Investigating the Relationship between the Influencing Fire Factors and Forest Fire Occurrence in the Districts of Rompin, Pekan, and Kuantan in the State of Pahang, Malaysia, Using Google Earth Engine

Yee Jian Chew, Shih Yin Ooi, Ying Han Pang, Zheng You Lim

Abstract


Forest fires pose a significant threat to ecosystems and human livelihoods. Understanding the role of climatic factors in fire occurrence is crucial for effective fire management and prevention. This study analyses the influences of temperature, precipitation, and wind speed on fire incidents in the districts of Rompin, Pekan, and Kuantan in Pahang, Malaysia. The investigation is motivated by newspaper articles dated early March 2021, which report that the fires in these districts were triggered by an extended period of hot and dry conditions, as highlighted by the Director of the Fire and Rescue Department of Pahang, Malaysia. However, no further investigation or detailed discussion has been deliberated. By examining the historical climatic data and fire incidents, this study aims to investigate the relationships between these climatic variables and fire occurrences. The results reveal that higher temperatures and lower precipitation are associated with increased fire susceptibility due to reduced soil moisture. In contrast, wind speed does not appear to impact fire spread significantly. These findings will undoubtedly provide valuable insights into the complex interactions between climatic variables and regional fire incidents, enabling policymakers and fire management authorities to develop targeted fire prevention and mitigation strategies.

Keywords


Forest fire; Google Earth engine; Malaysia; factors; Pahang; temperature; precipitation

Full Text:

PDF

References


B. T. Pham et al., “Performance evaluation of machine learning methods for forest fire modeling and prediction,†Symmetry (Basel)., vol. 12, no. 6, p. 1022, 2020, doi: 10.3390/sym12061022.

S. D. Chicas and J. Østergaard Nielsen, “Who are the actors and what are the factors that are used in models to map forest fire susceptibility? A systematic review,†Nat. Hazards, vol. 114, no. 3, pp. 2417–2434, 2022, doi: 10.1007/s11069-022-05495-5.

F. Abid, “A survey of machine learning algorithms based forest fires prediction and detection systems,†Fire Technol., pp. 1–32, 2020,

doi: 10.1007/s10694-020-01056-z.

P. Jain, S. C. P. Coogan, S. G. Subramanian, M. Crowley, S. Taylor, and M. D. Flannigan, “A review of machine learning applications in wildfire science and management,†Environ. Rev., vol. 28, no. 4, pp. 478–505, 2020, doi: 10.1139/er-2020-0019.

M. Naderpour, H. M. Rizeei, N. Khakzad, and B. Pradhan, “Forest fire induced Natech risk assessment: A survey of geospatial technologies,†Reliab. Eng. Syst. Saf., vol. 191, p. 106558, 2019,

doi: 10.1016/j.ress.2019.106558.

Y. J. Chew, S. Y. Ooi, Y. H. Pang, and K.-S. Wong, “A Review of Forest Fire Combating Efforts, Challenges and Future Directions in Peninsular Malaysia, Sabah, and Sarawak,†Forests, vol. 13, no. 9, p. 1405, 2022, doi: 10.3390/f13091405.

R. Bradstock, H. Clarke, L. Collins, M. Clarke, R. H. Nolan, and T. Penman, “A staggering 1.8 million hectares burned in ‘high-severity’ fires during Australia’s Black Summer,†Mar. 30, 2021. https://theconversation.com/a-staggering-1-8-million-hectares-burned-in-high-severity-fires-during-australias-black-summer-157883 (accessed Apr. 02, 2021).

M. Goss et al., “Climate change is increasing the likelihood of extreme autumn wildfire conditions across California,†Environ. Res. Lett., vol. 15, no. 9, p. 94016, 2020, doi: 10.1088/1748-9326/ab83a7.

S. D. Chicas, J. Østergaard Nielsen, M. C. Valdez, and C.-F. Chen, “Modelling wildfire susceptibility in Belize’s ecosystems and protected areas using machine learning and knowledge-based methods,†Geocarto Int., pp. 1–24, 2022,

doi: 10.1080/10106049.2022.2102231.

S. Costafreda-Aumedes, C. Comas, and C. Vega-Garcia, “Human-caused fire occurrence modelling in perspective: A review,†Int. J. Wildl. Fire, vol. 26, no. 12, pp. 983–998, 2017,

doi: 10.1071/WF17026.

E. Chuvieco and J. Salas, “Mapping the spatial distribution of forest fire danger using GIS,†Int. J. Geogr. Inf. Sci., vol. 10, no. 3, pp. 333–345, 1996, doi: 10.1080/02693799608902082.

