Improving Measurement of Forest Structural Parameters by Co-Registering of High Resolution Aerial Imagery and Low Density LiDAR Data
Open Access
- 3 February 2009
- Vol. 9 (3), 1541-1558
- https://doi.org/10.3390/s90301541
Abstract
Forest structural parameters, such as tree height and crown width, are indispensable for evaluating forest biomass or forest volume. LiDAR is a revolutionary technology for measurement of forest structural parameters, however, the accuracy of crown width extraction is not satisfactory when using a low density LiDAR, especially in high canopy cover forest. We used high resolution aerial imagery with a low density LiDAR system to overcome this shortcoming. A morphological filtering was used to generate a DEM (Digital Elevation Model) and a CHM (Canopy Height Model) from LiDAR data. The LiDAR camera image is matched to the aerial image with an automated keypoints search algorithm. As a result, a high registration accuracy of 0.5 pixels was obtained. A local maximum filter, watershed segmentation, and object-oriented image segmentation are used to obtain tree height and crown width. Results indicate that the camera data collected by the integrated LiDAR system plays an important role in registration with aerial imagery. The synthesis with aerial imagery increases the accuracy of forest structural parameter extraction when compared to only using the low density LiDAR data.Keywords
This publication has 20 references indexed in Scilit:
- Reduction of atmospheric and topographic effect on Landsat TM data for forest classificationInternational Journal of Remote Sensing, 2008
- Review of methods of small‐footprint airborne laser scanning for extracting forest inventory data in boreal forestsInternational Journal of Remote Sensing, 2008
- Using Tree Clusters to Derive Forest Properties from Small Footprint Lidar DataPhotogrammetric Engineering & Remote Sensing, 2006
- Individual Tree-Crown Delineation and Treetop Detection in High-Spatial-Resolution Aerial ImageryPhotogrammetric Engineering & Remote Sensing, 2004
- Measuring individual tree crown diameter with lidar and assessing its influence on estimating forest volume and biomassCanadian Journal of Remote Sensing, 2003
- LiDAR remote sensing of forest structureProgress in Physical Geography: Earth and Environment, 2003
- Lidar Remote Sensing for Ecosystem StudiesBioScience, 2002
- Radiometric slope correction for forest biomass estimation from SAR data in the Western Sayani Mountains, SiberiaRemote Sensing of Environment, 2001
- Local Maximum Filtering for the Extraction of Tree Locations and Basal Area from High Spatial Resolution ImageryRemote Sensing of Environment, 2000
- Determining forest canopy characteristics using airborne laser dataRemote Sensing of Environment, 1984