Tree Extraction from Airborne Laser Scanning Data in Urban Areas
Tree information in urban areas plays a significant role in many fields of study, such as ecology and environmental management. Airborne LiDAR scanning (ALS) excels at the fast and efficient acquisition of spatial information in urban-scale areas. Tree extraction from ALS data is an essential part o...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-08-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/17/3428 |
| _version_ | 1797520916030160896 |
|---|---|
| author | Hangkai You Shihua Li Yifan Xu Ze He Di Wang |
| author_facet | Hangkai You Shihua Li Yifan Xu Ze He Di Wang |
| author_sort | Hangkai You |
| collection | DOAJ |
| description | Tree information in urban areas plays a significant role in many fields of study, such as ecology and environmental management. Airborne LiDAR scanning (ALS) excels at the fast and efficient acquisition of spatial information in urban-scale areas. Tree extraction from ALS data is an essential part of tree structural studies. Current raster-based methods that use canopy height models (CHMs) suffer from the loss of 3D structure information, whereas the existing point-based methods are non-robust in complex environments. Aiming at making full use of the canopy’s 3D structure information that is provided by point cloud data, and ensuring the method’s suitability in complex scenes, this paper proposes a new point-based method for tree extraction that is based on 3D morphological features. Considering the elevation deviations of the ALS data, we propose a neighborhood search method to filter out the ground and flat-roof points. A coarse extraction method, combining planar projection with a point density-filtering algorithm is applied to filter out distracting objects, such as utility poles and cars. After that, a Euclidean cluster extraction (ECE) algorithm is used as an optimization strategy for coarse extraction. In order to verify the robustness and accuracy of the method, airborne LiDAR data from Zhangye, Gansu, China and unmanned aircraft vehicle (UAV) LiDAR data from Xinyang, Henan, China were tested in this study. The experimental results demonstrated that our method was suitable for extracting trees in complex urban scenes with either high or low point densities. The extraction accuracy obtained for the airborne LiDAR data and UAV LiDAR data were 99.4% and 99.2%, respectively. In addition, a further study found that the aberrant vertical structure of the artificially pruned canopy was the main cause of the error. Our method achieved desirable results in different scenes, with only one adjustable parameter, making it an easy-to-use method for urban area studies. |
| first_indexed | 2024-03-10T08:05:17Z |
| format | Article |
| id | doaj.art-b875fc5168a24b319ed43f28e7a47775 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T08:05:17Z |
| publishDate | 2021-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b875fc5168a24b319ed43f28e7a477752023-11-22T11:08:44ZengMDPI AGRemote Sensing2072-42922021-08-011317342810.3390/rs13173428Tree Extraction from Airborne Laser Scanning Data in Urban AreasHangkai You0Shihua Li1Yifan Xu2Ze He3Di Wang4School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Resources and Environment, University of Electronic Science and Technology of China, Chengdu 611731, ChinaDepartment of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi’an 710077, ChinaTree information in urban areas plays a significant role in many fields of study, such as ecology and environmental management. Airborne LiDAR scanning (ALS) excels at the fast and efficient acquisition of spatial information in urban-scale areas. Tree extraction from ALS data is an essential part of tree structural studies. Current raster-based methods that use canopy height models (CHMs) suffer from the loss of 3D structure information, whereas the existing point-based methods are non-robust in complex environments. Aiming at making full use of the canopy’s 3D structure information that is provided by point cloud data, and ensuring the method’s suitability in complex scenes, this paper proposes a new point-based method for tree extraction that is based on 3D morphological features. Considering the elevation deviations of the ALS data, we propose a neighborhood search method to filter out the ground and flat-roof points. A coarse extraction method, combining planar projection with a point density-filtering algorithm is applied to filter out distracting objects, such as utility poles and cars. After that, a Euclidean cluster extraction (ECE) algorithm is used as an optimization strategy for coarse extraction. In order to verify the robustness and accuracy of the method, airborne LiDAR data from Zhangye, Gansu, China and unmanned aircraft vehicle (UAV) LiDAR data from Xinyang, Henan, China were tested in this study. The experimental results demonstrated that our method was suitable for extracting trees in complex urban scenes with either high or low point densities. The extraction accuracy obtained for the airborne LiDAR data and UAV LiDAR data were 99.4% and 99.2%, respectively. In addition, a further study found that the aberrant vertical structure of the artificially pruned canopy was the main cause of the error. Our method achieved desirable results in different scenes, with only one adjustable parameter, making it an easy-to-use method for urban area studies.https://www.mdpi.com/2072-4292/13/17/3428LiDARALSUAVtree extraction3D morphological featurespoint-based |
| spellingShingle | Hangkai You Shihua Li Yifan Xu Ze He Di Wang Tree Extraction from Airborne Laser Scanning Data in Urban Areas Remote Sensing LiDAR ALS UAV tree extraction 3D morphological features point-based |
| title | Tree Extraction from Airborne Laser Scanning Data in Urban Areas |
| title_full | Tree Extraction from Airborne Laser Scanning Data in Urban Areas |
| title_fullStr | Tree Extraction from Airborne Laser Scanning Data in Urban Areas |
| title_full_unstemmed | Tree Extraction from Airborne Laser Scanning Data in Urban Areas |
| title_short | Tree Extraction from Airborne Laser Scanning Data in Urban Areas |
| title_sort | tree extraction from airborne laser scanning data in urban areas |
| topic | LiDAR ALS UAV tree extraction 3D morphological features point-based |
| url | https://www.mdpi.com/2072-4292/13/17/3428 |
| work_keys_str_mv | AT hangkaiyou treeextractionfromairbornelaserscanningdatainurbanareas AT shihuali treeextractionfromairbornelaserscanningdatainurbanareas AT yifanxu treeextractionfromairbornelaserscanningdatainurbanareas AT zehe treeextractionfromairbornelaserscanningdatainurbanareas AT diwang treeextractionfromairbornelaserscanningdatainurbanareas |