Intan Nuni Wahyuni
ABSTRACT
Forest and land fires cause various negative impacts on human life and the environment. Understanding the fire spread over an area is important for developing a mitigation strategy. This paper aims to model the fire spread in Kalimantan using Cellular Automata. Simulations of the effects of land cover, terrain, and wind on fire spread during September 2015 are presented. Our results reveal that the proposed model can describe fire spread reliably well, showing that the most impacted area occurs in the southern part of Kalimantan. For the wind effect, its direction to the north is seemingly the dominant direction for the fire spread at most locations and affects fire spreads much faster in lowlands than in other locations. The present study also summarizes that the type of land cover gives more influence to the fire spread rate on lowlands, while the elevation contributes more to accelerating the fire spread over complex terrain.
- R N Adi, E Savitri, P B Putra, and Y Indrajaya. 2021. Water Balance of Various Peatland Typologies in Central Kalimantan. IOP Conference Series: Earth and Environmental Science 874, 1 (Oct. 2021), 012002. https://doi.org/10.1088/1755-1315/874/1/012002Google Scholar
- H D Bhakti, H Ibrahim, F Fristella, and M Faisal. 2020. Fire spread simulation using cellular automata in forest fire. IOP Conference Series: Materials Science and Engineering 821, 1 (April 2020), 012037. https://doi.org/10.1088/1757-899X/821/1/012037Google ScholarCross Ref
- Megan E. Cattau, Mark E. Harrison, Iwan Shinyo, Sady Tungau, María Uriarte, and Ruth DeFries. 2016. Sources of anthropogenic fire ignitions on the peat-swamp landscape in Kalimantan, Indonesia. Global Environmental Change 39 (July 2016), 205–219. https://doi.org/10.1016/j.gloenvcha.2016.05.005Google ScholarCross Ref
- Unna Chokkalingam, Iwan Kurniawan, and Yayat Ruchiat. 2005. Fire, Livelihoods, and Environmental Change in the Middle Mahakam Peatlands, East Kalimantan. Ecology and Society 10, 1 (May 2005). https://doi.org/10.5751/ES-01320-100126Google Scholar
- Soni Darmawan, Dewi Kania Sari, Ketut Wikantika, Anggun Tridawati, Rika Hernawati, and Maria Kurniawati Sedu. 2020. Identification before-after Forest Fire and Prediction of Mangrove Forest Based on Markov-Cellular Automata in Part of Sembilang National Park, Banyuasin, South Sumatra, Indonesia. Remote Sensing 12, 22 (Nov. 2020), 3700. https://doi.org/10.3390/rs12223700Google ScholarCross Ref
- Robert D. Field, Guido R. van der Werf, Thierry Fanin, Eric J. Fetzer, Ryan Fuller, Hiren Jethva, Robert Levy, Nathaniel J. Livesey, Ming Luo, Omar Torres, and Helen M. Worden. 2016. Indonesian fire activity and smoke pollution in 2015 show persistent nonlinear sensitivity to El Niño-induced drought. Proceedings of the National Academy of Sciences 113, 33 (Aug. 2016), 9204–9209. https://doi.org/10.1073/pnas.1524888113Google ScholarCross Ref
- M.D Flannigan, B.J Stocks, and B.M Wotton. 2000. Climate change and forest fires. Science of The Total Environment 262, 3 (Nov. 2000), 221–229. https://doi.org/10.1016/S0048-9697(00)00524-6Google ScholarCross Ref
- Kristina Fryanova and Valeriy Perminov. 2017. Impact of forest fires on buildings and structures. (2017). https://doi.org/10.18720/MCE.75.2Google Scholar
- Maryam Ghodrat, Farshad Shakeriaski, David James Nelson, and Albert Simeoni. 2021. Existing Improvements in Simulation of Fire–Wind Interaction and Its Effects on Structures. Fire 4, 2 (May 2021), 27. https://doi.org/10.3390/fire4020027Google ScholarCross Ref
- J. G. Goldammer and B. Seibert. 1990. The Impact of Droughts and Forest Fires on Tropical Lowland Rain Forest of East Kalimantan. In Fire in the Tropical Biota: Ecosystem Processes and Global Challenges, Johann Georg Goldammer (Ed.). Springer, Berlin, Heidelberg, 11–31. https://doi.org/10.1007/978-3-642-75395-4_2Google ScholarCross Ref
