A Comparison of the Qualitative Analytic Hierarchy Process and the Quantitative Frequency Ratio Techniques in Predicting Forest Fire-Prone Areas in Bhutan Using GIS
Abstract
:1. Introduction
2. Methodology
2.1. Study Area Description
2.2. Forest Fire Modeling Flow Chart
2.3. Data Used
2.3.1. Forest Fire Inventory
2.3.2. Forest Fire Conditioning Factors
2.4. Modeling Forest Fire-Prone Areas
2.4.1. Analytic Hierarchy Process (AHP) Modeling
2.4.2. Frequency Ratio (FR) Modeling
- = frequency ratio of a class for the conditioning factor;
- = area of a class for the conditioning factor;
- = total area of the conditioning factor;
- = number of pixels in each class of the factor;
- = number of total pixels in the study area;
- = the percentage for area with respect to a class for the factor; and
- = is the percentage for the entire domain.
2.5. Accuracy Assessment of Model Outputs
2.6. Validation of MODIS Fire Points with Model Outputs
3. Results
3.1. Model Outputs: Forest Fire-Prone Area Maps
3.2. Accuracies of Model Outputs
3.3. Validation of MODIS Fire Points
4. Discussion
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
- Tien Bui, D.; Bui, Q.T.; Nguyen, Q.P.; Pradhan, B.; Nampak, H.; Trinh, P.T. A hybrid artificial intelligence approach using gis-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area. Agric. For. Meteorol. 2017, 233, 32–44. [Google Scholar] [CrossRef]
- Huebner, K.; Lindo, Z.; Lechowicz, M. Post-fire succession of collembolan communities in a northern hardwood forest. Eur. J. Soil Biol. 2012, 48, 59–65. [Google Scholar] [CrossRef]
- Argañaraz, J.P.; Gavier Pizarro, G.; Zak, M.; Landi, M.A.; Bellis, L.M. Human and biophysical drivers of fires in semiarid Chaco mountains of Central Argentina. Sci. Total Environ. 2015, 520, 1–12. [Google Scholar] [CrossRef] [PubMed]
- Stevens-Rumann, C.S.; Kemp, K.B.; Higuera, P.E.; Harvey, B.J.; Rother, M.T.; Donato, D.C.; Morgan, P.; Veblen, T.T. Evidence for declining forest resilience to wildfires under climate change. Ecol. Lett. 2018, 21, 243–252. [Google Scholar] [CrossRef] [PubMed]
- Allen, C.D.; Macalady, A.K.; Chenchouni, H.; Bachelet, D.; Mcdowell, N.; Vennetier, M.; Kitzberger, T.; Rigling, A.; Breshears, D.D.; Hogg, E.H. A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. For. Ecol. Manag. 2010, 259, 660–684. [Google Scholar] [CrossRef] [Green Version]
- Rother, M.T.; Veblen, T.T. Limited conifer regeneration following wildfires in dry ponderosa pine forests of the Colorado Front Range. Ecosphere 2016, 7, e01594. [Google Scholar] [CrossRef]
- Keeley, J.E. Fire Intensity, Fire Severity and Burn Severity: A Brief Review and Suggested Usage. Int. J. Wildland Fire 2009, 18, 116–126. [Google Scholar] [CrossRef]
- Cannon, S.H.; Gartner, J.E. Wildfire-related debris flow from a hazards perspective. In Debris-Flow Hazards and Related Phenomena; Springer: Berlin/Heidelberg, Germany, 2005; ISBN 3-540-20726-0. [Google Scholar]
- Carabella, C.; Miccadei, E.; Paglia, G.; Sciarra, N. Post-wildfire landslide hazard assessment: The case of the 2017 montagna del morrone fire (central apennines, Italy). Geosciences 2019, 9, 175. [Google Scholar] [CrossRef] [Green Version]
- Wickramasinghe, C.H.; Jones, S.; Reinke, K.; Wallace, L. Development of a multi-spatial resolution approach to the surveillance of active fire lines using Himawari-8. Remote Sens. 2016, 8, 932. [Google Scholar] [CrossRef] [Green Version]
