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This paper proposes a change detection approach that uses a low-resolution octree enhanced with Gaussian kernels to describe free and occupied space. This so-called Gaussian Occupancy Octree is derived from range measurements and used to represent spatial information for a single epoch. Changes between epochs are encoded using a Delta Octree. A qualitative and quantitative evaluation of the proposed approach shows that its advantages are a fast runtime and the ability to make a statement about the re-exploration of space. An evaluation of the classification accuracy shows that our approach tents towards correct classifications with an overall accuracy of 51.5 %, but is also systematically biased towards the appearance of occupied space.