- Elisa Guelpa, Adriano Sciacovelli, Vittorio Verda, and Davide Ascoli. 2016. Faster prediction of wildfire behaviour by physical models through application of proper orthogonal decomposition. International Journal of Wildland Fire 25, 11 (2016), 1181. https://doi.org/10.1071/WF15150Google ScholarCross Ref
- L. Hernández Encinas, S. Hoya White, A. Martín del Rey, and G. Rodríguez Sánchez. 2007. Modelling forest fire spread using hexagonal cellular automata. Applied Mathematical Modelling 31, 6 (June 2007), 1213–1227. https://doi.org/10.1016/j.apm.2006.04.001Google ScholarCross Ref
- Ishardina C. Hidayati, Novinda Nalaratih, Ayu Shabrina, Intan N. Wahyuni, and Arnida L. Latifah. 2020. Correlation of Climate Variability and Burned Area in Borneo using Clustering Methods. Forest and Society 4, 2 (July 2020). https://doi.org/10.24259/fs.v4i2.9687Google ScholarCross Ref
- V. Huijnen, M. J. Wooster, J. W. Kaiser, D. L. A. Gaveau, J. Flemming, M. Parrington, A. Inness, D. Murdiyarso, B. Main, and M. van Weele. 2016. Fire carbon emissions over maritime southeast Asia in 2015 largest since 1997. Scientific Reports 6, 1 (May 2016), 26886. https://doi.org/10.1038/srep26886Google ScholarCross Ref
- BAPPENAS ICCTF. 2022. Peatland Mapping Using Active and Passive Satellite Imagery. https://peta.lcdi-indonesia.id/Google Scholar
- Ioannis Karafyllidis and Adonios Thanailakis. 1997. A model for predicting forest fire spreading using cellular automata. Ecological Modelling 99, 1 (June 1997), 87–97. https://doi.org/10.1016/S0304-3800(96)01942-4Google ScholarCross Ref
- K Kita, M Fujiwara, and S Kawakami. 2000. Total ozone increase associated with forest fires over the Indonesian region and its relation to the El Niño-Southern oscillation. Atmospheric Environment 34, 17 (Jan. 2000), 2681–2690. https://doi.org/10.1016/S1352-2310(99)00522-1Google ScholarCross Ref
- Arnida L. Latifah, Ayu Shabrina, Intan N. Wahyuni, and Rifki Sadikin. 2019. Evaluation of Random Forest model for forest fire prediction based on climatology over Borneo. In 2019 International Conference on Computer, Control, Informatics and its Applications (IC3INA). IEEE, Tangerang, Indonesia, 4–8. https://doi.org/10.1109/IC3INA48034.2019.8949588Google ScholarCross Ref
- Mark G. Lawrence. 2005. The Relationship between Relative Humidity and the Dewpoint Temperature in Moist Air: A Simple Conversion and Applications. Bulletin of the American Meteorological Society 86, 2 (Feb. 2005), 225–234. https://doi.org/10.1175/BAMS-86-2-225Google ScholarCross Ref
- K. Mahata, A. Sarkar, R. Das, and S. Das. 2017. Fuzzy evaluated quantum cellular automata approach for watershed image analysis. In Quantum Inspired Computational Intelligence. Elsevier, 259–284. https://doi.org/10.1016/B978-0-12-804409-4.00008-5Google Scholar
- Jukka Miettinen, Chenghua Shi, and Soo Chin Liew. 2017. Fire Distribution in Peninsular Malaysia, Sumatra and Borneo in 2015 with Special Emphasis on Peatland Fires. Environmental Management 60, 4 (Oct. 2017), 747–757. https://doi.org/10.1007/s00267-017-0911-7Google ScholarCross Ref
- Timothy Neale. 2015. Scientific Knowledge and Scientific Uncertainty in Bushfire and Flood Risk Mitigation: Literature Review.Google Scholar
- Sri Nurdiati, Ardhasena Sopaheluwakan, and Pandu Septiawan. 2021. Spatial and Temporal Analysis of El Niño Impact on Land and Forest Fire in Kalimantan and Sumatra. Agromet 35, 1 (Jan. 2021), 1–10. https://doi.org/10.29244/j.agromet.35.1.1-10Google ScholarCross Ref
- Ignatius Agung Prasetyoko, Yetrie Ludang, Prof Heriamariaty, and Andria Elia Embang. 2020. Studies on the Causes of Forest and Land Fires in the Palm Oil Plantation in Central Kalimantan Province. https://papers.ssrn.com/abstract=3628112Google Scholar
- Dedi Rosadi, Widyastuti Andriyani, Deasy Arisanty, and Dina Agustina. 2020. Prediction of Forest Fire Occurrence in Peatlands using Machine Learning Approaches. In 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems (ISRITI). IEEE, Yogyakarta, Indonesia, 48–51. https://doi.org/10.1109/ISRITI51436.2020.9315359Google Scholar
