Canadian Forest Service Publications

Combined high-density lidar and multispectral imagery for individual tree crown analysis. 2003. Leckie, D.G.; Gougeon, F.A.; Hill, D.A.; Quinn, R.; Armstrong, L.; Shreenan, R. Canadian Journal of Remote Sensing 29(5): 633-649.

Year: 2003

Issued by: Pacific Forestry Centre

Catalog ID: 22833

Language: English

Availability: PDF (download)

Mark record


Lidar technology has reached a point where ground and forest canopy elevation models can be produced at high spatial resolution. Individual tree crown isolation and classification methods are developing rapidly for multispectral imagery. Analysis of multispectral imagery, however, does not readily provide tree height information and lidar data alone cannot provide species and health attributes. The combination of lidar and multispectral data at the individual tree level may provide a very useful forest inventory tool. A valley following approach to individual tree isolation was applied to both high resolution digital frame camera imagery and a canopy height model (CHM) created from high-density lidar data over a test site of even aged (55 years old) Douglas-fir plots of varying densities (300, 500, and 725 stems/ha) on the west coast of Canada. Tree height was determined from the laser data within the automated crown delineations. Automated tree isolations of the multispectral imagery achieved 80%–90% good correspondence with the ground reference tree delineations based on ground data. However, for the more open plot there were serious commission errors (false trees isolated) mostly related to sunlit ground vegetation. These were successfully reduced by applying a height filter to the isolations based on the lidar data. Isolations from the lidar data produced good isolations with few commission errors but poorer crown outline delineations especially for the densest plot. There is a complimentarity in the two data sources that will help in tree isolation. Heights of the automated isolations were consistently underestimated versus ground reference trees with an average error of 1.3 m. Further work is needed to test and develop tools and capabilities, but there is an effective synergy of the two high resolution data sources for providing needed forest inventory information.