Readily observable field evidence of only the largest or most recent natural disasters typically persists in temperate environments due to the constant regrowth cycle of vegetation. Ongoing work to increase predictive capabilities for natural hazard events ( Goetz et al., 2015 Guzzetti et al., 2006) rely on the robust characterization (e.g., number or spatial distribution of landslides) of modern-day events ( Xu et al., 2016 Gallen et al., 2017). As such, both Earth scientists and emergency managers have a keen interest in understanding natural disaster occurrences and their spatial extent.
Natural disasters such as landslides, wildfires, and volcanic eruptions are a primary mechanism of landscape change ( Korup et al., 2010 Santi et al., 2013) while simultaneously causing fatalities into the 21st century ( Froude and Petley, 2018 Petley, 2012 Auker et al., 2013 Holzer and Savage, 2013 Ashley and Ashley, 2008). HazMapper is intended for use by both scientists and non-scientists, such as emergency managers and public safety decision-makers. Case studies are included for the identification of landslides and debris flows, wildfires, pyroclastic flows, and lava flow inundation. HazMapper is not a semi-automated routine but makes rapid and repeatable analysis and visualization feasible for both recent and historical natural disasters. Because of the vegetation-based metric, the tool is typically not suitable for use in desert or polar regions. The first iteration of HazMapper relies on a vegetation-based metric, the relative difference in the normalized difference vegetation index (rdNDVI), to identify areas on the landscape where vegetation was removed following a natural disaster. HazMapper is an open-access hazard mapping application developed in Google Earth Engine that allows users to derive map and GIS-based products from Sentinel or Landsat datasets without the time- and cost-intensive resources required for traditional analysis. Publicly accessible multi-spectral datasets (e.g., Landsat, Sentinel-2) are particularly helpful in analyzing the spatial extent of disturbances, however, the datasets are large and require intensive processing on high-powered computers by trained analysts. Modern satellite networks with rapid image acquisition cycles allow for near-real-time imaging of areas impacted by natural hazards such as mass wasting, flooding, and volcanic eruptions.
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