- Shakesby, R.; Doerr, S. Wildfire as a hydrological and geomorphological agent. Earth-Sci. Rev. 2006, 74, 269–307. [Google Scholar] [CrossRef]
- Esposito, G.; Parodi, A.; Lagasio, M.; Masi, R.; Nanni, G.; Russo, F.; Alfano, S.; Giannatiempo, G. Characterizing Consecutive Flooding Events after the 2017 Mt. Salto Wildfires (Southern Italy): Hazard and Emergency Management Implications. Water 2019, 11, 2663. [Google Scholar]
- Busico, G.; Giuditta, E.; Kazakis, N.; Colombani, N. A Hybrid GIS and AHP Approach for Modelling Actual and Future Forest Fire Risk Under Climate Change Accounting Water Resources Attenuation Role. Sustainability 2019, 11, 7166. [Google Scholar] [CrossRef] [Green Version]
- Abatzoglou, J.T.; Williams, A.P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl. Acad. Sci. USA 2016, 113, 11770–11775. [Google Scholar] [CrossRef] [Green Version]
- Littell, J.S.; Peterson, D.L.; Riley, K.L.; Liu, Y.; Luce, C.H. A review of the relationships between drought and forest fire in the United States. Glob. Chang. Biol. 2016, 22, 2353–2369. [Google Scholar] [CrossRef] [PubMed]
- Moritz, M.A.; Parisien, M.A.; Batllori, E.; Krawchuk, M.A.; Van Dorn, J.; Ganz, D.J.; Hayhoe, K. Climate change and disruptions to global fire activity. Ecosphere 2012, 3, 1–22. [Google Scholar] [CrossRef]
- Arpaci, A.; Malowerschnig, B.; Sass, O.; Vacik, H. Using multi variate data mining techniques for estimating fire susceptibility of Tyrolean forests. Appl. Geogr. 2014, 53, 258–270. [Google Scholar] [CrossRef]
- Eastaugh, C.S.; Hasenauer, H. Deriving forest fire ignition risk with biogeochemical process modelling. Environ. Modell. Softw. 2014, 55, 132–142. [Google Scholar] [CrossRef] [Green Version]
- Hoffman, C.M.; Canfield, J.; Linn, R.R.; Mell, W.; Sieg, C.H.; Pimont, F.; Ziegler, J. Evaluating crown fire rate of spread predictions from physics-based models. Fire Technol. 2016, 52, 221–237. [Google Scholar] [CrossRef] [Green Version]
- Pourtaghi, Z.S.; Pourghasemi, H.R.; Rossi, M. Forest fire susceptibility mapping in the minudasht forests, golestan province. Iran. Environ. Earth Sci. 2015, 73, 1515–1533. [Google Scholar] [CrossRef]
- Cheng, T.; Wang, J. Integrated spatio-temporal data mining for forest fire prediction. Trans. Gis. 2008, 12, 591–611. [Google Scholar] [CrossRef]
- Casbeer, D.W.; Kingston, D.B.; Beard, R.W.; McLain, T.W. Cooperative forest fire surveillance using a team of small unmanned air vehicles. Int. J. Syst. Sci. 2006, 37, 351–360. [Google Scholar] [CrossRef]
- Finney, M.A. FARSITE: Fire Area Simulator-Model Development and Evaluation; U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station: Ft. Collins, CO, USA, 1998; p. RMRS-RP-4. [Google Scholar]
- Mell, W.; Charney, J.; Jenkins, M.A.; Cheney, P.; Gould, J. Numerical simulations of grassland fire behavior from the LANL-FIRETEC and NIST-WFDS models. In Remote Sensing and Modeling Applications to Wildland Fires; Springer: Berlin/Heidelberg, Germany, 2013; pp. 209–225. Available online: https://doi.org/10.1007/978-3-642-32530-4_15 (accessed on 9 February 2020).
- Xue, W.; Zhang, G. FDS Fire Simulation and Application. Jilin Forestry Sci. Technol. 2006, 6, 4–8. Available online: http://en.cnki.com.cn/Article_en/CJFDTotal-JLLK200606004.htm (accessed on 11 January 2018).
