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Demarcation of Forest Fire Risk Zones in Silent Valley National Park and the Effectiveness of Forest Management Regime

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Abstract

Wildfires pose a major threat to the forest ecosystems and species of the Western Ghats’ protected areas. Fires have also ravaged the Silent Valley National Park in the past. This study aimed to map the fire risk zones using the analytical hierarchy process (AHP) method, to assess the effect of each factor on the occurrence of fire and to assess the effectiveness of the forest management regime on fire prevention and mitigation. The causative factors selected for the risk modelling are land cover types, slope, aspect, normalized difference vegetation index (NDVI), water ratio index (WRI), normalized difference water index (NDWI), proximity to the settlement, proximity to the road and proximity to the anti-poaching camp shed. AHP is utilized to calculate weights, and GIS is utilized to identify the risk zones. The area covered by the fire risk map is classified into five zones and was validated using the fire incidence data collected for the period 2002–2020. According to the study, 72% of all fires occur in areas categorized as high or very high risk on the prepared map. The result of the validation revealed that the AHP model is effective (with an AUC value of 78.79% for the training dataset and 77.64% for the validation dataset) in identifying the fire risk zones in Silent Valley National Park. The vast majority of fires in this region have been proven to be caused by human activity. This study confirmed that the forest management initiatives are effective in the core zone of the national park. The findings of this study will aid planners, managers and decision-makers in determining the location of fire lookout towers, installing sensors and constructing firebreaks, etc.

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References

  • Achour Y, Boumezbeur A, Hadji R, Chouabbi A, Cavaleiro V, Bendaoud EA (2017) Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria. Arab J Geosci 10. https://doi.org/10.1007/s12517-017-2980-6

  • Adab H, Kanniah KD, Solaimani K (2013) Modeling forest fire risk in the northeast of Iran using remote sensing and GIS techniques. Nat Hazards 65:1723–1743. https://doi.org/10.1007/s11069-012-0450-8

    Article  Google Scholar 

  • Adarsh CK, Vidyasagaran K, Ganesh PN (2019) The diversity and distribution of polypores (Basidiomycota: Aphyllophorales) in wet evergreen and shola forests of Silent Valley National Park, southern Western Ghats, India, with three new records. J Threat Taxa 11(7): 13886–13909. https://doi.org/10.11609/jot.3856.11.7.13886-13909

  • Agus C, Azmi FF, Widiyatno, Ilfana ZR, Wulandari D, Rachmanadi D, Harun MK, Yuwati TW (2019) The impact of forest fire on the biodiversity and the soil characteristics of tropical peatland. In: Leal Filho W, Barbir J, Preziosi R (eds) Handbook of climate change and biodiversity. Springer, Cham, Switzerland, pp. 287–303. https://doi.org/10.1007/978-3-319-98681-4_18

  • Ajin RS, Ciobotaru AM, Vinod PG, Jacob MK (2015) Forest and wildland fire risk assessment using geospatial techniques: a case study of Nemmara forest division, Kerala, India. J Wetlands Biodiver 5:29–37

    Google Scholar 

  • Ajin RS, Jacob MK, Menon ARR, Vinod PG (2014) Forest fire risk analysis using geo-information technology: a study of Peppara Wildlife Sanctuary, Thiruvananthapuram, Kerala, India. In: Pradeepkumar AP, Behr FJ, Illiyas FT, Shaji E (eds) Proceedings of the 2nd Disaster Risk Vulnerability Conference 2014. University of Kerala, Thiruvananthapuram, India, pp. 160–165.

  • Ajin RS, Loghin AM, Jacob MK, Vinod PG, Krishnamurthy RR (2016a) The risk assessment of potential forest fire in Idukki Wildlife Sanctuary using RS and GIS techniques. Int J Adv Earth Sci Eng 5(1):308–318

    Article  Google Scholar 

  • Ajin RS, Loghin AM, Vinod PG, Jacob MK (2016b) Forest fire risk zone mapping in Chinnar Wildlife Sanctuary, Kerala, India: a study using geospatial tools. J Global Resour 3:16–26

    Google Scholar 

  • Ajin RS, Loghin AM, Vinod PG, Jacob MK (2016c) Forest fire risk zone mapping using RS and GIS techniques: a study in Achankovil forest division, Kerala, India. J Earth, Environ Health Sci 2(3):109–115. https://doi.org/10.4103/2423-7752.199288

