Use of Aerial LiDAR in Measuring Streetscape and Street Trees

Abstract

This paper investigates the usefulness of 3D volumetric pixels (voxels) and the United States Geological Survey (USGS) Quality Level 2 (QL2) Light Detection and Ranging (LiDAR) data to measure features in streetscapes. As the USGS embarks on a national LiDAR database with the goal of covering the entire United States of America (U.S.) with QL2 data or better, this paper investigates uses of QL2 LiDAR for the 3D measuring of streetscapes. Tree mapping is a common use of QL2 LiDAR data, and street trees are among the most common features within urban streetscapes that transportation and urban designers assess. Traditional remote sensing techniques derive tree polygons from imagery, and traditional uses of LiDAR for tree canopy mapping is based on deriving a 2D canopy polygon with an attribute for elevation height. However, when breaking up streetscapes into 5 Ft elevation zones and calculating street–tree voxels at each elevation zone height, 3D characteristics of street trees become prevalent that completely differ from the common 2D LiDAR-derived street trees. Statistical tests in this paper display how different the 3D characteristics are from the 2D-derived LiDAR polygons, as this paper introduces a new methodology for measuring streetscape features in 3D, particularly street trees.

Measuring Streetscape Features with High-Density Aerial Light Detection and Ranging

“…As mentioned in the introduction, there are conflicting research findings about the impact street trees and landscape improvements have on road safety, and one possible source of conflict has been our inability to measure these features properly. The authors' previous study exploring the limits of USGS QL2 data expresses and legitimizes voxel use for measuring trees (5). Though some manual classification is involved with the QL1 process, actual objective and measurable landscape features can coincide with street tree data to assist with such research.…”

“…Golombek and Marshall (5) explored the limits of QL2 data by designating streetscapes in three-dimensional (3D) pixels-better known as ''voxel'' grids-and found that QL2 data for measuring streetscapes is primarily limited to buildings and street trees. Since understanding the impacts of street trees and other clear zone objects is an important transportation topic, that study also discussed how 3D measurement of street trees widely differs from traditional 2D-derived canopy data.…”

High-density mobile LiDAR for measuring urban streetscape features

“…Mobile LiDAR has potential to fill these gaps. In addition to our previous studies (Golombek and Marshall 2019 , 2020 ), examples above from Lehtomaki et al, Wu et al, Gargoum et al, and Yang et al all utilized voxel-based approaches to advance their stated methods for measuring and quantifying urban features. Automated classification methods are quickly evolving for large-scale/area classification but are currently questionable regarding their exactness.…”

“…Research by Golombek and Marshall, for instance, explored the streetscape mapping and streetscape feature detection/extraction capabilities of publicly available aerial LiDAR data. More specifically, this research tested data derived from the national United States Geological Survey (USGS) 3D elevation program, which included Quality Level (QL) 2 LiDAR data (Golombek and Marshall 2019 ) as well as the four times denser QL1 LiDAR data (Golombek and Marshall 2020 ). QL2 LiDAR has a point density of at least 2 points per square meter while QL1 LiDAR is at least 8 points per square meter.…”

“…Golombek and Marshall took potential urban streetscape measuring a step further and introduced voxel-based quantitative streetscape feature extraction of USGS QL1 and QL2 data, which creates descriptive statistics for how various features mentioned above are represented in a streetscape in 3D. These studies are among the most accurate methods to date, but as also mentioned in the introduction, many smaller streetscape features were still not detectable, even with the higher-density QL1 standard (Golombek and Marshall 2019 , 2020 ). Also, ground-level vertical features such as windows, awnings, and building signage are important parts of streetscape studies but also not detectable with QL1 data.…”