- Sturtevant, B.R.; Scheller, R.M.; Miranda, B.R.; Shinneman, D.; Syphard, A. Simulating dynamic and mixed-severity fire regimes: A process-based fireextension for LANDIS-II. Ecol. Model. 2009, 220, 3380–3393. [Google Scholar] [CrossRef]
- Massada, A.B.; Syphard, A.D.; Hawbaker, T.J.; Stewart, S.I.; Radeloff, V.C. Effects of ignition location models on the burn patterns of simulated wildfires. Environ. Model. Softw. 2011, 26, 583–592. [Google Scholar] [CrossRef]
- Pimont, F.; Parsons, R.; Rigolot, E.; de Coligny, F.; Dupuy, J.-L.; Dreyfus, P.; Linn, R.R. Modeling fuels and fire effects in 3D: Model description and applications. Environ. Model. Softw. 2016, 80, 225–244. [Google Scholar] [CrossRef] [Green Version]
- Kanungo, D.P.; Arora, M.; Sarkar, S.; Gupta, R. A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas. Eng. Geol. 2006, 85, 347–366. [Google Scholar] [CrossRef]
- Marjanovic, M.; Kovacevic, M.; Bajat, B.; Vozenilek, V. Landslide susceptibility assessment using SVM machine learning algorithm. Eng. Geol. 2011, 123, 225–234. [Google Scholar] [CrossRef]
- Tehrany, M.S.; Pradhan, B.; Jebur, M.N. Spatial prediction of flood susceptible areas using rule based decision tree (DT) and a novel ensemble bivariate and multivariate statistical models in GIS. J. Hydrol. 2013, 504, 69–79. [Google Scholar] [CrossRef]
- A Hybrid Intelligent Algorithm by Combining Particle Swarm Optimization with Chaos Searching Technique for Solving Nonlinear Bilevel Programming Problems. Available online: https://doi.org/10.1016/j.swevo.2012.08.001 (accessed on 22 October 2018).
- Teodoro, A.; Duarte, L.; Sillero, N.; Gonçalves, J.A.; Fonte, J.; Gonçalves-Seco, L.; Pinheiro da Luz, L.M.; dos Santos Beja, L.M.R. An integrated and open source GIS environmental management system for a protected area in the south of Portugal. In Proceedings of the SPIE 9644, Earth Resources and Environmental Remote Sensing/GIS Applications VI, Toulouse, France, 24 September 2015. [Google Scholar]
- Gitas, I.Z.; San-Miguel-Ayanz, J.; Chuvieco, E.; Camia, A. Advances in remote sensing and GIS applications in support of forest fire management. Int. J. Wildland Fire 2014, 23, 603–605. [Google Scholar] [CrossRef] [Green Version]
- Wittenberg, L.; Malkinson, D. Spatio-temporal perspectives of forest fires regimes in a maturing Mediterranean mixed pine landscape. Eur. J. Forest Res. 2009, 128, 297–304. [Google Scholar] [CrossRef]
- Tehrany, M.S.; Pradhan, B.; Jebur, M.N. Flood susceptibility analysis and its verification using a novel ensemble support vector machine and frequency ratio method. Stochastic Environ. Res. Risk Assess. 2015, 29, 1149–1165. [Google Scholar] [CrossRef]
- Kant Sharma, L.; Kanga, S.; Singh Nathawat, M.; Sinha, S.; Chandra Pandey, P. Fuzzy AHP for forest fire risk modeling. Disaster Prev. Manag. Int. J. 2012, 21, 160–171. [Google Scholar] [CrossRef]
- DoFPS (Department of Forests and Park Services). Forestry Facts and Figures; Kuensel Corporation Limited: Thimphu, Bhutan, 2015. [Google Scholar]
- Dorji, T. Forest fire situation in Bhutan. In International Forest Fire News (IFFN); FAO/UNECE: Geneva, Switzerland, 2006; p. 9. Available online: https://gfmc.online/wp-content/uploads/07-IFFN-34-Bhutan-1.pdf (accessed on 1 January 2020).
- Tshering, K. Development of an Effective Forest Fire Management Strategy for Bhutan; The University of Montana: Helena, MT, USA, 2006; p. 87. Available online: https://scholarworks.umt.edu/etd/2049/ (accessed on 21 February 2019).