    Article  Google Scholar 

  • Ajin RS, Loghin AM, Vinod PG, Jacob MK (2017a) Mapping of forest fire risk zones in Peechi-Vazhani Wildlife Sanctuary, Thrissur, Kerala, India: a study using geospatial techniques. J Wetlands Biodiversity 7:7–16

    Google Scholar 

  • Ajin RS, Loghin AM, Vinod PG, Jacob MK (2017b) The risk analysis of potential forest fires in a Wildlife Sanctuary in the Western Ghats (Southwest Indian Peninsula) using geospatial techniques. Int J Health Syst Disaster Manag 5(1):18–23. https://doi.org/10.4103/ijhsdm.ijhsdm_26_16

    Article  Google Scholar 

  • Ajin RS, Loghin AM, Vinod PG, Menon ARR, Jacob MK (2018) Forest fire risk assessment using geospatial techniques: a study in Mannarkkad forest division of Palakkad district, Kerala, India. ECOTERRA - J Environ Res Protect 15(1):1–9

    Google Scholar 

  • Akshaya M, Danumah JH, Saha S, Ajin RS, Kuriakose SL (2021) Landslide susceptibility zonation of the Western Ghats region in Thiruvananthapuram district (Kerala) using geospatial tools: a comparison of the AHP and fuzzy-AHP methods. Safety in Extreme Environments 3(2). https://doi.org/10.1007/s42797-021-00042-0

  • Aquilue N, Fortin MJ, Messier C, Brotons L (2019) The potential of agricultural conversion to shape forest fire regimes in Mediterranean landscapes. Ecosystems. https://doi.org/10.1007/s10021-019-00385-7

    Article  Google Scholar 

  • Arca D, Hacısalihoğlu M, Kutoğlu ŞH (2020) Producing forest fire susceptibility map via multi-criteria decision analysis and frequency ratio methods. Nat Hazards 104:73–89. https://doi.org/10.1007/s11069-020-04158-7

    Article  Google Scholar 

  • Bahrami Y, Hassani H, Maghsoudi A (2020) Landslide susceptibility mapping using AHP and fuzzy methods in the Gilan province, Iran. GeoJournal. https://doi.org/10.1007/s10708-020-10162-y

  • Cao Q, Zhang L, Su Z, Wang G, Guo F (2020) Exploring spatially varying relationships between forest fire and environmental factors at different quantile levels. Int J Wildland Fire 29:486–498. https://doi.org/10.1071/WF19010

    Article  Google Scholar 

  • Chandra S (2005) Application of remote sensing and GIS technology in forest fire risk modeling and management of forest fires: a case study in the Garhwal Himalayan region. In: van Oosterom P, Zlatanova S, Fendel EM (eds) Geo-information for disaster management. Springer, Berlin, Heidelberg, Germany, pp. 1239–1254. https://doi.org/10.1007/3-540-27468-5_86

  • Couto FT, Iakunin M, Salgado R, Pinto P, Viegas T, Pinty JP (2020) Lightning modelling for the research of forest fire ignition in Portugal. Atmosph Res 242.https://doi.org/10.1016/j.atmosres.2020.104993

  • Cui X, Alam MA, Perry GLW, Paterson AM, Wyse SV, Curran TJ (2019) Green firebreaks as a management tool for wildfires: lessons from China. J Environ Manage 233:329–336. https://doi.org/10.1016/j.jenvman.2018.12.043

    Article  Google Scholar 

  • Eskandari S (2017) A new approach for forest fire risk modeling using fuzzy AHP and GIS in Hyrcanian forests of Iran. Arab J Geosci 10. https://doi.org/10.1007/s12517-017-2976-2

  • Eslami R, Azarnoush M, Kialashki A, Kazemzadeh F (2021) GIS-based forest fire susceptibility assessment by random forest, artificial neural network and logistic regression methods. J Trop Forest Sci 33(2): 173–184. https://doi.org/10.26525/jtfs2021.33.2.173

  • Estes BL, Knapp EE, Skinner CN, Miller JD, Preisler HK (2017) Factors influencing fire severity under moderate burning conditions in the Klamath Mountains, northern California, USA. Ecosphere 8(5). https://doi.org/10.1002/ecs2.1794

