A Section-based Method For Tree Species Classification Using Airborne LiDAR Discrete Points In Urban Areas

Abstract:

As a new approach to forest inventory utilizing, LiDAR remote sensing has become an important research issue in the past. Lidar researches initially concentrate on the investigation for mapping forests at the tree level and identifying important structural parameters, such as tree height, crown size, crown base height, individual tree species, and stem volume etc. But for the virtual city visualization and mapping, the traditional methods of tree classification can't satisfy the more complex conditions. Recently, the advanced LiDAR technology has generated new full waveform scanners that provide a higher point density and additional information about the reflecting characteristics of trees. Subsequently, it was demonstrated that it is feasible to detect individual overstorey trees in forests and classify species. But the important issues like the calibration and the decomposition of full waveform data with a series of Gaussian functions usually take a lot of works. What’s more, the detection and classification of vegetation results relay much on the prior outcomes.

From all above, the section-based method for tree species classification using small footprint and high sampling density lidar data is proposed in this paper, which can overcome the tree species classification issues in urban areas. More specific objectives are to: (1)use local maximum height decision and four direction sections certification methods to get the precise locations of the trees;(2) develop new lidar-derived features processing techniques for characterizing the section structure of individual tree crowns;(3) investigate several techniques for filtering and analyzing vertical profiles of individual trees to classify the trees, and using the expert decision skills based on percentile analysis;(4) assess the accuracy of estimating tree species for each tree, and (5) investigate which type of lidar data, point frequency or intensity, provides the most accurate estimate of tree species’ classification. All the test were conducted in Xuzhou City, one of the ecological cities in China.