- Climate Change Effects on Wildfire Hazards in the Wildland-Urban-Interface-Blue Pine Forests of Bhutan. Available online: https://doi.org/10.1016/j.foreco.2020.117927 (accessed on 18 February 2020).
- NSB (National Statistics Bureau). Bhutan at a Glance; National Statistics Bureau: Thimphu, Bhutan, 2018; Available online: http://www.nsb.gov.bt/publication/files/pub3kw5078sm.pdf (accessed on 18 February 2020).
- NBC (National Biodiversity Centre). Biodiversity Statistics of Bhutan 2017: A Preliminary Baseline; National Biodiversity Centre: Thimphu, Bhutan, 2017; p. 65. Available online: https://www.researchgate.net/publication/335001155_Biodiversity_Statistics_of_Bhutan_2017_-_A_Preliminary_Baseline (accessed on 12 December 2019).
- MoAF (Ministry of Agriculture and Forests). Atlas of Bhutan; Landuse Planning Section (LUPS), Policy and Planning Division: Thimphu, Bhutan, 1997; p. 49. [Google Scholar]
- Wangda, P.; Ohsawa, M. Gradational forest change along the climatically dry valley slopes of Bhutan in the midst of humid eastern Himalaya. Plant Ecol. 2006, 186, 109–128. [Google Scholar] [CrossRef]
- Ohsawa, M.E. Life Zone Ecology of the Bhutan Himalaya; Laboratory of Ecology, Chiba University: Chiba, Japan, 1987. [Google Scholar]
- Higuera, P.E.; Abatzoglou, J.T.; Littell, J.S.; Morgan, P. The changing strength and nature of fire-climate relationships in the northern Rocky Mountains, USA, 1902–2008. PLoS ONE 2015, 10, e0127563. [Google Scholar] [CrossRef] [Green Version]
- Tehrany, M.S.; Lee, M.-J.; Pradhan, B.; Jebur, M.N.; Lee, S. Flood susceptibility mapping using integrated bivariate and multivariate statistical models. Environ. Earth Sci. 2014, 72, 4001–4015. [Google Scholar] [CrossRef]
- Umar, Z.; Pradhan, B.; Ahmad, A.; Jebur, M.N.; Tehrany, M.S. Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia. Catena 2014, 118, 124–135. [Google Scholar] [CrossRef]
- Verde, J.; Zêzere, J. Assessment and validation of wildfire susceptibility and hazard in Portugal. Nat. Hazards Earth Syst. Sci. 2010, 10, 485–497. [Google Scholar] [CrossRef]
- Marsala, V.; Galli, A.; Paglia, G.; Miccadei, E. Landslide Susceptibility Assessment of Mauritius Island (Indian Ocean). Geosciences 2019, 9, 493. [Google Scholar] [CrossRef] [Green Version]
- Fell, R.; Corominas, J.; Bonnard, C.; Cascini, L.; Leroi, E.; Savage, W.Z. Guidelines for landslide susceptibility, hazard and risk zoning for land-use planning. Eng. Geol. 2008, 102, 99–111. [Google Scholar] [CrossRef] [Green Version]
- Roslee, R.; Mickey, A.C.; Simon, N.; Norhisham, M.N. Landslide susceptibility analysis (LSA) using weighted overlay method (WOM) along the Genting Sempah to Bentong Highway, Pahang. Malays. J. Geosci. 2017, 1, 13–19. [Google Scholar] [CrossRef]
- Robichaud, P.R.; Wagenbrenner, J.W.; Pierson, F.B.; Spaeth, K.E.; Ashmun, L.E.; Moffet, C.A. Infiltration and interrill erosion rates after a wildfire in western Montana, USA. Catena 2016, 142, 77–88. [Google Scholar] [CrossRef] [Green Version]
- Abbate, A.; Longoni, L.; Ivanov, V.I.; Papini, M. Wildfire Impacts on Slope Stability Triggering in Mountain Areas. Geosciences 2019, 9, 417. [Google Scholar] [CrossRef] [Green Version]
- Jarvis, A.; Reuter, H.I.; Nelson, A.; Geuvara, E. Hole-Filled SRTM for the Glove Version 3. Available online: http://srtm.csi.cgiar.org (accessed on 12 April 2016).
