Book Description
Recent advances in remote sensing data and technology have allowed for computational models to be designed that successfully extract landforms from the landscape. The goal of this work is to create one such semi-automated model to extract deep-seated landslides located in complex geomorphic terrain. This is accomplished using geographic object-based image analysis (GEOBIA) techniques, considered by leaders in the field of image analysis to have an advantage over traditional automated classification methods. GEOBIA methods can mimic human visual interpretation by including more characteristic features used to assess the relationship between image data and the ground surface such as color reflectance (spectral), texture, shadow, location, pattern, height, tone, context, size, and shape. The standard method for identifying and mapping landslides in the Pacific Northwest is for professional geologists to manually delineate landform features using remote sensing data, referred to as remote mapping. The method is currently employed by United States Geological Survey (USGS), Washington State Department of Natural Resources (WA DNR), and Oregon Department of Geology and Mineral Industries (DOGAMI). The question remains if semi-automated models can perform as well as independent manual mappers when identifying landslides, while reducing bias due to interpretation discrepancies between mappers. To test this hypothesis, two modeled landslide datasets are created. The first, using a model design that was not influenced by manual mapping efforts, and the second created using manually-mapped landslides for visual reference. These two modeled datasets are then compared to a manually-mapped landslide inventory, created with input from four professional geomorphologists. Differences in landslide numbers, densities, geometries, and extents, that were delineated by the geologists, reflected the range of professional backgrounds. The data suggest the model is objectively using a set of morphometric characteristics to map the landslides, while the professional geomorphologists have developed interpretation style biases that lead to a large range in area mapped as a landslide. Incorrectly identifying terrain as stable could have negative impacts on public safety, suggesting more research is necessary to determine the true population of landslides that exist on the landscape. Automated models can be useful with that effort.
