Abstract
The objective of this study is to develop new algorithms for automated urban forest inventory at the individual tree level using LiDAR point cloud data. LiDAR data contain three-dimensional structure information that can be used to estimate tree height, base height, crown depth, and crown diameter. This allows precision urban forest inventory down to individual trees. Unlike most of the published algorithms that detect individual trees from a LiDAR-derived raster surface, we worked directly with the LiDAR point cloud data to separate individual trees and estimate tree metrics. Testing results in typical urban forests are encouraging. Future works will be oriented to synergize LiDAR data and optical imagery for urban tree characterization through data fusion techniques.
Highlights
An urban forest is defined as a forest or a collection of trees that grow within a city, town, or suburb.Urban forests provide a number of benefits such as saving energy and reducing air pollution
The Light Detection and Ranging (LiDAR) points falling on roads, bare soil, and lawns were filtered as ground points (Figure 4a), while those hitting buildings and tree canopies were filtered as aboveground points (Figure 4b)
The summary statistics of the datasets for estimations and field measurements are listed in Overall, the results demonstrate that tree height can be accurately estimated using LiDAR point cloud data
Summary
An urban forest is defined as a forest or a collection of trees that grow within a city, town, or suburb. Urban forests provide a number of benefits such as saving energy and reducing air pollution. To maximize these benefits, an urban forest inventory is often needed for planning and management purposes. Urban forestry databases are rare, incomplete, and infrequently updated because. 2015, 7 traditional ground surveys are time-consuming, costly, and labor-intensive. Relatively little is known about urban forest resources. With the emergence of Light Detection and Ranging (LiDAR)
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