- Tehrany, M.S.; Pradhan, B.; Jebur, M.N. Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS. J. Hydrol. 2014, 512, 332–343. [Google Scholar] [CrossRef]
- Akgun, A.; Dag, S.; Bulut, F. Landslide susceptibility mapping for a landslide-prone area (Findikli, NE of Turkey) by likelihood-frequency ratio and weighted linear combination models. Environ. Geol. 2008, 54, 1127–1143. [Google Scholar] [CrossRef]
- Huesca, M.; Litago, J.; Palacios-Orueta, A.; Montes, F.; Sebastián-López, A.; Escribano, P. Assessment of forest fire seasonality using MODIS fire potential: A time series approach. Agric. For. Meteorol. 2009, 149, 1946–1955. [Google Scholar] [CrossRef]
- DoFPS (Department of Forests and Park Services). Forest Fire Management Strategy of Bhutan; Kuensel Corporation Ltd.: Thimphu, Bhutan, 2012; p. 21. [Google Scholar]
- Erten, E.; Kurgun, V.; Musaoglu, N. Forest fire risk zone mapping from satellite imagery and GIS: A case study. In Proceedings of the XXth Congress of the International Society for Photogrammetry and Remote Sensing, Istanbul, Turkey, 12–23 July 2004; pp. 222–230. [Google Scholar]
- Opie, K.; March, A.; Leonard, J.; Newnham, G. Indicators of Fire Vulnerability: Risk Factors in Victorian Settlements. CSIRO and Melbourne University report to the Natural Disaster Resilience grants Scheme. Melbourne, Australia, 2014. Available online: https://msd.unimelb.edu.au/__data/assets/pdf_file/0005/2590628/Indices-report-Final-2014-April.pdf (accessed on 22 December 2019).
- Evaluation of Forest Fire Risk with GIS. Available online: http://www.pjoes.com/Evaluation-of-Forest-Fire-Risk-with-GIS,89182,0,2.html (accessed on 8 November 2019).
- Chuvieco, E.; Congalton, R.G. Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sens. Environ. 1989, 29, 147–159. [Google Scholar] [CrossRef]
- Sowmya, S.; Somashekar, R. Application of Remote Sensing and Geographical Information System in Mapping Forest Fire Risk Zone at Bhadra Wildlife Sanctuary, India. 2010. Available online: https://www.ncbi.nlm.nih.gov/pubmed/21506484 (accessed on 16 March 2019).
- Schmidt, D.A.; Taylor, A.H.; Skinner, C.N. The influence of fuels treatment and landscape arrangement on simulated fire behavior, Southern Cascade range, California. For. Ecol. Manag. 2008, 255, 3170–3184. [Google Scholar] [CrossRef]
- Ghobadi, G.J.; Gholizadeh, B.; Dashliburun, O.M. Forest fire risk zone mapping from geographic information system in Northern Forests of Iran (Case study, Golestan province). Int. J. Agric. Crop Sci. 2012, 4, 818–824. [Google Scholar]
- Lentile, L.B.; Smith, F.W.; Shepperd, W.D. Influence of topography and forest structure on patterns of mixed severity fire in ponderosa pine forests of the South Dakota Black Hills, USA. Int. J. Wildl. Fire 2006, 15, 557–566. [Google Scholar] [CrossRef]
- Saaty, R.W. The analytic hierarchy process—What it is and how it is used. Math. Model. 1987, 9, 161–176. [Google Scholar] [CrossRef] [Green Version]
- Chhetri, S.; Kayastha, P. Manifestation of an analytic hierarchy process (AHP) model on fire potentialzonation mapping in Kathmandu Metropolitan City, Nepal. ISPRS Int. J. Geo-Inf. 2015, 4, 400–417. [Google Scholar] [CrossRef] [Green Version]