  • Fernandes PM, Botelho H (2003) A review of prescribed burning effectiveness in fire hazard reduction. Int J Wildland Fire 12:117–128. https://doi.org/10.1071/WF02042

    Article  Google Scholar 

  • Flach PA (2011) ROC analysis. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer, Boston, United States. https://doi.org/10.1007/978-0-387-30164-8_733

  • Gao BC (1996) NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sens Environ 58(3):257–266. https://doi.org/10.1016/S0034-4257(96)00067-3

    Article  Google Scholar 

  • Gheshlaghi HA, Feizizadeh B, Blaschke T (2020) GIS-based forest fire risk mapping using the analytical network process and fuzzy logic. J Environ Planning Manage 63(3):481–499. https://doi.org/10.1080/09640568.2019.1594726

    Article  Google Scholar 

  • Gigović L, Pourghasemi HR, Drobnjak S, Bai S (2019) Testing a new ensemble model based on SVM and random forest in forest fire susceptibility assessment and its mapping in Serbia’s Tara National Park. Forests 10(5). https://doi.org/10.3390/f10050408

  • Gompf K, Traverso M, Hetterich J (2021) Using analytical hierarchy process (AHP) to introduce weights to social life cycle assessment of mobility services. Sustainability 13. https://doi.org/10.3390/su13031258

  • Hammami S, Zouhri L, Souissi D, Souei A, Zghibi A, Marzougui A, Dlala M (2019) Application of the GIS based multi-criteria decision analysis and analytical hierarchy process (AHP) in the flood susceptibility mapping (Tunisia). Arab J Geosci 12. https://doi.org/10.1007/s12517-019-4754-9

  • Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36

    Article  Google Scholar 

  • Hoang TV, Chou TY, Fang YM, Nguyen NT, Nguyen QH, Canh PX, Toan DNB, Nguyen XL, Meadows ME (2020) Mapping forest fire risk and development of early warning system for NW Vietnam using AHP and MCA/GIS methods. Appl Sci 10(12). https://doi.org/10.3390/app10124348

  • Hosmer DW, Lemeshow S (2000) Applied logistic regression, 2nd edn. John Wiley & Sons Inc, United States of America, p 392

    Book  Google Scholar 

  • Jafarzadeh AA, Mahdavi A, Jafarzadeh H (2017) Evaluation of forest fire risk using the Apriori algorithm and fuzzy c-means clustering. J Forest Sci 63: 370–380. https://doi.org/10.17221/7/2017-JFS

  • Jaiswal RK, Mukherjee S, Raju KD, Saxena R (2002) Forest fire risk zone mapping from satellite imagery and GIS. Int J Appl Earth Obs Geoinf 4:1–10. https://doi.org/10.1016/S0303-2434(02)00006-5

    Article  Google Scholar 

  • Kerala Forests and Wildlife Department (2012) Silent valley national park management plan 2012–13 to 2021–2022, Silent Valley National Park Division, Mannarkkad, pp 1–244. Available at https://forest.kerala.gov.in/index.php/forest/forest-management/management-plans

  • Li Y, Feng Z, Chen S, Zhao Z, Wang F (2020) Application of the artificial neural network and support vector machines in forest fire prediction in the Guangxi Autonomous Region. Discrete Dynamics in Nature and Society, China. https://doi.org/10.1155/2020/5612650

    Book  Google Scholar 

  • McArthur AG (1967) Fire behaviour in eucalypt fuels. Leaflet No. 107, Forestry and Timber Bureau, Canberra, Australia.

  • Melo F (2013) Receiver operating characteristic (ROC) curve. In: Dubitzky W, Wolkenhauer O, Cho KH, Yokota H (eds) Encyclopedia of systems biology. Springer, New York, United States. https://doi.org/10.1007/978-1-4419-9863-7_242

  • Nikhil S, Danumah JH, Saha S, Prasad MK, Rajaneesh A, Mammen PC, Ajin RS, Kuriakose SL (2021) Application of GIS and AHP method in forest fire risk zone mapping: a study of the Parambikulam Tiger Reserve, Kerala, India. J Geovisual Spatial Anal 5. https://doi.org/10.1007/s41651-021-00082-x

  • Nuthammachot N, Stratoulias D (2019) A GIS- and AHP-based approach to map fire risk: a case study of Kuan Kreng peat swamp forest. Geocarto International, Thailand. https://doi.org/10.1080/10106049.2019.1611946