M. A. Cochrane, “Fire science for rainforests,†Nature, vol. 421, no. 6926, pp. 913–919, 2003, doi: 10.1038/nature01437.

S. S. Hashjin, A. H. Milaghardan, A. Esmaeily, B. Mojaradi, and F. Naseri, “Forest fire hazard modeling using hybrid AHP and fuzzy AHP methods using MODIS sensor,†in 2012 IEEE International Geoscience and Remote Sensing Symposium, 2012, pp. 931–934, doi: 10.1109/IGARSS.2012.6351403.

A. L. Westerling, T. J. Brown, T. Schoennagel, T. W. Swetnam, M. G. Turner, and T. T. Veblen, “Climate and wildfire in Western US Forests,†For. Conserv. Anthr. Sci. policy, Pract., pp. 43–55, 2016, doi: 10.5876/9781607324591.c003.

S. Bravo, C. Kunst, R. Grau, and E. Aráoz, “Fire–rainfall relationships in Argentine Chaco savannas,†J. Arid Environ., vol. 74, no. 10, pp. 1319–1323, 2010, doi: 10.1016/j.jaridenv.2010.04.010.

A. D. Syphard et al., “Predicting spatial patterns of fire on a southern California landscape,†Int. J. Wildl. Fire, vol. 17, no. 5, pp. 602–613, 2008, doi: 10.1071/WF07087.

M. G. Cruz and M. E. Alexander, “The 10% wind speed rule of thumb for estimating a wildfire’s forward rate of spread in forests and shrublands,†Ann. For. Sci., vol. 76, no. 2, pp. 1–11, 2019,

doi: 10.1007/s13595-019-0829-8.

X. Li et al., “Prediction of Forest fire spread rate using UAV images and an LSTM model considering the interaction between fire and wind,†Remote Sens., vol. 13, no. 21, p. 4325, 2021,

doi: 10.3390/rs13214325.

N. Gorelick, M. Hancher, M. Dixon, S. Ilyushchenko, D. Thau, and R. Moore, “Google Earth Engine: Planetary-scale geospatial analysis for everyone,†Remote Sens. Environ., vol. 202, pp. 18–27, 2017,

doi: 10.1016/j.rse.2017.06.031.

J. T. Abatzoglou, S. Z. Dobrowski, S. A. Parks, and K. C. Hegewisch, “TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958-2015,†Sci. Data, vol. 5, pp. 1–12, 2018, doi: 10.1038/sdata.2017.191.

N. A. N. Min, “Kebakaran hutan di Jalan Pekan-Nenasi terkawal (Forest Fire in Jalan Pekan-Nanasi Controlled),†Sinar Harian, Mar. 03, 2021.

T. N. Alagesh, “40ha of Pahang forest, peat land on fire,†New Straits Times, New Straits Times Press, Feb. 26, 2019.

TheStar, “Pahang Fire Dept busy putting out forest fires,†Mar. 11, 2021.

A. Awang, “Lebih 300 hektar hutan di Pahang terbakar (More than 300 hectare of forest burnt in Pahang),†Berita Harian, Mar. 11, 2021.

Y. J. Chew, S. Y. Ooi, Y. H. Pang, and K. S. Wong, “Trend Analysis of Forest Fire in Pahang, Malaysia from 2001-2021 with Google Earth Engine Platform,†J. Logist. Informatics Serv. Sci., vol. 9, no. 4, pp. 15–26, 2022, doi: 10.33168/LISS.2022.0402.

Y. J. Chew, S. Y. Ooi, and Y. H. Pang, “MCD64A1 Burnt Area Dataset Assessment using Sentinel-2 and Landsat-8 on Google Earth Engine: A Case Study in Rompin, Pahang in Malaysia,†in 2023 IEEE 13th Symposium on Computer Applications & Industrial Electronics (ISCAIE), 2023, pp. 38–43,

doi: 10.1109/ISCAIE57739.2023.10165382.

P. Dhal and C. Azad, “A comprehensive survey on feature selection in the various fields of machine learning,†Appl. Intell., pp. 1–39, 2022, doi: 10.1007/s10489-021-02550-9.

L. M. Johnston et al., “Wildland fire risk research in Canada,†Environ. Rev., vol. 28, no. 2, pp. 164–186, 2020, doi: 10.1139/er-2019-0046.

A. Tariq et al., “Assessing burned areas in wildfires and prescribed fires with spectral indices and SAR images in the Margalla Hills of Pakistan,†Forests, vol. 12, no. 10, p. 1371, 2021,

doi: 10.3390/f12101371.

L. Vilar, D. G. Woolford, D. L. Martell, and M. P. Martín, “A model for predicting human-caused wildfire occurrence in the region of Madrid, Spain,†Int. J. Wildl. Fire, vol. 19, no. 3, pp. 325–337, 2010, doi: 10.1071/WF09030.

