An urgent case for higher resolution digital elevation models in the world's poorest and most vulnerable countries

(2015) An urgent case for higher resolution digital elevation models in the world's poorest and most vulnerable countries. Front. Earth Sci. 3:50. Natural disasters disproportionately impact the poorest countries; low-income developing countries accounted for 95% of all disaster fatalities between 1970 and 2008 (Handmer et al., 2012). These impacts are generally expected to worsen as the climate changes and sea levels rise. Fortunately, we have the technology today that enables us to determine the likely deaths, damages, and losses (direct and indirect) that will result from natural hazards and climate change, and to highlight which actions will be most effective in reducing impacts on individuals, communities, and governments. This ability to understand disaster and climate losses before they happen and to provide robust analysis of the costs and benefits of preparedness, reduction, and adaptation is a powerful tool in managing disaster risks and adapting to a changing climate. One of the most critical datasets in this analytical process is a digital elevation model (DEM) (Global Facility for Disaster Reduction and Recovery, 2014). This dataset determines flow of water during a flood, the coastal inundation extent in a tsunami or storm surge, and the loss of coastal areas as the sea level rises. Until recently, one of the only open datasets in developing countries was the SRTM DEM at 90-m spatial resolution (SRTM90), but recently the US Government released the 30-m resolution version. However, major challenges with the 30-m resolution dataset include its great vertical error (up to 16 m) and its measurement of surface elevation—including treetops and building tops—not bare earth, which understates flood risk (Sanders, 2007). In contrast, developed countries increasingly are able to access bare-earth elevation datasets with vertical resolutions and accuracies at decimeter scale (e.g., typically below 0.2 m for LiDAR). Without this access to high spatial resolution and higher accuracy DEMs, decision-makers in developing countries are forced to use hazard information for planning and preparedness that is inaccurate. Indeed in many cases the use of hazard information generated on available low-resolution DEMs has been shown to be dangerously misleading. In Indonesia, tsunami models were run on three different DEMs—LiDAR, Airborne InSAR and SRTM90—by Geoscience Australia (Griffin et al., 2012). These results, when compared to actual inundation, showed that the use of LiDAR and InSAR datasets enabled an accurate prediction of the inundation extent. Unfortunately, using the SRTM90 datasets did not predict the inundation extent, and in …