method in GIS," Resources, Conservation & Recycling, Elsevier, vol. 86(C), pages 74-86.
    13. Conlisk, Erin & Syphard, Alexandra D. & Franklin, Janet & Regan, Helen M., 2015. "Predicting the impact of fire on a vulnerable multi-species community using a dynamic vegetation model," Ecological Modelling, Elsevier, vol. 301(C), pages 27-39.
    14. Netra Bhandary & Ranjan Dahal & Manita Timilsina & Ryuichi Yatabe, 2013. "Rainfall event-based landslide susceptibility zonation mapping," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 69(1), pages 365-388, October.
    15. Wenqun Xiu & Shuying Wang & Wenguang Qi & Xue Li & Chisheng Wang, 2021. "Disaster Chain Analysis of Landfill Landslide: Scenario Simulation and Chain-Cutting Modeling," Sustainability, MDPI, vol. 13(9), pages 1-22, April.
    16. Di Wang & Mengmeng Hao & Shuai Chen & Ze Meng & Dong Jiang & Fangyu Ding, 2021. "Assessment of landslide susceptibility and risk factors in China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(3), pages 3045-3059, September.
    17. Rajesh Khatakho & Dipendra Gautam & Komal Raj Aryal & Vishnu Prasad Pandey & Rajesh Rupakhety & Suraj Lamichhane & Yi-Chung Liu & Khameis Abdouli & Rocky Talchabhadel & Bhesh Raj Thapa & Rabindra Adhi, 2021. "Multi-Hazard Risk Assessment of Kathmandu Valley, Nepal," Sustainability, MDPI, vol. 13(10), pages 1-27, May.
    18. Aihua Wei & Duo Li & Yahong Zhou & Qinghai Deng & Liangdong Yan, 2021. "A novel combination approach for karst collapse susceptibility assessment using the analytic hierarchy process, catastrophe, and entropy model," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 105(1), pages 405-430, January.
    19. Amin Salehpour Jam & Jamal Mosaffaie & Faramarz Sarfaraz & Samad Shadfar & Rouhangiz Akhtari, 2021. "GIS-based landslide susceptibility mapping using hybrid MCDM models," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 108(1), pages 1025-1046, August.
    20. Abdessamed Derdour & Abderrazak Bouanani & Noureddine Kaid & Kanit Mukdasai & A. M. Algelany & Hijaz Ahmad & Younes Menni & Houari Ameur, 2022. "Groundwater Potentiality Assessment of Ain Sefra Region in Upper Wadi Namous Basin, Algeria Using Integrated Geospatial Approaches," Sustainability, MDPI, vol. 14(8), pages 1-20, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jforec:v:2:y:2020:i:2:p:3-58:d:338921. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.