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A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners

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4 Author(s)
J. Hyyppa ; Dept. of Photogrammetry & Remote Sensing, Finnish Geodetic Inst., Masala, Finland ; O. Kelle ; M. Lehikoinen ; M. Inkinen

In the boreal forest zone and in many forest areas, there exist gaps between the forest crowns. For example, in Finland, more than 30% of the first pulse data of laser scanning reflect directly from the ground without any interaction with the canopy. By increasing the number of pulses, it is possible to have samples from each individual tree and also from the gaps between the trees. Basically, this means that several laser pulses can be recorded per m2. This allows detailed investigation of forest areas and the creation of a three-dimensional (3D) tree height model. Tree height model can be calculated from the digital terrain and crown models both obtained with the laser scanner data. By analyzing the 3D tree height model by using image vision methods, e.g., segmentation, it is possible to locate individual trees, estimate individual tree heights, crown area, and, by using that data, to derive the stem diameter, number of stems, basal area, and stem volume. The advantage of the method is the capability to measure directly physical dimensions from the trees and use that information to calculate the needed stand attributes. This paper demonstrates for the first time that it is possible to accurately estimate standwise forest attributes, especially stem volume (biomass), using high-pulse-rate laser scanners to provide data, from which individual trees can be detected and characteristics of trees such as height, location, and crown dimensions can be determined. That information can be applied to provide estimates for larger areas (stands). Using the new method, the following standard errors were demonstrated for mean height, basal area and stem volume: 1.8 m (9.9%), 2.0 m2/ha (10.2%), and 18.5 m 3/ha (10.5%), respectively

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:39 ,  Issue: 5 )