Abstract:

LiDAR (light detection and ranging) is an active remote sensing technique, LiDAR instruments measure the roundtrip time for a pulse of laser energy to travel between the sensor and a target, provide a distance or range from the instrument to the object. In this research individual tree height was measured by combining LiDAR and aerial digital camera imagery. According to the terrain, vegetation and other things of the surface，the LiDAR point clouds were filtered and classified. The Tin Filter algorithm was used for the extraction of ground points and vegetation points. Object-oriented methods were used to identify the single tree in digital camera imagery. Multi-scale and canopy mode were applied in the segmentation, and the nearest neighbors and member function methods were the main classification algorithm, and the edges of canopy polygons were optimized for improving the precision of single tree identification. The classified vegetation points were overlapped with the canopy polygons, and the max height in the polygon was calculated for the correlation with ground measured tree heights. The regression equation was established, and the mean evaluation precision was 74.89％.

Key words: LiDAR, aerial digital camera imagery, multi-scale segmentation, tree height