Robust regression and outlier detection
Robust regression and outlier detection
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
Preface: 3D Remote Sensing in Forestry
International Journal of Remote Sensing - 3D Remote Sensing in Forestry
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The paper describes a methodology for tree species classification using features that are derived from small-footprint full waveform Light Detection and Ranging (LIDAR) data. First, 3-dimensional coordinates of the laser beam reflections, the intensity, and the pulse width are extracted by a waveform decomposition, which fits a series of Gaussian pulses to the waveform. Since multiple reflections are detected, and even overlapping pulse reflections are distinguished, a much higher point density is achieved compared to the conventional first/last-pulse technique. Secondly, tree crowns are delineated from the canopy height model (CHM) using the watershed algorithm. The CHM posts are equally spaced and robustly interpolated from the highest reflections in the canopy. Thirdly, tree features computed from the 3-dimensional coordinates of the reflections, the intensity and the pulse width are used to detect coniferous and deciduous trees by an unsupervised classification. The methodology is applied to datasets that have been captured with the TopEye MK II scanner and the Riegl LMS-Q560 scanner in the Bavarian Forest National Park in leaf-on and leaf-off conditions for Norway spruces, European beeches and Sycamore maples. The classification, which groups the data into two clusters (coniferous, deciduous), leads in the best case to an overall accuracy of 85% in a leaf-on situation and 96% in a leaf-off situation.