W. Sadok, J. R. Lopez, and K. P. Smith, “Transpiration increases under highâ€temperature stress: Potential mechanisms, tradeâ€offs and prospects for crop resilience in a warming world,†Plant. Cell Environ., vol. 44, no. 7, pp. 2102–2116, 2021, doi: 10.1111/pce.13970.

I. Setiawan, A. R. Mahmud, S. Mansor, A. R. M. Shariff, and A. A. Nuruddin, “GISâ€gridâ€based and multiâ€criteria analysis for identifying and mapping peat swamp forest fire hazard in Pahang, Malaysia,†Disaster Prev. Manag. An Int. J., 2004,

doi: 10.1108/09653560410568507.

A. Mahmud, I. Setiawan, S. Mansor, A. Shariff, B. Pradhan, and A. Nuruddin, “Utilization of geoinformation tools for the development of forest fire hazard mapping system: example of Pekan fire, Malaysia,†Open Geosci., vol. 1, no. 4, pp. 456–462, 2009, doi: 10.2478/v10085-009-0032-5.

S. M. Razali, A. A. Nuruddin, I. A. Malek, and N. A. Patah, “Forest fire hazard rating assessment in peat swamp forest using Landsat thematic mapper image,†J. Appl. Remote Sens., vol. 4, no. 1, p. 43531, 2010, doi: 10.1117/1.3430040.

P. Ismail, I. Shamsudin, and H. Khali Aziz, “Development of indicators for assessing susceptibility of degraded peatland areas to forest fires in Peninsular Malaysia,†IUFRO World Ser. Vol. 29, p. 67, 2011.

M. E. Bin Jamaruppin, L. Bayuaji, N. B. Ab Ghani, M. A. Rahman, F. W. Akashah, and A. Shah, “Forest fire occurrence analysis base on land brightness temperature using Landsat data (Study area: Jalan Kuantan–Pekan, Pahang, Malaysia),†in The National Conference for Postgraduate Research, 2016, pp. 798–805.

L. Yang, J. Driscol, S. Sarigai, Q. Wu, H. Chen, and C. D. Lippitt, “Google Earth Engine and Artificial Intelligence (AI): A Comprehensive Review,†Remote Sens., vol. 14, no. 14, 2022,

doi: 10.3390/rs14143253.

J. Suhaila, S. M. Deni, W. Z. W. Zin, and A. A. Jemain, “Trends in peninsular Malaysia rainfall data during the southwest monsoon and northeast monsoon seasons: 1975–2004,†Sains Malaysiana, vol. 39, no. 4, pp. 533–542, 2010.

J. L. Hodges and B. Y. Lattimer, “Wildland fire spread modeling using convolutional neural networks,†Fire Technol., vol. 55, no. 6, pp. 2115–2142, 2019, doi: 10.1007/s10694-019-00846-4.

G. Zhang, M. Wang, and K. Liu, “Forest fire susceptibility modeling using a convolutional neural network for Yunnan province of China,†Int. J. Disaster Risk Sci., vol. 10, no. 3, pp. 386–403, 2019,

doi: 10.1007/s13753-019-00233-1.

Y. Ban, P. Zhang, A. Nascetti, A. R. Bevington, and M. A. Wulder, “Near real-time wildfire progression monitoring with Sentinel-1 SAR time series and deep learning,†Sci. Rep., vol. 10, no. 1, pp. 1–15, 2020, doi: 10.1038/s41598-019-56967-x.

M. Xin, L. W. Ang, and S. Palaniappan, “A Data Augmented Method for Plant Disease Leaf Image Recognition based on Enhanced GAN Model Network,†J. Informatics Web Eng., vol. 2, no. 1, pp. 1–12, 2023, doi: 10.33093/jiwe.2023.2.1.1.

Y. Wang, L. Dang, and J. Ren, “Forest fire image recognition based on convolutional neural network,†J. Algorithm. Comput. Technol., vol. 13, 2019, doi: 10.1177/1748302619887689.

Z. Jiao et al., “A deep learning based forest fire detection approach using UAV and YOLOv3,†in 2019 1st International Conference on Industrial Artificial Intelligence (IAI), 2019, pp. 1–5,

doi: 10.1109/ICIAI.2019.8850815.

S. Wang, J. Zhao, N. Ta, X. Zhao, M. Xiao, and H. Wei, “A real-time deep learning forest fire monitoring algorithm based on an improved Pruned+ KD model,†J. Real-Time Image Process., pp. 1–11, 2021, doi: 10.1007/s11554-021-01124-9.

M. Xin, L. W. Ang, and S. Palaniappan, “A Multi-Scale Feature Attention Image Recognition Algorithm,†J. Informatics Web Eng., vol. 2, no. 2, pp. 1–7, Sep. 2023, doi: 10.33093/jiwe.2023.2.2.1.

