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In 2016, Hölbling partnered with New Zealand's Landcare Research, a Crown Research Institute headquartered in Lincoln, to test an OBIA, semi-automated approach for identifying and mapping landslide-prone "hotspots" based on historical and recent aerial photography. The team selected an approximately 1,000-hectare (2,470-acre) study area located in New Zealand's North Island, where rain-triggered landslides are common. They acquired five orthophotos—three black and white and two natural color—from five different years between 1944 and 2011. The color orthophotos had nominal accuracies of 15 m and a spatial resolution of up to 0.4 m. They also obtained a 15 m resolution DEM to provide ancillary data such as slope information. To adequately compare the OBIA approach to the manual approach, Landcare researchers spent two weeks manually digitizing visible landslides on each orthophoto in ESRI ArcGIS. In parallel, Hölbling spent one day preparing the eCognition software for integrating the datasets and classifying the landslides. Using the 2011 orthophoto, he first developed a customized rule set that used spectral, spatial, contextual and morphological properties to properly classify and map all detected landslides on the image. He then applied that rule set—with a few modifications—to the other orthophotos. In a few hours, eCognition classified all visible landslides across all five time stamps. With both the manual and automated mapping completed, the team compared the two approaches. Although the eCognition mapping was equivalent to the manual results, the eCognition approach was considerably faster, more consistent, more objective, and it was easily repeatable. "The manual mapping was painfully slow," said Harley Betts, a researcher with the soils and landscapes team at Landcare Research's Palmerston North office. "The eCognition approach has the potential to cut out a big chunk of that manual stage. I was very impressed with that." The Iceland study aims to take that rapid-mapping approach to a deeper level by pairing optical satellite imagery with InSAR datasets to create a more powerful, integrated landslide tool. Targeting a site in Iceland's southeastern Öræfajökull region, the team acquired a 5 m resolution optical RapidEye image, a 2-m-resolution LiDAR-derived DEM and two 3 m resolution TerraSAR-X StripMap scenes. In addition to calculating a vegetation index from the optical image and slope values from the DEM, they used the two SAR scenes to calculate the phase differ-ence between the two images, which helps identify areas on the ground surface that have moved. Hölbling and his team developed an eCognition rule set to integrate the imagery and InSAR data information to identify and map all landslides as well as to test its ability to distinguish both shal-low and deep- seated landslides not visible on the optical imagery. The software not only distin-guished landslides based on the optical image but with the additional InSAR data, it identified more potentially affected landslide areas. Taiwan Landslide (2014) South Öræfajökull

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