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Sensors 2009, 9(3), 1541-1558; doi:10.3390/s90301541

Improving Measurement of Forest Structural Parameters by Co-Registering of High Resolution Aerial Imagery and Low Density LiDAR Data

1
State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, P.R. China
2
Division of Ecosystem Science, University of California, Berkeley, CA 94720-3114, USA
3
Institute of Forest Resources Information Technology, Chinese Academic of Forestry, Beijing, 100091, P.R. China
*
Author to whom correspondence should be addressed.
Received: 27 November 2008 / Revised: 2 February 2009 / Accepted: 3 February 2009 / Published: 4 February 2009
(This article belongs to the Section Remote Sensors)
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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. View Full-Text
Keywords: LiDAR; Aerial image; Forest structural parameters extraction LiDAR; Aerial image; Forest structural parameters extraction
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Huang, H.; Gong, P.; Cheng, X.; Clinton, N.; Li, Z. Improving Measurement of Forest Structural Parameters by Co-Registering of High Resolution Aerial Imagery and Low Density LiDAR Data. Sensors 2009, 9, 1541-1558.

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