A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data
Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/2/322 |
| _version_ | 1827599672457297920 |
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| author | Melissa Latella Fabio Sola Carlo Camporeale |
| author_facet | Melissa Latella Fabio Sola Carlo Camporeale |
| author_sort | Melissa Latella |
| collection | DOAJ |
| description | Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></semantics></math></inline-formula> as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management. |
| first_indexed | 2024-03-09T04:19:25Z |
| format | Article |
| id | doaj.art-d44ae8011a4e4b3bbb1b5eca80320619 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T04:19:25Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-d44ae8011a4e4b3bbb1b5eca803206192023-12-03T13:49:46ZengMDPI AGRemote Sensing2072-42922021-01-0113232210.3390/rs13020322A Density-Based Algorithm for the Detection of Individual Trees from LiDAR DataMelissa Latella0Fabio Sola1Carlo Camporeale2DIATI—Department of Environmental, Land and Infrastructure Engineering, Politecnico di Torino, 10129 Torino, ItalyDIATI—Department of Environmental, Land and Infrastructure Engineering, Politecnico di Torino, 10129 Torino, ItalyDIATI—Department of Environmental, Land and Infrastructure Engineering, Politecnico di Torino, 10129 Torino, ItalyNowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m<inline-formula><math display="inline"><semantics><msup><mrow></mrow><mrow><mo>−</mo><mn>2</mn></mrow></msup></semantics></math></inline-formula> as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management.https://www.mdpi.com/2072-4292/13/2/322airborne LiDAR dataindividual tree identificationtree countingdeciduous forest |
| spellingShingle | Melissa Latella Fabio Sola Carlo Camporeale A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data Remote Sensing airborne LiDAR data individual tree identification tree counting deciduous forest |
| title | A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data |
| title_full | A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data |
| title_fullStr | A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data |
| title_full_unstemmed | A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data |
| title_short | A Density-Based Algorithm for the Detection of Individual Trees from LiDAR Data |
| title_sort | density based algorithm for the detection of individual trees from lidar data |
| topic | airborne LiDAR data individual tree identification tree counting deciduous forest |
| url | https://www.mdpi.com/2072-4292/13/2/322 |
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