Modelling wildfire risk using GIS and Analytical Hierarchy Process (AHP) in Aberdare afromontane forest ranges, Kenya
DOI:
https://doi.org/10.58825/jog.2024.18.1.131Keywords:
Geographical Information System (GIS) Analytical Hierarchy Process (AHP), risk maps, wildfires, protected areas, Aberdare rangesAbstract
The knowledge of wildfire risk is crucial to sensitize and create awareness on fire prevention strategies and mobilization of resources to counter the spread after early detection. This study was undertaken to determine the most important environmental and anthropogenic factors associated with wildfire risk in Aberdares ranges. An integrated participatory decisions and geospatial analysis was used. A pair wise comparative analysis of seven factors namely: proximity to roads, proximity to farming areas within the forest, mean precipitation, elevation, slope, land cover and NDVI was undertaken to attribute weight of each factor in relative to the other though Analytical Hierarchy Process (AHP). A wildfire risk equation was developed using the criteria weights of respective factors and risk map developed using QGIS version 3.16. Results indicated that land cover (0.39) and NDVI (0.23) were the most important factors in developing wildfire risk maps while proximity to roads (0.04) was the least. Wildfire risk maps shows that Aberdare ranges is within low (43%) and moderate (30%) risk zone and the area occupied by high and very high zones is 13% and 4 % respectively. The study recommends testing the applicability of developed method in other areas with different climatic and land cover characteristics.
References
Abdi, O., Kamkar, B., Shirvani, Z., Da Silva, J. A. T., & Buchroithner, M. F. (2018). Spatial-statistical analysis of factors determining forest fires: A case study from Golestan, Northeast Iran. Geomatics, Natural Hazards and Risk, 9(1), 267–280. https://doi.org/10.1080/19475705.2016.1206629
Adaktylou, N., Stratoulias, D., & Landenberger, R. (2020). Wildfire risk assessment based on geospatial open data: Application on chios, greece. ISPRS International Journal of Geo-Information, 9(9). https://doi.org/10.3390/ijgi9090516
Agevi, H., Mwendwa, K. A., Koros, H., Mulinya, C., Kawawa, R.. C., Kimutai, D. K., Wabusya M., Khanyufu, M., J. (2016). PELIS Forestry Programme as a Strategy for Increasing Forest Cover and Improving Community Livelihoods: Case of Malava Forest, Western Kenya. American Journal of Agriculture and Forestry, 4(5), 128. https://doi.org/10.11648/j.ajaf.20160405.13
Akay, A. E., & Erdoan, A. (2017). GIS-based multi-criteria decision analysis for forest fire risk mapping. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4(4W4), 25–30. https://doi.org/10.5194/isprs-annals-IV-4-W4-25-2017
Andela, N., Van Der Werf, G. R., Kaiser, J. W., Van Leeuwen, T. T., Wooster, M. J., & Lehmann, C. E. R. (2016). Biomass burning fuel consumption dynamics in the tropics and subtropics assessed from satellite. Biogeosciences, 13(12), 3717–3734. https://doi.org/10.5194/bg-13-3717-2016
Bar, S., Parida, B. R., Roberts, G., Pandey, A. C., Acharya, P., & Dash, J. (2021). Spatio-temporal characterization of landscape fire in relation to anthropogenic activity and climatic variability over the Western Himalaya, India. GIScience and Remote Sensing, 58(2), 281–299. https://doi.org/10.1080/15481603.2021.1879495
Chen, A., Tang, R., Mao, J., Yue, C., Li, X., Gao, M., Shi, X., Jin, M., Ricciuto, D., Rabin, S., Ciais, P., & Piao, S. (2020). Spatiotemporal dynamics of ecosystem fires and biomass burning-induced carbon emissions in China over the past two decades. Geography and Sustainability, 1(1), 47–58. https://doi.org/10.1016/j.geosus.2020.03.002
Costafreda-Aumedes, S., Comas, C., & Vega-Garcia, C. (2017). Human-caused fire occurrence modelling in perspective: A review. International Journal of Wildland Fire, 26(12), 983–998. https://doi.org/10.1071/WF17026
Downing, T. A., Imo, M., & Kimanzi, J. (2017). Fire occurrence on Mount Kenya and patterns of burning. GeoResJ, 13, 17–26. https://doi.org/10.1016/j.grj.2016.12.003
Fasullo, J. T., Otto-Bliesner, B. L., & Stevenson, S. (2018). ENSO’s Changing Influence on Temperature, Precipitation, and Wildfire in a Warming Climate. Geophysical Research Letters, 45(17), 9216–9225. https://doi.org/10.1029/2018GL079022
Giglio, L. (2018). MODIS Collection 4 Active Fire Product User ’ s Guide Table of Contents. Revisión B. Nasa, 1(June), 64.
Guo, F., Zhang, L., Jin, S., Tigabu, M., Su, Z., & Wang, W. (2016). Modeling anthropogenic fire occurrence in the boreal forest of China using logistic regression and random forests. Forests, 7(11), 1–14. https://doi.org/10.3390/f7110250
Henry, M. C., Maingi, J. K., & McCarty, J. (2019). Fire on the water towers: Mapping burn scars on mount Kenya using satellite data to reconstruct recent fire history. Remote Sensing, 11(2). https://doi.org/10.3390/rs11020104
Kagombe, J., Kiprop, J., Langat, D., Cheboiwo, J., Wekesa, L., Ongugo, P., Mbuvi, M. T., & Leley, N. (2021). Socio-economic Impact of Forest Harvesting Moratorium in Kenya. Kenya Forestry Research Institute.
