Quantitative assessment of Hurricane Ian’s damage on urban vegetation dynamics utilizing Landsat 9 in Fort Myers, Florida

Florida’s unique climatic and geographical features have profoundly influenced its hurricane history. This study quantitatively examines the effects of Hurricane Ian on urban vegetation in Fort Myers, Florida, using remote sensing data. We analyzed pre- and post-hurricane vegetation indices, including NDVI (Normalized Difference Vegetation Index), ARVI (Atmospherically Resistant Vegetation Index), and SAVI (Soil-Adjusted Vegetation Index). Our findings reveal varied spatial impacts, with NDVI changes ranging from −0.03 to 0.333, ARVI changes from −0.016 to 0.25, and SAVI changes from −0.04 to 0.5. Negative values indicate vegetation damage, while positive values suggest resilience or recovery. The study area experienced a 63.75% reduction in vegetation cover, from 67.10 km2 before Hurricane Ian to 24.325 km2 after. Pre-hurricane NDVI ranged from −0.2298 to 0.5663, while post-hurricane values ranged from −0.189 to 0.521, indicating overall vegetation stress. ARVI maxima decreased from 0.379 to 0.352, and SAVI maxima from 0.849 to 0.782, further confirming vegetation damage. Support Vector Machine classification achieved 89% accuracy (Kappa = 0.85) for pre-hurricane and 87% (Kappa = 0.83) for post-hurricane vegetation mapping. These findings enhance our understanding of hurricane impacts on urban green infrastructure, with significant implications for urban planning and disaster preparedness in coastal cities prone to extreme weather events. The outcomes enhance damage assessment methodologies and provide valuable insights into the ecological consequences of hurricanes on urban ecosystems. •Multi-index approach reveals complex hurricane impact on urban vegetation.•Machine learning achieves 89% accuracy in vegetation damage assessment.•63.75% reduction in Fort Myers' urban vegetation post-Hurricane Ian.•Remote sensing enhances urban disaster resilience assessment.•Findings inform targeted urban green infrastructure planning.