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.