 The study used an unmanned aerial vehicle, UAV, to collect high-resolution images of a landslide for four years at seven epochs. Structure from motion, SFM, was applied to create digital surface models, DSMs, with an accuracy of four to five centimeters in the horizontal and three to four centimeters in the vertical direction. The co-registration of subsequent DSMs was checked and corrected based on comparing non-active areas of the landslide, minimizing alignment errors to a mean of 0.07 m variables such as landslide area and leading edge slope were measured, temporal patterns were discovered, and volumetric changes of particular areas of the landslide were measured over the time series. The COSI-SORR image correlation algorithm was used to track and quantify surface movement of the landslide without ground validation. Historical aerial photographs were used to create a baseline DSM, and the total displacement of the landslide was found to be approximately 6,630 cubic meters. The study demonstrated a robust and repeatable algorithm that allows landslides dynamics to be mapped and monitored with a UAV over a relatively long time series. This article was authored by Darren Turner, Arco Lucier, and Stephen M. de Jong. We are article.tv, links in the description below.