Fine-Scale Spatial and Spectral Clustering of UAV-Acquired Digital Aerial Photogrammetric (DAP) Point Clouds for Individual Tree Crown Detection and Segmentation

Abstract

The field of remote sensing is undergoing rapid changes through the utilization of unmanned aerial vehicle (UAV) technology. The rise of this new technology and the corresponding growth in the application of digital aerial photogrammetric point clouds (DAP) require renewed investigation into individual tree detection (ITD) routines; most of which have been developed for airborne laser scanning (ALS) and traditional aerial imagery. This article analyzes the application of a well-known ALS-ITD routine to UAV-acquired DAP. In particular, specific modifications are proposed and evaluated aimed at improving its applicability to DAP, with particular emphasis on the incorporation of spectral information through subcrown scale k-means clustering. The new routine, which utilizes point-level spectral information in the clustering process, improved overall true positive detection by ∼6.3%, with the most significant improvements in true positive detection found in lower canopy class stems. The new routine was also tested without the inclusion of spectral information and was shown to produce poorer results by ∼4.1%, indicating that the inclusion of these data is beneficial for ITD approaches. This new routine represents an advancement of processing approaches incorporating both structural and spectral components for ITD in the era of UAV remote sensing in forested environments.