The positioning accuracy of airborne LiDAR point clouds is mainly originated from GPS positioning. In general, DGPS method is used in Taiwan for solving the position of GPS rover on the air-plane with a ground base station within 20 km. However, part of the high mountains in Taiwan is inaccessible, where suitable site for installing ground reference stations is almost impossible. It is important to know the potential of applying Precise Point Positioning(PPP). PPP by using IGS GPS satellite precise ephemeris and clock anywhere in the world can achieve real-time or post processing for high-precision positioning. In this study, a test is carried out by using both conventional DGPS and PPP methods for a block of 6 selected flight strips of LiDAR scans. Results of LAS data generated by both methods are compared by the TIMESTAMP of point clouds for both before and after strip adjustment. In addition, results of adjustment of strips combined from both DGPS and PPP are compared with those of all strips of DGPS and, thus, to understand the possibility of using PPP instead of DGPS for the area not accessible for installing continuous GPS stations. It is concluded that though there is a datum difference between DGPS and PPP, strips of point clouds generated by PPP can be effectively merged into strip adjustment combining both strips of point clouds by DGPS and PPP.

Recently, image classification methods have transferred from pixel-based to object-based. Under the consideration of specific spatial features of objects, such as spectral, shape, texture, or the subordinative relations among them, object-based image analysis (OBIA) could improve the efficiency of image classification. In order to raise the capability of automatic recognition of land features from LiDAR data, 2D object-based classification method is extended for 3D point cloud classification of LiDAR data in this study. First, point cloud is segmented to independent 3D objects by various methods. Second, object features designed by this study are calculated. At last, the point clouds are classified automatically according to the object features . This study applies airborne LiDAR and ground-based LiDAR to automatic land feature classification. On the part of airborne LiDAR, structures, trees and cars were chosen to be the targets of classification. The overall accuracy and kappa value ran up to 98.40 % and 0.9638 respectively. On the other hand, on the part of ground-based LiDAR,  buildings, small structures, trees, trunks and groves were chosen to be the targets. The overall accuracy and kappa value were 84.28 % and 0.7221 respectively. The results show that utilizing the object-based concept to classify LiDAR point cloud can give assistance to point cloud recognition by means of depicting the spatial characters of these objects. The classification results then, therefore, improve not only the completeness, but also the quality.

The airborne laser scanning point clouds have become one of the primary data sources for DEM generation. By applying filtering algorithms to point clouds, the points can be classified into non-ground points and ground points. The DEM is then produced from the ground points. To assess the quality of DEM generation, a traditional method is to compute the classification accuracy of filtering results. Another method is to check the elevation differences between the produced DEM and a reliable reference DEM or some control points. However, the classification accuracy cannot reveal the over-filtering situations or any distinct non-ground points still remaining in the filtered ground data set. Although those disadvantages can be complemented by using the elevation difference method, the elevation difference method still needs to further take the topography relief into considerations for the use in a practical application. Usually those higher elevation differences occur in slope surfaces because the points on a slope surface are not easier to be classified by most filters compared with the points in a planar surface. For this reason, this paper presents a normalized elevation difference method which takes account of the surface slopes. The basic idea is using the slope as the weights for elevation difference computing. In a slope surface, the elevation difference toleration will raise while decrease in a planar surface. The experiment results show that our proposed method can be considered as a new assessment indicator especially in a practical application.

LiDAR (Light Detection and Ranging) point clouds are measurements of irregularly distributed points on scanned object surfaces acquired with airborne or terrestrial LiDAR systems. Feature extraction is the key to transform LiDAR data into spatial information. Surface features are dominant in most LiDAR data corresponding to scanned object surfaces. This paper proposes a general method to segment co-surface points. An incremental segmentation strategy is developed for the implementation, which comprises several algorithms and employs various criteria to gradually segment LiDAR point clouds into several levels. There are four operation steps. First, the proximity of point clouds is established as spatial indices defined in an octree-structured voxel space. Second, a connected-component labeling algorithm for voxels is applied for segmenting neighboring points. Third, coplanar points then can be segmented with the octree-based split-and-merge algorithm as plane features. Finally, combining neighboring plane features forms surface features. With respect to each step, processed LiDAR point clouds are segmented into organized points, neighboring point groups, coplanar point groups, and co-surface point groups. The proposed method enables an incremental retrieval and analysis of a large LiDAR dataset. Experiment results demonstrate the effectiveness of the segmentation algorithm in handling both airborne and terrestrial LiDAR data. The end results as well as the intermediate results of the segmentation may be useful for object modeling of different purposes using LiDAR data.

Airborne LiDAR can obtain abundant 3D coordinates and intensity of point clouds which include ground points and object points in short time by penetrating the canopies to the ground in forest areas which can be used in making DEM (Digital Elevation Model). The coverage on the topography, flying height and laser incident angle have an effect on the laser penetration of the LiDAR. Three different types of airborne LiDAR (Optech HD400, Leica ALS60 & Riegl LMS-Q680i) separately collect data in low, medium and high flying altitude in the same area to calculate the ground penetration where plenteous tree species cover two study areas: Najenshan ecological reserve area and Dakanuwa village, Namaxia district, Kaohsiung City. To sum up from the research, the number of penetration rate greater than 0.2 in low flying altitude, 2000 m for Optech HD400 and 1250 m for Riegl LMS-Q680i respectively, are much more than medium and high flying height in contrast that Leica ALS60 has no significant discrepancy. Besides the effect of flying altitude, the setting of LiDAR’s parameters result the better penetration for Optech HD400 in Najenshan ecological reserve area and worse to Leica ALS60 in Dakanuwa village.