Development of Fire Hazard Index through Multispectral Indices and Geospatial Analysis

Wildfires are among the most destructive natural disasters due to their increased frequency, intensity, and duration. Countries in Southeast Asia (SEA), which primarily have tropical climates, have seen a few wildfire incidents in recent decades. To manage ecosystems and minimise disasters, effective surveillance systems are necessary. Thus, this paper aimed to derive a Fire Hazard Index (FHI) using multispectral indices through geospatial analysis, in parallel with the objectives of ascertaining the correlation coefficient using the Multiple Linear Regression (MLR) method and identifying the wildfire hazard zone in Perlis. Utilising high-resolution satellite imagery from Landsat 8, various significant spectral indices, including Land Surface Temperature (LST), Normalised Difference Vegetation Index (NDVI), Normalised Difference Moisture Index (NDMI), Normalised Difference Water Index (NDWI), Normalised Difference Drought Index (NDDI), and Normalised Burn Ratio (NBR), were combined and analysed. The correlation coefficients for the spectral indices used to develop the index were calculated using the Ordinary Least Squares (OLS) tool in ArcMap 10.5. The resultant coefficients were used to develop the FHI, and the wildfire hazard index for Perlis state was calculated using the Raster Calculator tool. The result indicates that the combined multispectral indices have a high predictive accuracy of 71% (R2 = 0.71). The results of the hazard zone identification in Perlis, Malaysia, determined using our developed FHI model, reveal that the very high hazard zone was concentrated in the Kangar area during hot, dry weather. The resulting FHI map is an adaptable tool that local authorities may use to monitor long-term ecological recovery and establish wildfire mitigation strategies.

Fire Hazard Index, Hazard Assessment, Geospatial Analysis, Multispectral Indices, Remote Sensing

1. Anwarzaini, A. Z., & Tarmizi, N. M.(2025). Spatiotemporal Analysis of Wildfire Occurrence in Perlis from Year 2014 To 2024. Journal of Tourism Hospitality and Environment Management, 10(42),21–38. https://doi.org/10.35631/ JTHEM.1042003 [Google Scholar] [Crossref]

2. Anucharan, T., Sroprom, T., Chaikaew, N., & Iamchuen, N. (2025). Assessing Burn Severity and Vegetation Impact Using Sentinel-2 Satellite Imagery and Geospatial Analysis in Mae Ka Subdistrict, Mueang District, Phayao, Thailand. Int. J. Geoinformatics, 21(4), 51–70. [Google Scholar] [Crossref]

3. Bandara, S., Navaratnam, S., & Rajeev, P. (2023). Bushfire Management Strategies: Current Practice, Technological Advancement and Challenges, Fire, 6(11). https://doi.org/10.3390/fire6110421. [Google Scholar] [Crossref]

4. Burapapol, K. & Nagasawa, R. (2016). Mapping Soil Moisture as an Indicator of Wildfire Risk Using Landsat 8 Images in Sri Lanna National Park, Northern Thailand, J. Agric. Sci., 8(10), 107. https://doi.org/10.5539/jas.v8n10p107. [Google Scholar] [Crossref]

5. Chew, Y. J., Ooi, S. Y., Pang, Y. H., and Wong, K. S. (2022). Trend Analysis of Forest Fire in Pahang, Malaysia from 2001-2021 with Google Earth Engine Platform, J. Logist. Informatics Serv. Sci., 9(4), 15–26. https://doi.org/10.33168/LISS.2022.0402. [Google Scholar] [Crossref]

6. Chew, Y. J., Ooi, S. Y., Pang, Y. H., and Lim, Z. Y. (2023). 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, Int. J. Adv. Sci. Eng. Inf. Technol., 13(5), 1733–1741. https://doi.org/10.18517/ijaseit.13.5.19026. [Google Scholar] [Crossref]

