Using LiDAR data to measure the 3D green biomass of Beijing urban forest in China.
The purpose of the paper is to find a new approach to measure 3D green biomass of urban forest and to testify its precision. In this study, the 3D green biomass could be acquired on basis of a remote sensing inversion model in which each standing wood was first scanned by Terrestrial Laser Scanner t...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2013-01-01
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| Series: | PLoS ONE |
| Online Access: | http://europepmc.org/articles/PMC3795711?pdf=render |
| _version_ | 1828887604617019392 |
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| author | Cheng He Matteo Convertino Zhongke Feng Siyu Zhang |
| author_facet | Cheng He Matteo Convertino Zhongke Feng Siyu Zhang |
| author_sort | Cheng He |
| collection | DOAJ |
| description | The purpose of the paper is to find a new approach to measure 3D green biomass of urban forest and to testify its precision. In this study, the 3D green biomass could be acquired on basis of a remote sensing inversion model in which each standing wood was first scanned by Terrestrial Laser Scanner to catch its point cloud data, then the point cloud picture was opened in a digital mapping data acquisition system to get the elevation in an independent coordinate, and at last the individual volume captured was associated with the remote sensing image in SPOT5(System Probatoired'Observation dela Tarre)by means of such tools as SPSS (Statistical Product and Service Solutions), GIS (Geographic Information System), RS (Remote Sensing) and spatial analysis software (FARO SCENE and Geomagic studio11). The results showed that the 3D green biomass of Beijing urban forest was 399.1295 million m(3), of which coniferous was 28.7871 million m(3) and broad-leaf was 370.3424 million m(3). The accuracy of 3D green biomass was over 85%, comparison with the values from 235 field sample data in a typical sampling way. This suggested that the precision done by the 3D forest green biomass based on the image in SPOT5 could meet requirements. This represents an improvement over the conventional method because it not only provides a basis to evalue indices of Beijing urban greenings, but also introduces a new technique to assess 3D green biomass in other cities. |
| first_indexed | 2024-12-13T12:07:38Z |
| format | Article |
| id | doaj.art-e059a8fccf594fbaabf7bc229a104221 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-13T12:07:38Z |
| publishDate | 2013-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-e059a8fccf594fbaabf7bc229a1042212022-12-21T23:46:55ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-01810e7592010.1371/journal.pone.0075920Using LiDAR data to measure the 3D green biomass of Beijing urban forest in China.Cheng HeMatteo ConvertinoZhongke FengSiyu ZhangThe purpose of the paper is to find a new approach to measure 3D green biomass of urban forest and to testify its precision. In this study, the 3D green biomass could be acquired on basis of a remote sensing inversion model in which each standing wood was first scanned by Terrestrial Laser Scanner to catch its point cloud data, then the point cloud picture was opened in a digital mapping data acquisition system to get the elevation in an independent coordinate, and at last the individual volume captured was associated with the remote sensing image in SPOT5(System Probatoired'Observation dela Tarre)by means of such tools as SPSS (Statistical Product and Service Solutions), GIS (Geographic Information System), RS (Remote Sensing) and spatial analysis software (FARO SCENE and Geomagic studio11). The results showed that the 3D green biomass of Beijing urban forest was 399.1295 million m(3), of which coniferous was 28.7871 million m(3) and broad-leaf was 370.3424 million m(3). The accuracy of 3D green biomass was over 85%, comparison with the values from 235 field sample data in a typical sampling way. This suggested that the precision done by the 3D forest green biomass based on the image in SPOT5 could meet requirements. This represents an improvement over the conventional method because it not only provides a basis to evalue indices of Beijing urban greenings, but also introduces a new technique to assess 3D green biomass in other cities.http://europepmc.org/articles/PMC3795711?pdf=render |
| spellingShingle | Cheng He Matteo Convertino Zhongke Feng Siyu Zhang Using LiDAR data to measure the 3D green biomass of Beijing urban forest in China. PLoS ONE |
| title | Using LiDAR data to measure the 3D green biomass of Beijing urban forest in China. |
| title_full | Using LiDAR data to measure the 3D green biomass of Beijing urban forest in China. |
| title_fullStr | Using LiDAR data to measure the 3D green biomass of Beijing urban forest in China. |
| title_full_unstemmed | Using LiDAR data to measure the 3D green biomass of Beijing urban forest in China. |
| title_short | Using LiDAR data to measure the 3D green biomass of Beijing urban forest in China. |
| title_sort | using lidar data to measure the 3d green biomass of beijing urban forest in china |
| url | http://europepmc.org/articles/PMC3795711?pdf=render |
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