- Xiaoping Rui, Shan Hui, Xuetao Yu, Guangyuan Zhang, and Bin Wu. 2018. Forest fire spread simulation algorithm based on cellular automata. Natural Hazards 91, 1 (March 2018), 309–319. https://doi.org/10.1007/s11069-017-3127-5Google ScholarCross Ref
- Muhammad Riza Saputra, Deasy Arisanty, and Sidharta Adyatma. 2021. Tingkat Kerawanan Kebakaran Hutan Dan Lahan Di Banjarbaru Provinsi Kalimantan Selatan. Jambura Geoscience Review 3, 2 (June 2021), 57–64. https://doi.org/10.34312/jgeosrev.v3i2.5648Google ScholarCross Ref
- Saryono Saryono, Senjavi Rakhmana, Fitri Rahayu, Aulia Ardhi, Rusli Rusli, Nova Wahyu Pratiwi, and Titania T. Nugroho. 2017. Molecular identification of endophytic fungi isolated from the tuber of Dahlia variabilis and exploration of their ability in producing ß-galactosidase. Biodiversitas Journal of Biological Diversity 18, 1 (Feb. 2017). https://doi.org/10.13057/biodiv/d180120Google ScholarCross Ref
- KLHK Sipongi. 2022. Recapitulation of Land and Forest Fire Areas (Ha) Per Province in Indonesia. https://sipongi.menlhk.go.id/Google Scholar
- Liyang Sun, Congcong Xu, Yanglangxing He, Yanjun Zhao, Yuan Xu, Xiaoping Rui, and Hanwei Xu. 2021. Adaptive Forest Fire Spread Simulation Algorithm Based on Cellular Automata. Forests 12, 11 (Nov. 2021), 1431. https://doi.org/10.3390/f12111431Google ScholarCross Ref
- Aurore Vergnoux, Laure Malleret, Laurence Asia, Pierre Doumenq, and Frederic Theraulaz. 2011. Impact of forest fires on PAH level and distribution in soils. Environmental Research 111, 2 (Feb. 2011), 193–198. https://doi.org/10.1016/j.envres.2010.01.008Google ScholarCross Ref
- Intan Nuni Wahyuni, Ayu Shabrina, and Arnida Lailatul Latifah. 2021. Investigating Multivariable Factors of the Southern Borneo Forest and Land Fire based on Random Forest Model. In The 2021 International Conference on Computer, Control, Informatics and Its Applications. ACM, Virtual/online conference Indonesia, 71–75. https://doi.org/10.1145/3489088.3489115Google ScholarDigital Library
- Wahyunto, S Ritung, and H Subagjo. 2003. Peta Luas Sebaran Lahan Gambut Dan Kandungan Karbon Di Pulau Sumatera 1990-2002(1 ed.). Wetlands International - Indonesia Programme & Wildlife Habitat Canada (WHC).Google Scholar
- Zhengfei Wang. 1992. Current Forest Fire Danger Rating System (Cffdrs). Fire Safety Science 1(1992), 121–125.Google Scholar
- Mao Xianmin. 1993. The Influence of Wind and Relief on the Speed of the Forest Fire Spreading. Journal of Applied Meteorological Science 4, 1 (Feb. 1993), 100–104. http://qikan.camscma.cn/en/article/id/19930119Google Scholar
Recommendations
Spatial Distribution of Surface Temperature and Land Cover: A Study Concerning Sardinia, Italy
Computational Science and Its Applications – ICCSA 2020AbstractLand surface temperature (LST) is a key climate variable that has been studied mainly at the urban scale and in the context of urban heat islands. By analyzing the connection between LST and land cover, this study shows the potential of LST to ...
Modelling land-use changes using a novel vector-based geographic cellular automata
MOAS'07: Proceedings of the 18th conference on Proceedings of the 18th IASTED International Conference: modelling and simulationCellular automata (CA) models have been increasingly used to simulate land-use changes due to their computational simplicity and their explicit representation of space and time. Typically, these models use the raster model, as defined in Geographic ...
Land Cover of Erhai Area Based on High-Resolution Remote Sensing Image Spatial Distribution and Its Changes (2000-2010)
ISDEA '15: Proceedings of the 2015 Sixth International Conference on Intelligent Systems Design and Engineering ApplicationsErhai lake area is one of the important plateau lakes and ecological fragile areas buzzed with frequent human activities. Technical means such as GIS and remote sensing are used to comprehensively understand and monitor the space Distribution and Its ...
Comments