Kenya Forest Service. (2010). Aberdare forest reserve managment plan. 94. http://www.kenyaforestservice.org/documents/Aberdare.pdf
Kim, S. J., Lim, C. H., Kim, G. S., Lee, J., Geiger, T., Rahmati, O., Son, Y., & Lee, W. K. (2019). Multi-temporal analysis of forest fire probability using socio-economic and environmental variables. Remote Sensing, 11(1). https://doi.org/10.3390/rs11010086
Kipkoech, S., Melly, D. K., Muema, B. W., Wei, N., Kamau, P., Kirika, P. M., Wang, Q., & Hu, G. (2020). An annotated checklist of the vascular plants of Aberdare Ranges forest, a part of Eastern Afromontane biodiversity hotspot. PhytoKeys, 149, 1–88. https://doi.org/10.3897/PHYTOKEYS.149.48042
Lamat, R., Kumar, · Mukesh, Kundu, A., & Lal, · Deepak. (2021). Forest fire risk mapping using analytical hierarchy process (AHP) and earth observation datasets: a case study in the mountainous terrain of Northeast India. SN Applied Sciences, 3, 425. https://doi.org/10.1007/s42452-021-04391-0
Massey, A. L., King, A. A., & Foufopoulos, J. (2014). Fencing protected areas: A long-term assessment of the effects of reserve establishment and fencing on African mammalian diversity. Biological Conservation, 176(August), 162–171. https://doi.org/10.1016/j.biocon.2014.05.023
Matin, M. A., Chitale, V. S., Murthy, M. S. R., Uddin, K., Bajracharya, B., & Pradhan, S. (2017). Understanding forest fire patterns and risk in Nepal using remote sensing, geographic information system and historical fire data. International Journal of Wildland Fire, 26(4), 276–286. https://doi.org/10.1071/WF16056
Maukonen, P., Runsten, L., Thorley, J., & Miles, L. (2016). Mapping to support land-useplanning for REDD+ in Kenya: securing additional benefits. Prepared on Behalf of the UN-REDD Programme.
Milanovi´c, S. M., Markovi´c, N. M., Pamučar, D., Gigovi´cgigovi´c, L., Kosti´c, P. K., & Milanovi´c, S. D. (2020). Forest Fire Probability Mapping in Eastern Serbia: Logistic Regression versus Random Forest Method. https://doi.org/10.3390/f12010005
Mohammadi, F., Bavaghar, M. P., & Shabanian, N. (2014). Forest Fire Risk Zone Modeling Using Logistic Regression and GIS: An Iranian Case Study. March. https://doi.org/10.1007/s11842-013-9244-4
Molaudzi, O. D., & Adelabu, S. A. (2019). Review of the use of remote sensing for monitoring wildfire risk conditions to support fire risk assessment in protected areas. South African Journal of Geomatics, 7(3), 222. https://doi.org/10.4314/sajg.v7i3.2
Nyongesa, K. W., & Vacik, H. (2018). Fire Management in Mount Kenya: A case study of Gathiuru forest station. Forests, 9(8). https://doi.org/10.3390/f9080481
Parajuli, A., Gautam, A. P., Sharma, S. P., Bhujel, B., Sharma, G., Thapa, P. B., Bist, B. S., & Poudel, S. (2020). Forest fire risk mapping using GIS and remote sensing in two major landscapes of Nepal. Geomatics, Natural Hazards and Risk, 11(1), 2569–2586. https://doi.org/10.1080/19475705.2020.1853251
Poletti, C., Dioszegi, G., Nyongesa, K. W., Vacik, H., Barbujani, M., & Kigomo, J. N. (2019). Characterization of Forest Fires to Support Monitoring and Management of Mount Kenya Forest. Mountain Research and Development, 39(3), R1–R12. https://doi.org/10.1659/MRD-JOURNAL-D-18-00104.1
Renard, Q., Ṕlissier, R., Ramesh, B. R., & Kodandapani, N. (2012). Environmental susceptibility model for predicting forest fire occurrence in the Western Ghats of India. International Journal of Wildland Fire, 21(4), 368–379. https://doi.org/10.1071/WF10109
Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 15(3), 234–281. https://doi.org/10.1016/0022-2496(77)90033-5
Strydom, S., & Savage, M. J. (2016). A spatio-temporal analysis of fires in South Africa. South African Journal of Science, 112(11–12), 1–8. https://doi.org/10.17159/sajs.2016/20150489
Vallejo-Villalta, I., Rodríguez-Navas, E., & Márquez-Pérez, J. (2019). Mapping forest fire risk at a local scale—A case study in Andalusia (Spain). Environments - MDPI, 6(3). https://doi.org/10.3390/environments6030030
Van Hoang, T., Chou, T. Y., Fang, Y. M., Nguyen, N. T., Nguyen, Q. H., Canh, P. X., Toan, D. N. B., Nguyen, X. L., & Meadows, M. E. (2020). Mapping forest fire risk and development of early warning system for NW vietnam using AHP and MCA/GIS methods. Applied Sciences (Switzerland), 10(12), 1–19. https://doi.org/10.3390/app10124348
Vilar, L., Gómez, I., Martínez-Vega, J., Echavarría, P., Riaño, D., & Martín, M. P. (2016). Multitemporal modelling of socio-economic wildfire drivers in central Spain between the 1980s and the 2000s: Comparing generalized linear models to machine learning algorithms. PLoS ONE, 11(8), 1–17. https://doi.org/10.1371/journal.pone.0161344
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Journal of Geomatics
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.