Single tree detection in very high resolution remote sensing data

Tree detection is a major focus in the field of (semi-) automatic extraction of forest information from very high resolution remote sensing data. Many existing tree crown delineation algorithms require a set of seed pixels to start the process of image segmentation. In this study, different methods of obtaining seed pixels (semi-) automatically from orthophotos and digital surface models derived from stereo digital camera imagery are tested and compared. The UltracamD digital camera provides images with a stereo overlap of about 90% and this paper presents a new method of DSM generation based on multiple stereo pairs. The evaluation of the DSMs shows that by using a multiple image approach, also referred to as block-based approach, the quality is significantly increased: the mean difference between the estimated values and 356 measured upper layer tree heights is only 0.77 m with a standard deviation of 2.39 m. ms of seed generation, the morph algorithm (2d) used in this paper detected 64% of the trees visible in the aerial photos with an error margin of around 25% both for commission and omission in a dense natural forest. The orthophoto-based local maximum approach generally yielded lower accuracies and more multiple hits than the morph algorithm. 3d seed generation from the block based model returned about 70% correct hits for the upper tree canopy layer. All evaluations are performed based on field measurements and visual aerial photo interpretation. Furthermore, the dependence of successful tree detection on the dominance of a tree within the stand is analyzed. As expected, suppressed trees are more likely to be omitted. The segmentation proved to be useful, as the automatically generated segments had a similar number of correct hits as achieved by visual interpretation, with the only drawback being a higher error of commission.