Posters

Topographic data are fundamental to landscape characterization across the geosciences, for monitoring change and supporting process modelling. Over the last decade, the dominance of laser-based instruments for high resolution data collection has been challenged by advances in digital photogrammetry and computer vision, particularly in ‘structure from motion’ (SfM) algorithms, which offer a new paradigm to geoscientists.

High resolution topographic (HiRT) data are now obtained over spatial scales from millimetres to kilometres, and over durations of single events to lasting time series (e.g. from sub-second to decadal-duration time-lapse), allowing evaluation of dependencies between event magnitudes and frequencies. Such 4D-reconstruction capabilities enable new insight in diverse fields such as soil erosion, micro-topography reconstruction, volcanology, glaciology, landslide monitoring, and coastal and fluvial geomorphology. Furthermore, broad data integration from multiple sensors offers increasingly exciting opportunities.

This session will evaluate the advances in techniques to model topography and to study patterns of topographic change at multiple temporal and spatial scales. We invite contributions covering all aspects of HiRT reconstruction in the geosciences, and particularly those which transfer traditional expertise or demonstrate a significant advance enabled by novel datasets. We encourage contributions describing workflows that optimize data acquisition and post-processing to guarantee acceptable accuracies and to automate data application (e.g. geomorphic feature detection and tracking), and field-based experimental studies using novel multi-instrument and multi-scale methodologies. A major goal is to provide a cross-disciplinary exchange of experiences with modern technologies and data processing tools, to highlight their potentials, limitations and challenges in different environments.

Solicited speaker: Kuo-Jen Chang (National Taipei University of Technology) - UAS LiDAR data processing, quality assessment and geosciences prospects