The Light Detection and Ranging (LiDAR) system is one of the best ways to accurately and effectively gather 3-D terrain information. However, it is complicated to process the LiDAR cloud data due to its irregularity and large number of collected data points. This letter proposes a novel method to automatically extract urban road network from 3-D LiDAR data. This method uses height and reflectance of LiDAR data, and clustered road point information. Geometric information of general roads is also applied to correctly extract road points group. The proposed method has been tested on various urban areas which contain complicated road networks. The results demonstrate that the integration of height, reflectance, and geometric information of roads is a crucial factor that distinguishes the proposed method in its ability to reliably and correctly classify road points.

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Understanding forest dynamics is important for assessing the health of urban forests, which experience various disturbances, both natural (e.g., treefall events) and artificial (e.g., making space for agricultural fields). Therefore, quantifying three-dimensional changes in canopies is a helpful way to manage and understand urban forests better. Multitemporal airborne light detection and ranging (LiDAR) datasets enable us to quantify the vertical and lateral growth of trees across a landscape scale. The goal of this study is to assess the annual changes in the 3-D structures of canopies and forest gaps in an urban forest using annual airborne LiDAR datasets for 2012–2015. The canopies were classified as high canopies and low canopies by a 5 m height threshold. Then, we generated pixel- and plot-level canopy height models and conducted change detection annually. The vertical growth rates and leaf area index showed consistent values year by year in both canopies, while the spatial distributions of the canopy and leaf area profile (e.g., leaf area density) showed inconsistent changes each year in both canopies. In total, high canopies expanded their foliage from 12 m height, while forest gap edge canopies (including low canopies) expanded their canopies from 5 m height. Annual change detection with LiDAR datasets might inform about both steady growth rates and different characteristics in the changes of vertical canopy structures for both high and low canopies in urban forests.