Forest fire, thinning, and flood in wildland-urban interface: UAV and lidar-based estimate of natural disaster impacts

Abstract Context Wildland-urban interface (WUI) areas are facing increased forest fire risks and extreme precipitation events due to climate change, which can lead to post-fire flood events. The city of Flagstaff in northern Arizona, USA experienced WUI forest thinning, fire, and record rainfall events, which collectively contributed to large floods and damages to the urban neighborhoods and city infrastructure. Objectives We demonstrate multi-temporal, high resolution image applications from an unoccupied aerial vehicle (UAV) and terrestrial lidar in estimating landscape disturbance impacts within the WUI. Changes in forest vegetation and bare ground cover in WUIs are particularly challenging to estimate with coarse-resolution satellite images due to fine-scale landscape processes and changes that often result in mixed pixels. Methods Using Sentinel-2 satellite images, we document forest fire impacts and burn severity. Using 2016 and 2021 UAV multispectral images and Structure-from-Motion data, we estimate post-thinning changes in forest canopy cover, patch sizes, canopy height distribution, and bare ground cover. Using repeat lidar data within a smaller area of the watershed, we quantify geomorphic effects in the WUI associated with the fire and subsequent flooding. Results We document that thinning significantly reduced forest canopy cover, patch size, tree density, and mean canopy height resulting in substantially reduced active crown fire risks in the future. However, the thinning equipment ignited a forest fire, which burned the WUI at varying severity at the top of the watershed that drains into the city. Moderate-high severity burns occurred within 3 km of downtown Flagstaff threatening the WUI neighborhoods and the city. The upstream burned area then experienced 100-year and 200–500-year rainfall events, which resulted in large runoff-driven floods and sedimentation in the city. Conclusion We demonstrate that UAV high resolution images and photogrammetry combined with terrestrial lidar data provide detailed and accurate estimates of forest thinning and post-fire flood impacts, which could not be estimated from coarser-resolution satellite images. Communities around the world may need to prepare their WUIs for catastrophic fires and increase capacity to manage sediment-laden stormwater since both fires and extreme weather events are projected to increase.