Digital terrain model: A DTM, generated from aerial photos, with ground resolution of 4

meter was used in this study. Fig. 1 depicts shaded relief of the study area computed from the digital elevation model assuming 315° (azimuth) and 45° (altitude) as the position of the illumination source.

2.2.3 Ancillary data: In addition to aerial photographs and satellite images, this study used topographic maps (1:1000), orthoimages (1:5000), land cover maps, ground control points (GCP) measured using real-time kinematic (RTK) GPS, and field data to evaluate the quality of the lidar dataset. For photogrammetric processes, ground control points were measured using high-precision surveying instruments such as GPS and total stations.

2.3 Method

The lidar datasets contain irregularly spaced point clouds with four measurement readings, i.e., easting, northing, elevation, and intensity of the returned pulse. As shown in Table 1, the airborne lidar data has three datasets including all points, surface points, and ground points. The data were text files, and the (X,Y, Z) values were in TWD97 (Taiwan geodetic datum based on the Geodetic Reference System 1980, GRS80) coordinate system with elevation representing geodetic height. In order to compare with the DEM, the datasets were converted to ARC/INFO raster grids with common origin and cell size as the DEM. From the dataset containing all points data, the minimum elevation, maximum elevation, average elevation, and number of points within each grid cell were computed and corresponding GRID maps were created. Moreover, the lidar datasets were converted into vector maps, i.e., ARC/INFO point coverages. All the data conversions were done using

software programs written in C and AML (Arc Macro Languate).

The lidar DSM (LIDAR_DSM) was represented by the grid map containing local maximum, and the grid map of local minimum provided estimation of the DTM (LIDAR_DTM). The lidar canopy height model (LIDAR_CHM) was obtained by subtracting the LIDAR_DTM from the

LIDAR_DSM. Processing of the lidar data required large amount of computer disk space and memory because the size of the original data was quite large. To reduce processing time, a subset of the lidar dataset, 1 km x 1 km, was selected to experiment with the analytical methods developed for this study. Within the experimental site, a sample plot was selected to capture data using the terrestrial lidar system. The same algorithms were applied to the terrestrial lidar dataset in order to

Fig. 1. Shaded relief of the study area produced from DEM

3. Results

The airborne lidar data points were grouped into 4 m x 4 m grid cells, within each cell statistical analysis was performed. Because of the high point density, only very few grid cells were voids, i.e., without any observation of lidar pulses. For the 1 km x 1 km experimental site, only 15 out of 62,500 cells were voids, which amounted to 0.024% of the experimental area.

Analyzed on a per cell basis, the LIDAR_DSM, LIDAR_DTM, and LIDAR_CHM are shown in Fig. 2, Fig. 3, and Fig. 4, respectively. As compared to the 4-meter AP_DTM (Fig. 5), more roughness and details are observed in the LIDAR_DSM and LIDAR_DTM. This was because the AP_DTM was produced by interpolating the stereo image model during the automatic matching process, in contrast, the LIDAR_DSM and LIDAR_DTM were generated using local functions. For comparison between the AP_DTM and LIDAR_DTM, a TIN (triangulated irregular network) was constructed from the AP_DTM, then the (X, Y) coordinates of the grid points of the LIDAR_DTM were used to compute the elevation values by interpolating from the TIN model. Table 3 depicts the comparisons of the elevations between the AP_DTM and LIDAR_DTM. The mean difference between the terrestrial LIDAR_DTM and the AP_DTM was less than mean difference between the airborne LIDAR_DTM and the AP_DTM. This was because the airborne lidar could not see through densely vegetated areas, which caused larger bias for estimation of the ground elevations.

Fig. 2. DSM created from airborne lidar data Fig.3. DTM created from airborne lidar data

Fig. 4. CHM created from airborne lidar data Fig. 5. DTM produced from aerial photos

Variable Number of Obaservations

Mean (m)

Maximum (m)

Minimum (m)

Standard Deviation (m)

4. conclusions

While a more complete analysis is needed to evaluate the accuracy of estimation of forest canopy height, the results indicate that the lidar data have great potential for measuring forest canopy structure directly. Further study will be focused on validation of the predicted lidar canopy height with field survey data, and methodology for integrating remote sensing data with lidar data to improve the accuracy of classification results as well as canopy height estimation.

5. acknowledgement

The authors would like to thank the Agricultural and Forestry Aerial Survey Institute, and the Control Signal Corporation for providing data, and the National Science Council for providing support for this study. Our gratitude also goes to the Council of Agriculture and Prof. Shih of the National Chiao Tung University for sponsoring and supervising the lidar pilot project, which resulted in data for this study.

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