Customer Stories

TRANSFORMING THE WAY THE WORLD WORKS overview Although the words "natural disasters" are often associated with earthquakes, flooding and hurricanes, landslides are no less destructive or deadly. They are sudden. They are swift. And perhaps most unsettling, they strike without warning. The UN Office for Disaster Risk Reduction ranks these natural hazards as the fifth most frequent and the seventh most damaging. Location NEW ZEALAND, TAIWAN, ICELAND In August 2017 alone, there were landslides in China, India, Sierra Leone, the Democratic Republic of Congo, Nepal and Bangladesh that, combined, claimed nearly 1,000 lives. They destroyed entire villages, orphaned hundreds and left hundreds missing. Such sobering, mud-caked catastrophes challenge emergency responders and landslide experts both to rapidly assess and map the extent of landslides and find adequate tools to identify high-risk areas and create planning strategies. That is perhaps why the puzzle of landslides—how to adequately define them, categorize them, detect them, map them and plan for them—has been an intriguing focus of much research since the late 1990s. With the advancement of geospatial tools such as very high-resolution satellite imagery, synthetic aperture radar interferometry (InSAR), and powerful object-based image analysis (OBIA) technology, geoscientists have been particularly interested in developing more effective solutions for landslide detection, mapping, inventorying, monitoring, and possibly, forecasting. Trimble ® eCognition ® software, the environment for object-based image analysis, has played a significant role in a host of research projects in Taiwan, Italy, Austria, New Zealand and Iceland. The overall aim of each project has been to develop new, semi-automated methods for classifying and mapping landslides. Of all of this research, two areas that hold particular promise to better map, track and possibly predict future landslides are landslide hotspot mapping and combining optical and InSAR data Based on these encouraging studies, a groundswell of possibilities may be afoot to help organizations better assess, map, prepare and plan for the unpredictable nature of landslides. CHALLENGE The complexities in efficiently and accurately identifying, mapping and inventorying landslides are many. Landslides don't have uniform behaviors or patterns; they don't always look and act the same. They are quite variable in shape and size, and they can be difficult to distinguish from manmade features such as small quarries or harvested forests. Particularly challenging is that identifying and mapping landslides is predominantly a manual process—specialists visually interpret each aerial or satellite image and manually delineate and map each landslide. This traditional work is not only tedious and slow, it also highly subjective—what is or is not a landslide is decided by the expert mapping the event. To bring better efficacy, accuracy, and possibly, predictability to mapping and monitoring landslides, landslide experts need tools that will enable them to rapidly classify landslides, map their extent and identify high- risk areas.They also need the ability to create comprehensive, detailed landslide inventories for developing location-specific, risk mitigation measures. SOLUTIONS One main objective for the landslide research community has been to create more efficient and reliable mapping frameworks for landslide- prone countries to adopt and customize to suit their needs. As part of this overall goal, Daniel Hölbling, a research scientist at the University of Salzburg, has been working since 2009 to develop such solutions through several studies in Taiwan, Italy, Austria and Iceland. Most recently, he moved closer to achieving that goal with research projects in New Zealand and Iceland, countries that are well versed in landslides.

• Cover