- Thakur, A.K.; Singh, D. Forest fire risk zonation using geospatial techniques and analytic hierarchy process in Dehradun District, Uttarakhand, India. Univ. J. Environ. Res. Technol. 2014, 4, 82–89. [Google Scholar]
- Vadrevu, K.P.; Eaturu, A.; Badarinath, K.V.S. Fire risk evaluation using multicriteria analysis—A case study. Environ. Monit. Assess. 2010, 166, 223–239. [Google Scholar] [CrossRef]
- Bonham-Carter, G.F. Geographic Information Systems for Geoscientists: Modeling with GIS; Pergamon Press: Oxford, UK, 1994. [Google Scholar]
- Ozdemir, A.; Altural, T. A comparative study of frequency ratio, weights of evidence and logistic regression methods for landslide susceptibility mapping: Sultan mountains, sw turkey. J. Asian Earth Sci. 2013, 64, 180–197. [Google Scholar] [CrossRef]
- Oh, H.J.; Kim, Y.S.; Choi, J.K.; Park, E.; Lee, S. GIS mapping of regional probabilistic groundwater potential in the area of Pohang City. Korea. J. Hydrol. 2011, 399, 158–172. [Google Scholar] [CrossRef]
- Pourghasemi, H.R.; Pradhan, B.; Gokceoglu, C. Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Nat. Hazards 2012, 63, 965–996. [Google Scholar] [CrossRef]
- Hong, H.; Biswajeet, P.; Bui, D.T.; Xu, C. Landslide susceptibility assessment at the Suichuan area (China) using support vector machine model. In Emerging Economies, Risk and Development, and Intelligent Technology: Proceedings of the 5th International Conference on Risk Analysis and Crisis Response, Tangier, Morocco, 1–3 June 2015; CRC Press: Boca Raton, FL, USA, 2015; Available online: https://www.researchgate.net/profile/Chong_Xu/publication/279456422_Landslide_susceptibility_assessment_at_the_Suichuan_area_China_using_support_vector_machine_model/links/580e9e9208ae7525273d299a/Landslide-susceptibility-assessment-at-the-Suichuan-area-China-using-support-vector-machine-model.pdf (accessed on 25 November 2019).
- Fielding, A.H.; Bell, J.F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 1997, 24, 38–49. [Google Scholar] [CrossRef]
- Müller, D.; Stefan, S. Can the MODIS Active Fire Hotspots be Used to Monitor Vegetation Fires in the Lao PDR? Climate Protection through Avoided Deforestation (CliPAD) programme, Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ): Vientiane, Lao PDR, 2011; p. 50. Available online: https://amor.cms.hu-berlin.de/~muelleda/download/Mueller_&_Suess_2011_MODIS_fire_hotspots_to_monitor_vegetation_fires_in_Laos.pdf (accessed on 22 October 2019).
- Justice, C.O.; Giglio, L.; Korontzi, S.; Owens, J.; Morisette, J.; Roy, D.; Descloitres, J.; Alleaume, S.; Petitcolin, F.; Kaufman, Y.J. The MODIS fire products. Remote Sens. Environ. 2002, 83, 244–262. [Google Scholar] [CrossRef]
- Rathore, C.S.; Dubey, Y.; Shrivastava, A.; Pathak, P.; Patil, V. Opportunities of habitat connectivity for tiger (Panthera tigris) between Kanha and Pench national parks in Madhya Pradesh, India. PLoS ONE 2012, 7, e39996. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Müller, D.; Suess, S.; Hoffmann, A.A.; Buchholz, G. The value of satellite-based active fire data for monitoring, reporting and verification of REDD+ in the Lao PDR. Human Ecol. 2013, 41, 7–20. [Google Scholar] [CrossRef]
