Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds
Fallen tree mapping provides valuable information regarding the ecological value of boreal forests. Airborne laser scanning (ALS) enables mapping fallen trees on a large scale. We compared the performance of line-detection-based individual fallen tree detection when using moderate point density ALS...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/2/382 |
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| author | Einari Heinaro Topi Tanhuanpää Mikko Vastaranta Tuomas Yrttimaa Antero Kukko Teemu Hakala Teppo Mattsson Markus Holopainen |
| author_facet | Einari Heinaro Topi Tanhuanpää Mikko Vastaranta Tuomas Yrttimaa Antero Kukko Teemu Hakala Teppo Mattsson Markus Holopainen |
| author_sort | Einari Heinaro |
| collection | DOAJ |
| description | Fallen tree mapping provides valuable information regarding the ecological value of boreal forests. Airborne laser scanning (ALS) enables mapping fallen trees on a large scale. We compared the performance of line-detection-based individual fallen tree detection when using moderate point density ALS data (15 points/m<sup>2</sup>) and high-point-density unmanned aerial vehicle-based laser scanning (ULS) data (285 points/m<sup>2</sup>). Furthermore, we inspected the dataset and detection methodology-related factors impacting performance in each case. The results of this study showed that increasing the point density of the laser scanning dataset enables the detection of a larger proportion of fallen trees. However, based on our experiment, a line-detection-based fallen tree detection approach is sensitive to noise, thus generating a large number of false detections, especially with high-point-density data. Different types of filters, such as a simple height-based filter and machine-learning-based filters, can be used for reducing noise. However, using such filters is always a compromise, as in addition to reducing noise and thus false detections, they also reduce the number of true detections. Hence, a less noise-sensitive fallen tree detection method utilizing the finer details visible in high-density point clouds could be more suitable for high-point-density laser scanning data. |
| first_indexed | 2024-03-09T11:20:27Z |
| format | Article |
| id | doaj.art-a7c6b40a87e5404c8868cfbd1ca304a2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:20:27Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-a7c6b40a87e5404c8868cfbd1ca304a22023-12-01T00:19:52ZengMDPI AGRemote Sensing2072-42922023-01-0115238210.3390/rs15020382Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point CloudsEinari Heinaro0Topi Tanhuanpää1Mikko Vastaranta2Tuomas Yrttimaa3Antero Kukko4Teemu Hakala5Teppo Mattsson6Markus Holopainen7Department of Forest Sciences, University of Helsinki, 00014 Helsinki, FinlandDepartment of Forest Sciences, University of Helsinki, 00014 Helsinki, FinlandSchool of Forest Sciences, University of Eastern Finland, 80101 Joensuu, FinlandDepartment of Forest Sciences, University of Helsinki, 00014 Helsinki, FinlandDepartment of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, The National Land Survey of Finland, 02150 Espoo, FinlandDepartment of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, The National Land Survey of Finland, 02150 Espoo, FinlandDepartment of Forest Sciences, University of Helsinki, 00014 Helsinki, FinlandDepartment of Forest Sciences, University of Helsinki, 00014 Helsinki, FinlandFallen tree mapping provides valuable information regarding the ecological value of boreal forests. Airborne laser scanning (ALS) enables mapping fallen trees on a large scale. We compared the performance of line-detection-based individual fallen tree detection when using moderate point density ALS data (15 points/m<sup>2</sup>) and high-point-density unmanned aerial vehicle-based laser scanning (ULS) data (285 points/m<sup>2</sup>). Furthermore, we inspected the dataset and detection methodology-related factors impacting performance in each case. The results of this study showed that increasing the point density of the laser scanning dataset enables the detection of a larger proportion of fallen trees. However, based on our experiment, a line-detection-based fallen tree detection approach is sensitive to noise, thus generating a large number of false detections, especially with high-point-density data. Different types of filters, such as a simple height-based filter and machine-learning-based filters, can be used for reducing noise. However, using such filters is always a compromise, as in addition to reducing noise and thus false detections, they also reduce the number of true detections. Hence, a less noise-sensitive fallen tree detection method utilizing the finer details visible in high-density point clouds could be more suitable for high-point-density laser scanning data.https://www.mdpi.com/2072-4292/15/2/382airborne laser scanningunmanned aerial vehiclelight detection and rangingdeadwoodfallen treesbiodiversity |
| spellingShingle | Einari Heinaro Topi Tanhuanpää Mikko Vastaranta Tuomas Yrttimaa Antero Kukko Teemu Hakala Teppo Mattsson Markus Holopainen Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds Remote Sensing airborne laser scanning unmanned aerial vehicle light detection and ranging deadwood fallen trees biodiversity |
| title | Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds |
| title_full | Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds |
| title_fullStr | Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds |
| title_full_unstemmed | Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds |
| title_short | Evaluating Factors Impacting Fallen Tree Detection from Airborne Laser Scanning Point Clouds |
| title_sort | evaluating factors impacting fallen tree detection from airborne laser scanning point clouds |
| topic | airborne laser scanning unmanned aerial vehicle light detection and ranging deadwood fallen trees biodiversity |
| url | https://www.mdpi.com/2072-4292/15/2/382 |
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