Abstract
Accurately predicting lava flow path behavior is critical for active crisis management operations. The advance and emplacement of pāhoehoe flows modifies and inverts pre-existing topography, prompting the need for rapid and accurate updates to the topographic models used to forecast flow paths. The evolution and velocity of pāhoehoe flows are dependent on macro and micro topography, the slope of the descent path, effusion rate, and rheology. During the 2014–2015 Pāhoa crisis on the island of Hawai‘i, we used a low-altitude unmanned aerial system (UAS) to quickly and repeatedly image the active front of a slowly advancing pāhoehoe lava flow. This imagery was used to generate a series of 1 m resolution bare-earth digital elevation models (DEMs) and associated paths of steepest descent over the study area. The spatial resolution and timeliness of these UAS-derived models are an improvement over the existing topographic data used by managers during the crisis. Results from a stepwise resampling experiment suggest that the optimum DEM resolution for generating accurate pāhoehoe flow paths through lowland tropical forest environments is between 1 and 3 m. Our updated models show that future flows in this area will likely be deflected by these newly emplaced flows, possibly threatening communities not directly impacted by the original 2014–2015 lava flow. We demonstrate the value of deploying UAS during a dynamic volcanic crisis and suggest that this technology can fill critical monitoring gaps for Kīlauea and other active volcanoes worldwide.
Highlights
Pāhoehoe lava flows pose major threats to communities living near active basaltic volcanoes worldwide (Hamilton et al 2013; Del Negro et al 2016)
We present work done to generate pāhoehoe lava flow paths, based on high resolution topographic models extracted from unmanned aerial system (UAS) imagery collected over the June 27th lava flow during the 2014– 2015 eruption event near Pāhoa on the island of Hawai‘i. In coordination with Hawai‘i County Civil Defense (HCCD) and the U.S Geological Survey Hawaii Volcano Observatory (HVO), we mapped the pre- and post-flow topography and developed a computational workflow to merge multiple Digital Elevation Models (DEMs), filter them of vegetation, and generate projected paths of steepest descent
A key component of this study was to determine how well the UAS-derived DEMs performed in terms of characterizing the landscape and supporting lava hazard predictions
Summary
Pāhoehoe lava flows pose major threats to communities living near active basaltic volcanoes worldwide (Hamilton et al 2013; Del Negro et al 2016). Digital Elevation Models (DEMs) are the primary data layer used in models to estimate future lava flow paths and provide flow hazard assessments. The accuracy of the modeled results, either from the paths of steepest descent method (Kauahikaua 2007) or other physics-based lava flow models (e.g., FLOWGO, SCIARA, DOWNFLOW, MAGFLOW), Turner et al Journal of Applied Volcanology (2017) 6:17 depends strongly on how well the DEM represents the physical environment, which can be difficult to determine in heavily vegetated areas (Harris and Rowland 2001; Crisci et al 2004; Favalli et al 2005; Negro et al 2008). As lava flows change the landscape, subsequent flows will travel along new paths of steepest descent, requiring updated DEMs to reflect the dynamic environment. As lava flows change the landscape, subsequent flows will travel along new paths of steepest descent, requiring updated DEMs to reflect the dynamic environment. (Kauahikaua 2007; Favalli et al 2009)
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