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Repeat aerial photography is used to obtain closely spaced measurements of velocity and elevation over a complete transect of Ice Stream tributary B2, including the shear margins, the fast ice of the ice stream and several unusual features, as well as the UpB camp. Persistent features, mainly crevasses, are tracked to provide 1541 values of velocity and 1933 values of elevation. These are used to describe ice flow in the ice stream. Within the ice stream, the dominant velocity gradient is lateral shear. Crevasse patterns are studied in relation to measured velocity gradients. Crevasses intersect one another at acute angles, indicating that their origin is deeper than the depth to which crevasses penetrate. One feature within the ice stream seems to be a raft of stiff ice. Others are crevasse trains. Also, there are spreading ridges, perhaps due to upwelling ice. There is no evidence of large sticky spots within the studied transect, i.e. no steep surface slopes with associated surface stretching just up-glacier and surface compression down-glacier.

Lidar (light detection and ranging) data implicitly contains abundant three‐dimensional spatial information. The segmentation of lidar point clouds is the key procedure for transforming implicit spatial information into explicit spatial information. Common criteria used for point cloud segmentation are proximity and coherence of point distribution. An effective segmentation algorithm may apply various steps or combinations of criteria depending on the application. This paper proposes a four‐step segmentation method for lidar point clouds to deliver incremental segmentation results. Segmentation results of each step can provide the fundamental data for the next step. In the first step, the input point cloud is organised into an octree‐structured voxel space, in which the point neighbourhood is established. In the second step, connected voxels which are not empty are grouped to obtain grouped points based on proximity. The third step is a coplanar point segmentation based on both coherence and proximity, which was performed on each point group obtained in the second step. Finally, neighbouring coplanar point groups are merged into “co‐surface” point groups based on the criteria of plane connection and intersection. This scheme enables an incremental retrieval and analysis of a large lidar data‐set. Experimental results demonstrate the effectiveness of the segmentation algorithm in handling both airborne and terrestrial lidar data. It is anticipated that the incremental segmentation results will be useful for object modelling using lidar data.

This study demonstrated the potential of using dual-wavelength airborne light detection and ranging (LiDAR) data to classify land cover. Dual-wavelength LiDAR data were acquired from two airborne LiDAR systems that emitted pulses of light in near-infrared (NIR) and middle-infrared (MIR) lasers. The major features of the LiDAR data, such as surface height, echo width, and dual-wavelength amplitude, were used to represent the characteristics of land cover. Based on the major features of land cover, a support vector machine was used to classify six types of suburban land cover: road and gravel, bare soil, low vegetation, high vegetation, roofs, and water bodies. Results show that using dual-wavelength LiDAR-derived information (e.g., amplitudes at NIR and MIR wavelengths) could compensate for the limitations of using single-wavelength LiDAR information (i.e., poor discrimination of low vegetation) when classifying land cover.