Measuring landslide-driven ground displacements with high-resolution surface models and optical flow

This study proposes and evaluates the performance of two image matching algorithms applied to hillshades derived from consecutive high-resolution digital surface models (DSMs) to measure surface displacements on landslides. The method is applied on Te Horo, a slow-moving landslide in the Dart Valley of New Zealand's Southern Alps/Ka over bar Tiritiri o te Moana using pairs of hillshades derived from Airborne and Satellite Photogrammetric Mapping (APM/SPM) of imagery captured in 2018 and 2020. This novel approach uses the consistency of displacement predictions generated from multiple hillshade pairs to gauge match quality and mask unreliable displacement predictions. The study compares a widely used normalised cross-correlation algorithm (NCC) alongside an optical flow approach for image matching. The performances of both algorithms are assessed against manually derived displacements of prominent surface features. We demonstrate the effectiveness of the masking approach as well as the good performance of the optical flow algorithm in delivering dense, accurate displacement measurements efficiently, particularly when high resolution DSMs are available. The results show that the main translational body of the landslide was displaced at rates averaging 25-55 mm day-1 over the 2018-2020 period.

Automated summary

This study proposes and evaluates the performance of two image matching algorithms for measuring surface displacements on landslides using hillshades derived from high-resolution DSMs. The algorithms, including a widely used NCC algorithm and an optical flow approach, were applied to hillshades obtained from APM/SPM imagery captured in 2018 and 2020. The results demonstrate the effectiveness of the masking approach and the good performance of the optical flow algorithm in delivering dense, accurate displacement measurements, especially with high-resolution DSMs available.