Potential of multisensor assessment using digital image correlation for landslide detection and monitoring

With the combination of diverse remote sensing data, one can estimate the detection capabilities of gravitational mass movement dynamics and behaviour. Recent multispectral satellite sensors such as Sentinel-2, RapidEye and PlanetScope offer unprecedented spatiotemporal resolutions, hence reducing data gaps of alpine meteorological constraints. In addition to this data, very high resolution and accurate UAV images cover a broad range of spatial resolutions. The strengths of these remote sensing systems allow the data compilation of vast, difficult and dangerous to access mountain areas. However, the limitations of the spatiotemporal resolution for (i) pre-event landslide detection, (ii) monitoring of already known mass movements and (iii) the capability to measure rapid changes (e.g.  accelerations) for warnings have not been examined extensively. Thus, there is an important need to understand the potential of multispectral images to detect, monitor, and identify rapid changes prior to landslide events to increase the forecasting window.

Digital image correlation (DIC), as indispensable tool to measure surface displacements, aids in estimating the fitness of different remote sensing images. Here, we present first results of motion delineation by DIC of the Sattelkar, a high-alpine, deglaciated and debris-laden cirque in the Obersulzbach-valley, Austria. We used comprehensive knowledge of the study site to thoroughly understand DIC motion clusters for verification purposes. We then compared three different DIC software tools, COSI-Corr, DIC‑FFT and IMCORR. They revealed similar results for the three satellite systems in terms of hot spot areas as well as noise. Our findings show large motion inaccuracies for Sentinel-2, RapidEye and PlanetScope images due to spatial resolution, poor image co-registration and changing data quality. In contrast, displacement patterns from the three UAV images (7/2018, 7/2019, 9/2019) demonstrate good positional accuracy as well as data usability for this approach. The inherited noise results from decorrelation due to high velocities suggest using an increased temporal image acquisition for further evaluation.

Reliable, precise results for landslide detection, their ongoing monitoring and the measurement capability for significant changes are necessary for targeted investigations, precautionary measures and the start of the forecasting window. Multispectral UAV images of high positional accuracy and quality are able to provide dependable relative displacement velocities and have the capability to serve as a reliable tool. On the contrary, satellite images showed delusive results, and we recommend reconsidering their deployment in future applications. The knowledge of the most suitable data in terms of accuracy and processing speed is crucial for landslide identification, monitoring and acceleration threshold detection. At present, our prelimiary findings show the capability to detect and monitor relative and mainly slow changes. The detection of rapid changes lacks due to the accuracy, resolution and revisit time of the investigated remote sensing systems.