    Book  Google Scholar 

  • Parajuli A, Gautam AP, Sharma SP, Bhujel KB, Sharma G, Thapa PB, Bist BS, Poudel S (2020) Forest fire risk mapping using GIS and remote sensing in two major landscapes of Nepal. Geomat Nat Haz Risk 11(1):2569–2586. https://doi.org/10.1080/19475705.2020.1853251

    Article  Google Scholar 

  • Pradeep GS, Danumah JH, Nikhil S, Prasad MK, Patel N, Mammen PC, Rajaneesh A, Oniga VE, Ajin RS, Kuriakose SL (2022) Forest fire risk zone mapping of Eravikulam National Park in India: a comparison between frequency ratio and analytic hierarchy process methods. Croatian J Forest Eng 43(1):199–217. https://doi.org/10.5552/crojfe.2022.1137

    Article  Google Scholar 

  • Price OF, Edwards AC, Russell-Smith J (2007) Efficacy of permanent firebreaks and aerial prescribed burning in western Arnhem Land, Northern Territory, Australia. Int J Wildland Fire 16:295–307. https://doi.org/10.1071/WF06039

    Article  Google Scholar 

  • Qayum A, Ahmad F, Arya R, Singh RK (2020) Predictive modeling of forest fire using geospatial tools and strategic allocation of resources: eForestFire. Stoch Env Res Risk Assess. https://doi.org/10.1007/s00477-020-01872-3

    Article  Google Scholar 

  • Rouse JW, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the Great Plains with ERTS. In: Freden SC, Mercanti EP, Becker MA (eds) Proceedings of the Third Earth Resources Technology Satellite-1 Symposium. NASA, Washington D.C., USA, pp. 309–317.

  • Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation (Decision making series). McGraw Hill, New York, USA

    Google Scholar 

  • Saha S, Arabameri A, Saha A, Blaschke T, Ngo PTT, Nhu VH, Band SS (2021) Prediction of landslide susceptibility in Rudraprayag, India using novel ensemble of conditional probability and boosted regression tree-based on cross-validation method. Sci Total Environ 764. https://doi.org/10.1016/j.scitotenv.2020.142928

  • Saran S, Singh P, Padalia H, Singh A, Kumar V, Chauhan P (2020) Citizen-centric tool for near real-time mapping of active forest fires. Curr Sci 119(5):780–789

    Article  Google Scholar 

  • Satendra, Kaushik AD (2014) Forest fire disaster management. National Institute of Disaster Management, New Delhi, India.

  • Satish KV, Reddy CS (2015) Long term monitoring of forest fires in Silent Valley National Park, Western Ghats, India using remote sensing data. J Indian Soc Remote Sen 44:207–215. https://doi.org/10.1007/s12524-015-0491-z

    Article  Google Scholar 

  • Scott AC (2000) The pre-quaternary history of fire. Palaeogeogr Palaeoclimatol Palaeoecol 164(1–4):281–329. https://doi.org/10.1016/S0031-0182(00)00192-9

    Article  Google Scholar 

  • Setiawan I, Mahmud AR, Mansor S, Shariff ARM, Nuruddin AA (2004) GIS-grid-based and multi-criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang Malaysia. Disaster Prevent Manag 13(5):379–386. https://doi.org/10.1108/09653560410568507

    Article  Google Scholar 

  • Sevinc V, Kucuk O, Goltas M (2020) A Bayesian network model for prediction and analysis of possible forest fire causes. Forest Ecol Manag 457. https://doi.org/10.1016/j.foreco.2019.117723

  • Shakesby RA, Coelho CDA, Ferreira AD, Terry JP, Walsh RPD (1993) Wildfire impacts on soil-erosion and hydrology in wet Mediterranean forest. Portugal International Journal of Wildland Fire 3(2):95–110. https://doi.org/10.1071/WF9930095

    Article  Google Scholar 

  • Shen L, Li C (2010) Water body extraction from Landsat ETM+ imagery using adaboost algorithm. In: Proceedings of 18th International Conference on Geoinformatics. Beijing, China, pp. 1–4. https://doi.org/10.1109/GEOINFORMATICS.2010.5567762

  • Shivakumar BR, Rajashekararadhya SV (2018) Investigation on land cover mapping capability of maximum likelihood classifier: a case study on North Canara, India. Procedia Comput Sci 143:579–586. https://doi.org/10.1016/j.procs.2018.10.434

