An approach to automatically co-register 3-D point cloud surfaces from Unmanned Aerial Systems (UASs) and Light Detection and Ranging (LiDAR) systems is presented. A 3-D point cloud co-registration method is proposed to automatically compute all transformation parameters without the need for initial, approximate values. The approach uses a pair of point cloud height map images for automated feature point correspondence. Initially, keypoints are extracted on the height map images, and then a

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... polar descriptor is used as an attribute for matching the keypoints via a Euclidean distance similarity measure. Our study area is the Peace-Athabasca Delta (PAD) situated in north-eastern Alberta, Canada. The PAD is a world heritage site, therefore regular monitoring of this wetland is important. Our method automatically co-registers UAS point clouds with airborne LiDAR data collected over the PAD. Together with UAS data acquisition, our approach can potentially be used in the future to facilitate automated co-registration of heterogeneous data throughout the PAD region. Reported transformation parameter accuracies are: a scale error of 0.02, an average rotation error of 0.123° and an average translation error of 0.237m.