Laser scanning is rapidly evolving as a surveying technique and is not only used to assess the geometrical state of a scene but also to assess changes in that state. Change detection is, however, a challenging application for several reasons. First, laser scanning is not measuring fixed points, such as a total station does, therefore in general, some interpolation or object extraction method is required. Second, errors that are inevitably present when determining the geometric state of a scene of interest in one epoch will add up when comparing the state between epochs. In addition, data volumes are constantly increasing, therefore processing methods should be computationally efficient. This paper reviews recent methodology in the form of a method breakdown, thereby distinguishing methods aiming at pure binary change detection from methods that in addition want to quantify change. In addition, the direction of a change is discussed, notably in connection with the measurement geometry. Also, the reference state is discussed, which can be in the form of a free form surface, or in the form of some idealized mathematical primitive like a plane. The different methods are presented in connection with applications in fields like structural monitoring, geomorphology, urban inventory and forestry, as considered by the original authors.@en

Identification of harvested and fallen trees is a prerequisite for the detection and measurement of changes in forests. This paper presents a three step approach to monitor harvested and fallen trees based on direct comparison of repeated high density airborne LIDAR data. In a first step differences between data sets are obtained from a point to point comparison, such that the data can be reduced to the deviating points only. Secondly, the resulting points are clustered into spatially connected regions using region growing. Finally, individual trees are extracted from the clusters by analysing their relative proximity and by analysing geometric properties of points in the clusters. Two data sets, acquired at a four year interval and covering a forest with mainly deciduous trees, are compared. First results show that most points relating to a change can be extracted and that clustering of these with region growing enables us to efficiently separate harvested and fallen trees from the remaining trees. Grouped harvested trees could not be separated using the region growing approach due to touching crowns. Segmentation of these using spectral clustering however identified individual regions well, but the results depend mainly on the pre-defined number of clusters. Crowns of grouped trees can be therefore separated if the number of trees is known.

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