7. Fisal, N. S. M., Lintangah, W., and Ismenyah, M. (2017). Community Awareness & Challenges in Forest Fire Prevention: a Case Study At Peat Swamp Forest, Klias Forest Reserve, Beaufort, Sabah, Malaysia., Int. J. Agric. For. Plant., 5, 86–91. [Google Scholar] [Crossref]

8. Jamaruppin, M. E., Bayuaji, L., Ab Ghani, N., Rahman, M. A., Akashah, F. W., & Shah, A. (2016). Forest Fire Occurrence Analysis Based on Land Brightness Temperature using Landsat Data (Study Area: Jalan Kuantan–Pekan, Pahang, Malaysia), Natl. Conf. Postgrad. Res., 798–805. [Google Scholar] [Crossref]

9. Liu, D. & Zhang, Y. (2015). Research of regional forest fire prediction method based on multivariate linear regression, Int. J. Smart Home, 9(1), 13–22, https://doi.org/10.14257/ijsh.2015.9.1.02. [Google Scholar] [Crossref]

10. Mat Deli, M. (2023). Forest Fire Incident Forecasting System In Permanent Reserved Forest In Peninsular Malaysia Using Big Data Analytics, Sustain. Energy Environ. Rev., 1(1), 1–13. https://doi.org/10.59762/seer924712041120231103143053. [Google Scholar] [Crossref]

11. Moisa, M. B. et al. (2023). Analysing the correlation of forest and wetland with land surface temperature by using geospatial technology: a case of Yayo district, Southwestern Ethiopia, Geocarto Int., 38(1), https://doi.org/10.1080/10106049.2023.2256300. [Google Scholar] [Crossref]

12. Nguyen, T. T. N. & Tong, T. H. (2023). Wildfire Risks In The Southwest Of Ky Son District, Nghe An Province – A Multi-Critical Model, J. Sci. Tech., 7–18, https://doi.org/10.56651/lqdtu.jst.v6.n01.662.sce. [Google Scholar] [Crossref]

13. Rendana, M. et al. (2023). Current and future land fire risk mapping in the southern region of Sumatra, Indonesia, using CMIP6 data and GIS analysis, SN Appl. Sci., 5(8). https://doi.org/10.1007/s42452-023-05432-6. [Google Scholar] [Crossref]

14. Tarmizi, N. M., & Rizwan, N. I. (2024). Integrated Supervised Classification of LULC in Identifying Musang King Durian Illegal Farming Location. Jurnal Sylva Lestari, 12(2), 459–479. https://doi.org/10.23960/jsl.v12i2.856 [Google Scholar] [Crossref]

15. Tarmizi, N. B. M., Anwarzaini, A. Z. B., & Fisol, M. F. B. M. (2025, August). Maximum Likelihood Classifier Analysis of UAV Image Classification Post-Fire Area Estimation. In 2025 IEEE 16th Control and System Graduate Research Colloquium (ICSGRC)(pp. 222–227). IEEE. [Google Scholar] [Crossref]

16. Thangavel, K. et al. (2023). Autonomous Satellite Wildfire Detection Using Hyperspectral Imagery and Neural Networks: A Case Study on Australian Wildfire, Remote Sens., 15 (3). https://doi.org/10.3390/rs15030720. [Google Scholar] [Crossref]

17. Zahran, E. S. M. M., Shams, S., & Said, S. N. M. B. M. (2020). Validation of forest fire hotspot analysis in GIS using forest fire contributory factors, Syst. Rev. Pharm., 11(12), 249–255. https://doi.org/10.31838/srp.2020.12.40. [Google Scholar] [Crossref]

• Geophysical Investigation for Marl Exploration Using Vertical Electrical Sounding in Akpokponke Ibii Afikpo Southeast Nigeria
• Logging Data-Driven Geomechanical Parameter Estimation Using Advanced Machine Learning Techniques
• Seismic Refraction Tomography for Engineering Site Characterization in Awka, Southeastern Nigeria
• Design of a Competency Framework for BIM-Based Collaboration among Construction Professionals in Delta State
• Aeromagnetic Investigation of the Subsurface Sturctures in Parts of Niger Delta, Nigeria