J. O. Victor, X. Chew, K. W. Khaw, and M. H. Lee, “A Cost-Based Dual ConvNet-Attention Transfer Learning Model for ECG Heartbeat Classification,†J. Informatics Web Eng., vol. 2, no. 2, pp. 90–110, Sep. 2023, doi: 10.33093/jiwe.2023.2.2.7.

J. J. Ng, K. O. M. Goh, and C. Tee, “Traffic Impact Assessment System using Yolov5 and ByteTrack,†J. Informatics Web Eng., vol. 2, no. 2, pp. 168–188, Sep. 2023, doi: 10.33093/jiwe.2023.2.2.13.

Z. Y. Poo, C. Y. Ting, Y. P. Loh, and K. I. Ghauth, “Multi-Label Classification with Deep Learning for Retail Recommendation,†J. Informatics Web Eng., vol. 2, no. 2, pp. 218–232, Sep. 2023,

doi: 10.33093/jiwe.2023.2.2.16.

J.-R. Lee, K.-W. Ng, and Y.-J. Yoong, “Face and Facial Expressions Recognition System for Blind People Using ResNet50 Architecture and CNN,†J. Informatics Web Eng., vol. 2, no. 2, pp. 284–298, Sep. 2023, doi: 10.33093/jiwe.2023.2.2.20.

A. B. Abdusalomov, B. M. S. Islam, R. Nasimov, M. Mukhiddinov, and T. K. Whangbo, “An Improved Forest Fire Detection Method Based on the Detectron2 Model and a Deep Learning Approach,†Sensors, vol. 23, no. 3, p. 1512, Jan. 2023, doi: 10.3390/s23031512.

S. Khan and A. Khan, “FFireNet: Deep Learning Based Forest Fire Classification and Detection in Smart Cities,†Symmetry (Basel)., vol. 14, no. 10, p. 2155, Oct. 2022, doi: 10.3390/sym14102155.

V. E. Sathishkumar, J. Cho, M. Subramanian, and O. S. Naren, “Forest fire and smoke detection using deep learning-based learning without forgetting,†Fire Ecol., vol. 19, no. 1, p. 9, Feb. 2023,

doi: 10.1186/s42408-022-00165-0.

S. Saha, B. Bera, P. K. Shit, S. Bhattacharjee, and N. Sengupta, “Prediction of forest fire susceptibility applying machine and deep learning algorithms for conservation priorities of forest resources,†Remote Sens. Appl. Soc. Environ., vol. 29, p. 100917, Jan. 2023,

doi: 10.1016/j.rsase.2022.100917.

Y. Kang, E. Jang, J. Im, and C. Kwon, “A deep learning model using geostationary satellite data for forest fire detection with reduced detection latency,†GIScience Remote Sens., vol. 59, no. 1, pp. 2019–2035, Dec. 2022, doi: 10.1080/15481603.2022.2143872.

Y. Shao et al., “Assessment of China’s forest fire occurrence with deep learning, geographic information and multisource data,†J. For. Res., vol. 34, no. 4, pp. 963–976, Aug. 2023, doi: 10.1007/s11676-022-01559-1.

R. Ghosh and A. Kumar, “A hybrid deep learning model by combining convolutional neural network and recurrent neural network to detect forest fire,†Multimed. Tools Appl., vol. 81, no. 27, pp. 38643–38660, Nov. 2022, doi: 10.1007/s11042-022-13068-8.

H. D. Nguyen, “Hybrid models based on deep learning neural network and optimization algorithms for the spatial prediction of tropical forest fire susceptibility in Nghe An province, Vietnam,†Geocarto Int., vol. 37, no. 26, pp. 11281–11305, Dec. 2022,

doi: 10.1080/10106049.2022.2048904.

S. T. Seydi, V. Saeidi, B. Kalantar, N. Ueda, and A. A. Halin, “Fire-Net: A Deep Learning Framework for Active Forest Fire Detection,†J. Sensors, vol. 2022, pp. 1–14, Feb. 2022,

doi: 10.1155/2022/8044390.

B. Mishra, S. Panthi, S. Poudel, and B. R. Ghimire, “Forest fire pattern and vulnerability mapping using deep learning in Nepal,†Fire Ecol., vol. 19, no. 1, p. 3, Jan. 2023, doi: 10.1186/s42408-022-00162-3.

Z. Xue, H. Lin, and F. Wang, “A Small Target Forest Fire Detection Model Based on YOLOv5 Improvement,†Forests, vol. 13, no. 8, p. 1332, Aug. 2022, doi: 10.3390/f13081332.




DOI: http://dx.doi.org/10.18517/ijaseit.13.5.19026

Refbacks

  • There are currently no refbacks.



Published by INSIGHT - Indonesian Society for Knowledge and Human Development