The quantitative description of forest canopy structure is significant for the investigation of a forest, which serves as an important component of the terrestrial ecosystem. Light detection and ranging (LIDAR), as a new technical means that can acquire high-precision vertical information, plays a crucial role in forest monitoring and management. Choosing Dayekou forest experimental area in the Heihe watershed as a study area, we separated the ground points from the vegetation points using the skewness-change algorithm based on the intensity information from airborne LIDAR data. After that, digital terrain model (DTM) and digital surface model (DSM) were generated, respectively, based on which the canopy height model (CHM) was acquired. Finally, using the variational window, the local maximum filter method was used to extract individual tree heights and crown widths from CHM. The determination coefficients of tree heights and crown widths were 0.8568 and 0.3923, respectively. The validation results indicated that the tree heights could be effectively extracted from intensity information of airborne LIDAR, while the accuracy of extracted crown widths needed to be improved. In the future work, aerial photos and other high-resolution images would be combined to improve the accuracy.
Airborne laser scanning, also known by the acronym LIDAR (Light Detection And Ranging), is an operationally
mature remote sensing technology and it can provide rapid and highly-accurate measurements of both object and ground
surface over large areas. Presently, there are mostly two class of methods are used to process the LIDAR data. One
method is a method that processing the lidar image like two dimensions ordinary image; the other method is a way that
directly processing the point clouds of airborne LIDAR data, that is the non-ground points are filtered from all point
clouds of LIDAR data. Among the second class method, some algorithms have been also developed to process the point
clouds of LIDAR data. In this paper, a statistical algorithm-change of Kurtosis is presented to separate non-ground
points and ground points. From the curve of kurtosis's change, its inflexion is easily found to separate the object points
and ground points. The algorithm will be test on three study areas of LIDAR data provided by ISPRS Commission III
Working Group 3: City site 3, City site 4 and Forest site 5. The algorithm efficiently separates ground and object points.
Furthermore, lower objects, such as bridge, can be distinguished from other higher vegetation by the change of Kurtosis.