- Tanpipat, V.; Honda, K.; Nuchaiya, P. MODIS Hotspot Validation over Thailand. Remote Sens. 2009, 1, 1043–1054. [Google Scholar] [CrossRef] [Green Version]
Spatial Layers | Classes | Hazard Value |
---|---|---|
LULC | Glaciers/Snow/Rock outcrops/ Water spreads/landslips/Marshy areas | 1 |
Meadows | 2 | |
Scrub forest/Settlements/Agriculture/Improved pasture/horticulture | 3 | |
Plantations | 4 | |
Broadleaf | 5 | |
Fir | 6 | |
Broadleaf with conifer | 7 | |
Mixed Conifer | 8 | |
Blue pine | 9 | |
Chirpine | 10 | |
Settlement (Distance from human settlement in meters) | 0–1927.6 | 10 |
1927.7–3426.9 | 9 | |
3427–5140.4 | 8 | |
5140.5–6853.9 | 7 | |
6854–8781.5 | 6 | |
8781.6–11,138 | 5 | |
11,139–13,922 | 4 | |
13,923–17,991 | 3 | |
17,992–24,845 | 2 | |
24,846–54,617 | 1 | |
Road (Distance from road in meters) | 0–236.61 | 10 |
236.62–1183.1 | 9 | |
1183.2–2602.8 | 8 | |
2602.9–4259.1 | 7 | |
4259.2–6152 | 6 | |
6152.1–8518.1 | 5 | |
8518.2–11,357 | 4 | |
11,358–16,090 | 3 | |
16,091–25,791 | 2 | |
25,792–60,337 | 1 | |
Border (Distance from the southern international border with India in meters) | 0–3742.6 | 10 |
3742.7–8349 | 9 | |
8349.1–13,243 | 8 | |
13,244–18,425 | 7 | |
18,426–24,183 | 6 | |
24,184–30,229 | 5 | |
30,230–36,851 | 4 | |
36,852–44,912 | 3 | |
44,913–54,412 | 2 | |
54,413–73,413 | 1 |
Spatial Layers | Classes | Hazard Value |
---|---|---|
Aspect (directions) | Flat | 6 |
North | 1 | |
Northeast | 4 | |
East | 5 | |
Southeast | 7 | |
South | 9 | |
Southwest | 8 | |
West | 3 | |
Northwest | 2 | |
Elevation (meters above sea level) | 5–891 | 1 |
891.001–1406 | 2 | |
1406.01–1856 | 3 | |
1856.01–2295 | 4 | |
2295.01–2714 | 5 | |
2714.01–3111 | 6 | |
3111.01–3556 | 7 | |
3556.01–4090 | 8 | |
4090.01–4654 | 9 | |
4654.01–7519 | 9 | |
Slope (Degree) | 0–11.758 | 1 |
11.759–16.6 | 2 | |
16.601–20.404 | 3 | |
20.405–23.516 | 4 | |
23.517–26.629 | 5 | |
26.63–29.741 | 6 | |
29.742–32.854 | 7 | |
32.855–36.658 | 8 | |
36.659–41.845 | 9 | |
41.846–87.84 | 9 |
Factors | LULC | Settlement | Road | Boundary | Elevation | Aspect | Slope |
---|---|---|---|---|---|---|---|
LULC | 1 | 3 | 4 | 5 | 6 | 7 | 8 |
Settlement | 1/3 | 1 | 2 | 3 | 4 | 5 | 6 |
Road | 1/4 | 1/2 | 1 | 2 | 3 | 4 | 5 |
Boundary | 1/5 | 1/3 | 1/2 | 1 | 2 | 3 | 4 |
Elevation | 1/6 | 1/4 | 1/3 | 1/2 | 1 | 2 | 3 |
Aspect | 1/7 | 1/5 | 1/4 | 1/3 | 1/2 | 1 | 2 |
Slope | 1/8 | 1/6 | 1/5 | 1/4 | 1/3 | 1/2 | 1 |
Factors | Derived Ratio Scale | Percentage of Influence |
---|---|---|
LULC | 0.451 | 45 |
Settlement | 0.188 | 19 |
Road | 0.124 | 12 |
Boundary | 0.086 | 9 |
Elevation | 0.063 | 6 |
Aspect | 0.049 | 5 |
Slope | 0.04 | 4 |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Tshering, K.; Thinley, P.; Shafapour Tehrany, M.; Thinley, U.; Shabani, F. 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 2020, 2, 36-58. https://doi.org/10.3390/forecast2020003
Tshering K, Thinley P, Shafapour Tehrany M, Thinley U, Shabani F. 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. 2020; 2(2):36-58. https://doi.org/10.3390/forecast2020003
Chicago/Turabian StyleTshering, Kinley, Phuntsho Thinley, Mahyat Shafapour Tehrany, Ugyen Thinley, and 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 2, no. 2: 36-58. https://doi.org/10.3390/forecast2020003