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Remote Sens. 2018, 10(11), 1759; https://doi.org/10.3390/rs10111759

Evaluating Carbon Sequestration and PM2.5 Removal of Urban Street Trees Using Mobile Laser Scanning Data

1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
Nanjing Institute of Environmental Sciences, Ministry of Environmental Protection, Nanjing 210042, China
3
Key Laboratory for National Geography State Monitoring (National Administration of Surveying, Mapping and Geoinformation), Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Received: 28 September 2018 / Revised: 28 October 2018 / Accepted: 4 November 2018 / Published: 7 November 2018
(This article belongs to the Special Issue Aerial and Near-Field Remote Sensing Developments in Forestry)
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Abstract

Street trees are an important part of urban facilities, and they can provide both aesthetic benefits and ecological benefits for urban environments. Ecological benefits of street trees now are attracting more attention because of environmental deterioration in cities. Conventional methods of evaluating ecological benefits require a lot of labor and time, and establishing an efficient and effective evaluating method is challenging. In this study, we investigated the feasibility to use mobile laser scanning (MLS) data to evaluate carbon sequestration and fine particulate matter (PM2.5) removal of street trees. We explored the approach to extract individual street trees from MLS data, and street trees of three streets in Nantong City were extracted. The correctness rates and completeness rates of extraction results were both over 92%. Morphological parameters, including tree height, crown width, and diameter at breast height (DBH), were measured for extracted street trees, and parameters derived from MLS data were in a good agreement with field-measured parameters. Necessary information about street trees, including tree height, DBH, and tree species, meteorological data and PM2.5 deposition velocities were imported into i-Tree Eco model to estimate carbon sequestration and PM2.5 removal. The estimation results indicated that ecological benefits generated by different tree species were considerably varied and the differences for trees of the same species were mainly caused by the differences in morphological parameters (tree height and DBH). This study succeeds in estimating the amount of carbon sequestration and PM2.5 removal of individual street trees with MLS data, and provides researchers with a novel and efficient way to investigate ecological benefits of urban street trees or urban forests. View Full-Text
Keywords: ecological benefits; i-Tree Eco model; LiDAR; MLS; morphological parameters; street trees extraction ecological benefits; i-Tree Eco model; LiDAR; MLS; morphological parameters; street trees extraction
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhao, Y.; Hu, Q.; Li, H.; Wang, S.; Ai, M. Evaluating Carbon Sequestration and PM2.5 Removal of Urban Street Trees Using Mobile Laser Scanning Data. Remote Sens. 2018, 10, 1759.

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