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

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

Assessing a Template Matching Approach for Tree Height and Position Extraction from Lidar-Derived Canopy Height Models of Pinus Pinaster Stands

In this paper, an assessment of a method using a correlation filter over a lidar-derived digital canopy height model (CHM) is presented. The objective of the procedure is to obtain stem density, position, and height values, on a stand with the following characteristics: ellipsoidal canopy shape (Pinus pinaster), even-aged and single-layer structure. The process consists of three steps: extracting a correlation map from CHM by applying a template whose size and shape resembles the canopy to be detected, applying a threshold mask to the correlation map to keep a subset of candidate-pixels, and then applying a local maximum filter to the remaining pixel groups. The method performs satisfactorily considering the experimental conditions. The mean tree extraction percentage is 65% with a coefficient of agreement of 0.4. The mean absolute error of height is ~0.5 m for all plots except one. It can be considered a valid approach for extracting tree density and height in regularly spaced stands (i.e., poplar plantations) which are fundamental for extracting related forest parameters such as volume and biomass

Airborne light detection and ranging (LiDAR) point density analysis

The point density is a preeminent parameter on airborne laser scanner surveys. It is not only related to accuracy but costs and savings. The lack of uniformity of the point density across the survey is well-known in the scientific community. This paper analyzes the behaviour of the point density derived by an oscillating mirror laser scanner on different single strips on flat bare ground in order to estimate a meaningful mean density value. The variation of the point density at both extreme ends of the oscillating mirror scan is meaningful. It will be demonstrated that excluding the extreme sectors across the strip corresponding to 1/8 of the swath width (12.5% of the sampling area, half in each side) for the computation of the mean density value is enough to satisfy light detection and ranging (LiDAR) specifications for national level surveys.Balsa Barreiro, J.; Avariento Vicent, JP.; Lerma García, JL. (2012). Airborne light detection and ranging (LiDAR) point density analysis. Scientific Research and Essays. 7(33):3010-3019. doi:10.5897/SRE12.278S3010301973