Three-dimensional data acquired by terrestrial laser scanning (TLS) provides an accurate representation of the Earth’s surface, which is commonly used to detect and quantify topographic changes on a small scale. However, in Arctic permafrost regions TLS-based monitoring of thaw subsidence is challenging due to vegetation and the micro-topographic characteristics (e.g. dense moss-lichen layer, hummocks etc.). In this presentation, we focus, firstly, on the evaluation of raster- and point-based TLS methods for quantifying small-scale thaw subsidence within the continuous permafrost zone. Secondly, a new filter strategy is presented that reduces spatial sampling effects caused by various factors such as vegetation, micro-topography and scan-setup. Our study site is located at the Trail Valley Creek research watershed, 50 km north-east of Inuvik, Northwest Territories, Canada. Three field campaigns took place in 2015 and 2016. Besides capturing TLS data, at-point real-time kinematic (RTK) Global Navigation Satellite System (GNSS) measurements and manual subsidence measurements were gathered. To achieve a highly accurate registration (on mm-scale) of the three TLS campaigns, co-registration of the georeferenced point clouds is performed based on the stable fix points in the otherwise highly dynamic permafrost environment. Then, different methods to quantify vertical ground movements are applied and evaluated. The result reveals limitations of standard raster-based DEM differencing, but also of point-based distance calculation for detecting spatial patterns of small-scale thaw subsidence. In the Arctic tundra ecosystem, TLS-based deformation analysis is strongly affected by occlusion and spatial sampling effects, even if data acquisition is repeated from similar scan positions. We show that the mentioned errors can be reduced by capturing the ground surface from more than one TLS scan position. Our filter strategy allows to identify TLS points which are suitable for multi-temporal deformation analyses, and results in an average seasonal subsidence rate (2015/06-2015/08) of about -2.0 cm at our study site. The derived subsidence maps deliver highly accurate ground-truth data, which is needed to improve area-wide subsidence monitoring methods such as SAR interferometry. This leads to a deeper understanding of permafrost-related subsidence processes.