Harri Kaartinen

Advances in Forest Inventory Using Airborne Laser Scanning

We present two improvements for laser-based forest inventory. The first improvement is based on using last pulse data for tree detection. When trees overlap, the surface model between the trees corresponding to the first pulse stays high, whereas the corresponding model from the last pulse results in a drop in elevation, due to its better penetration between the trees. This drop in elevation can be used for separating trees. In a test carried out in Evo, Southern Finland, we used 292 forests plots consisting of more than 5,500 trees and airborne laser scanning (ALS) data comprised of 12.7 emitted laser pulses per m2. With last pulse data, an improvement of 6% for individual tree detection was obtained when compared to using first pulse data. The improvement increased with an increasing number of stems per plot and with decreasing diameter breast height (DBH). The results confirm that there is also substantial information for tree detection in last pulse data. The second improvement is based on the use of individual tree-based features in addition to the statistical point height metrics in area-based prediction of forest variables. The commonly-used ALS point height metrics and individual tree-based features were fused into the non-parametric estimation of forest variables. By using only four individual tree-based features, stem volume estimation improved when compared to the use of statistical point height metrics. For DBH estimation, the point height metrics and individual tree-based features complemented each other. Predictions were validated at plot level.

RELATIVE ORIENTATION BETWEEN A SINGLE FRAME IMAGE AND LIDAR POINT CLOUD USING LINEAR FEATURES

Registration of multi-source remote sensing data is an essential task prior their efficient integrated use. It is known that accurate registration of different data sources, such as aerial frame images and lidar data, is a challenging process, where extraction and selection of robust tie features is the key issue. In the presented approach, we used linear features, namely roof ridges, as tie features. Roof ridges derived from lidar data are automatically located in the 2D image plane and the relative orientation is based on the well-known coplanarity condition. According to the results, the average registration (absolute) errors varied between 0.003 to 0.196 m in the X direction, between 0.018 to 0.282 m in the Y direction and between 0.010 to 0.967 m in the Z direction. Rotation (absolute) errors varied between 0.001 to 0.078 degrees, 0.006 to 0.466 degrees and 0.013 to 0.115 degrees for ω, ϕ and κ rotations, respectively. This study revealed that the method has potential in automatic relative orientation of a single frame image and lidar data. However, the distribution, orientation and the number of successfully located tie features have an essential role in succeeding in the task.