    Article  Google Scholar 

  • Sivrikaya F, Küçük Ö (2022) Modeling forest fire risk based on GIS-based analytical hierarchy process and statistical analysis in Mediterranean region. Ecol Inform 68. https://doi.org/10.1016/j.ecoinf.2021.101537

  • Smith HG, Sheridan GJ, Lane PNJ, Nyman P, Haydon S (2011) Wildfire effects on water quality in forest catchments: a review with implications for water supply. J Hydrol 396(1–2):170–192. https://doi.org/10.1016/j.jhydrol.2010.10.043

    Article  Google Scholar 

  • Souissi D, Zouhri L, Hammami S, Msaddek MH, Zghibi A, Dlala M (2020) GIS-based MCDM – AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia. Geocarto Int 35(9):991–1017. https://doi.org/10.1080/10106049.2019.1566405

    Article  Google Scholar 

  • Stavi I (2019) Wildfires in grasslands and shrublands: a review of impacts on vegetation, soil, hydrology, and geomorphology. Water 11(5). https://doi.org/10.3390/w11051042

  • Thanh NN, Toan DNB, Canh PX (2017) Remote sensing and GIS application to establish a forest fire risk map for planning of forest fire prevention and mitigation in Son La Province, Vietnam. VNU J Sci Earth Environ Sci 33(3): 53–66. https://doi.org/10.25073/2588-1094/vnuees.4088

  • Thomas AV, Saha S, Danumah JH, Raveendran S, Prasad MK, Ajin RS, Kuriakose SL (2021) Landslide susceptibility zonation of Idukki district using GIS in the aftermath of 2018 Kerala Floods and Landslides: a comparison of AHP and frequency ratio methods. J Geovisual Spatial Anal 5(2). https://doi.org/10.1007/s41651-021-00090-x

  • Tiwari A, Shoab M, Dixit A (2020) GIS-based forest fire susceptibility modeling in Pauri Garhwal, India: a comparative assessment of frequency ratio, analytic hierarchy process and fuzzy modeling techniques. Nat Hazards. https://doi.org/10.1007/s11069-020-04351-8

    Article  Google Scholar 

  • Veena HS, Ajin RS, Loghin AM, Sipai R, Adarsh P, Viswam A, Vinod PG, Jacob MK, Jayaprakash M (2017) Wildfire risk zonation in a tropical forest division in Kerala, India: a study using geospatial techniques. Int J Conservation Sci 8(3):475–484

    Google Scholar 

  • Vinod PG, Ajin RS, Jacob MK (2016) RS and GIS based spatial mapping of forest fire risk zones in Wayanad Wildlife Sanctuary, North Kerala, India. Int J Earth Sci Eng 9(2):498–502

    Google Scholar 

  • Vojtek M, Vojteková J (2019) Flood susceptibility mapping on a national scale in Slovakia using the analytical hierarchy process. Water 11(2). https://doi.org/10.3390/w11020364

  • Yarragunta Y, Srivastava S, Mitra D, Chandola HC (2020) Influence of forest fire episodes on the distribution of gaseous air pollutants over Uttarakhand. GIScience & Remote Sensing, India. https://doi.org/10.1080/15481603.2020.1712100

    Book  Google Scholar 

  • Yathish H, Athira KV, Preethi K, Pruthviraj U, Shetty A (2019) A comparative analysis of forest fire risk zone mapping methods with expert knowledge. J Ind Soc Remote Sen 47:2047–2060. https://doi.org/10.1007/s12524-019-01047-w

    Article  Google Scholar 

  • Yin S, Wang X, Guo M, Santoso H, Guan H (2020) The abnormal change of air quality and air pollutants induced by the forest fire in Sumatra and Borneo in 2015. Atmos Res 243. https://doi.org/10.1016/j.atmosres.2020.105027

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The authors are thankful to the editors and “anonymous” reviewers for their valuable and constructive comments. The authors are grateful to the anonymous reviewers for their invaluable, detailed, and informative suggestions and comments on the different versions of this manuscript.

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Amrutha, K., Danumah, J.H., Nikhil, S. et al. Demarcation of Forest Fire Risk Zones in Silent Valley National Park and the Effectiveness of Forest Management Regime. J geovis spat anal 6, 8 (2022). https://doi.org/10.1007/s41651-022-00103-3

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