, "Method for estimating rice plant height without ground surface detection using laser scanner measurement," J. Appl. Remote Sens. 10(4), 046018 (2016), doi: 10.1117/1.JRS.10.046018. Relative vertical distances (rD) were computed from the difference between the top and bottom positions of the rice plant. These correlated well with measured H, with slopes greater than 1.0. A greater number of stems in 2014 led to steeper slopes. Estimated H was more accurate when plant bottom positions were closer to the ground surface, and the best results were obtained with p b ¼ 95 (r 2 > 0.87; RMSE ≈ 4 cm). Overall, H was typically 16.0 cm greater than rD with p b ¼ 95.

For road construction, cracking is the main factor affecting pavement damage, which can decrease the quality of pavement and seriously affect transportation. To ensure the quality of the road surface for safety transportation, it needs to check the status of the road surface periodically. Structural health monitoring solutions, such as strain gauges and fiber optics systems, have been proposed for the monitoring of such cracks. However, expensive devices prevent these solutions being deployed. In this study, a low-cost 2D laser scanner, HOKUYO UTM 30LX, is used to detect the cracks on asphalt pavement. The point-clouds are created aligned and merged. Then, the normal vectors of all points are computed. From this result, the dip angle is generated. Thanks to the difference of dip angle, the crack and its affected area can be detected directly from the point cloud. This study confirms the ability of using a 2D laser scanner for detecting asphalt pavement cracks. It is expected that all information of the crack can be extracted and analyzed automatically by applying an image processing algorithm in the near future.

The automatic detection of cracks in the road surface is of great significance for maintaining the road surface to ensure the safety of moving vehicles. The Hokuyo UTM 30 LX 2D laser scanner in this study is used to observe the road surface containing two cracks and distress areas. As a result, dense point clouds are created. The road surface is automatically extracted from the point clouds based on the geometry of the two curb lines. The normal vector of the points is calculated based on the principal component analysis method. Points belonging to cracks and zones of distress are extracted from the intensity gradient and the inclination angle between the two normal vectors of the neighboring points and then converted to binary images. The crack edges are extracted based on the Sobel operator. Although salt-and-pepper spots due to crack points extraction using intensity gradient affect the definition of crack edges, especially small cracks, large cracks and distress areas are extracted clearly. Research results show that reflectance intensity and elevation variation combination lead to the efficiency of crack extraction and distress area.