Quantification of Forest Regeneration on Forest Inventory Sample Plots Using Point Clouds from Personal Laser Scanning
The presence of sufficient natural regeneration in mature forests is regarded as a pivotal criterion for their future stability, ensuring seamless reforestation following final harvesting operations or forest calamities. Consequently, forest regeneration is typically quantified as part of forest inv...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/2/269 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832587582408491008 |
|---|---|
| author | Sarah Witzmann Christoph Gollob Ralf Kraßnitzer Tim Ritter Andreas Tockner Lukas Moik Valentin Sarkleti Tobias Ofner-Graff Helmut Schume Arne Nothdurft |
| author_facet | Sarah Witzmann Christoph Gollob Ralf Kraßnitzer Tim Ritter Andreas Tockner Lukas Moik Valentin Sarkleti Tobias Ofner-Graff Helmut Schume Arne Nothdurft |
| author_sort | Sarah Witzmann |
| collection | DOAJ |
| description | The presence of sufficient natural regeneration in mature forests is regarded as a pivotal criterion for their future stability, ensuring seamless reforestation following final harvesting operations or forest calamities. Consequently, forest regeneration is typically quantified as part of forest inventories to monitor its occurrence and development over time. Light detection and ranging (LiDAR) technology, particularly ground-based LiDAR, has emerged as a powerful tool for assessing typical forest inventory parameters, providing high-resolution, three-dimensional data on the forest structure. Therefore, it is logical to attempt a LiDAR-based quantification of forest regeneration, which could greatly enhance area-wide monitoring, further supporting sustainable forest management through data-driven decision making. However, examples in the literature are relatively sparse, with most relevant studies focusing on an indirect quantification of understory density from airborne LiDAR data (ALS). The objective of this study is to develop an accurate and reliable method for estimating regeneration coverage from data obtained through personal laser scanning (PLS). To this end, 19 forest inventory plots were scanned with both a personal and a high-resolution terrestrial laser scanner (TLS) for reference purposes. The voxelated point clouds obtained from the personal laser scanner were converted into raster images, providing either the canopy height, the total number of filled voxels (containing at least one LiDAR point), or the ratio of filled voxels to the total number of voxels. Local maxima in these raster images, assumed to be likely to contain tree saplings, were then used as seed points for a raster-based tree segmentation, which was employed to derive the final regeneration coverage estimate. The results showed that the estimates differed from the reference in a range of approximately −10 to +10 percentage points, with an average deviation of around 0 percentage points. In contrast, visually estimated regeneration coverages on the same forest plots deviated from the reference by between −20 and +30 percentage points, approximately −2 percentage points on average. These findings highlight the potential of PLS data for automated forest regeneration quantification, which could be further expanded to include a broader range of data collected during LiDAR-based forest inventory campaigns. |
| format | Article |
| id | doaj-art-8e883100633041dcbad58cbb2d3cace8 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-8e883100633041dcbad58cbb2d3cace82025-01-24T13:47:56ZengMDPI AGRemote Sensing2072-42922025-01-0117226910.3390/rs17020269Quantification of Forest Regeneration on Forest Inventory Sample Plots Using Point Clouds from Personal Laser ScanningSarah Witzmann0Christoph Gollob1Ralf Kraßnitzer2Tim Ritter3Andreas Tockner4Lukas Moik5Valentin Sarkleti6Tobias Ofner-Graff7Helmut Schume8Arne Nothdurft9Department of Forest and Soil Sciences, Institute of Forest Growth, University of Natural Resources and Life Sciences, Vienna (BOKU), 1190 Vienna, AustriaDepartment of Forest and Soil Sciences, Institute of Forest Growth, University of Natural Resources and Life Sciences, Vienna (BOKU), 1190 Vienna, AustriaDepartment of Forest and Soil Sciences, Institute of Forest Growth, University of Natural Resources and Life Sciences, Vienna (BOKU), 1190 Vienna, AustriaDepartment of Forest and Soil Sciences, Institute of Forest Growth, University of Natural Resources and Life Sciences, Vienna (BOKU), 1190 Vienna, AustriaDepartment of Forest and Soil Sciences, Institute of Forest Growth, University of Natural Resources and Life Sciences, Vienna (BOKU), 1190 Vienna, AustriaDepartment of Forest and Soil Sciences, Institute of Forest Growth, University of Natural Resources and Life Sciences, Vienna (BOKU), 1190 Vienna, AustriaDepartment of Forest and Soil Sciences, Institute of Forest Growth, University of Natural Resources and Life Sciences, Vienna (BOKU), 1190 Vienna, AustriaDepartment of Forest and Soil Sciences, Institute of Forest Growth, University of Natural Resources and Life Sciences, Vienna (BOKU), 1190 Vienna, AustriaDepartment of Forest and Soil Sciences, Institute of Forest Ecology, University of Natural Resources and Life Sciences, Vienna (BOKU), 1190 Vienna, AustriaDepartment of Forest and Soil Sciences, Institute of Forest Growth, University of Natural Resources and Life Sciences, Vienna (BOKU), 1190 Vienna, AustriaThe presence of sufficient natural regeneration in mature forests is regarded as a pivotal criterion for their future stability, ensuring seamless reforestation following final harvesting operations or forest calamities. Consequently, forest regeneration is typically quantified as part of forest inventories to monitor its occurrence and development over time. Light detection and ranging (LiDAR) technology, particularly ground-based LiDAR, has emerged as a powerful tool for assessing typical forest inventory parameters, providing high-resolution, three-dimensional data on the forest structure. Therefore, it is logical to attempt a LiDAR-based quantification of forest regeneration, which could greatly enhance area-wide monitoring, further supporting sustainable forest management through data-driven decision making. However, examples in the literature are relatively sparse, with most relevant studies focusing on an indirect quantification of understory density from airborne LiDAR data (ALS). The objective of this study is to develop an accurate and reliable method for estimating regeneration coverage from data obtained through personal laser scanning (PLS). To this end, 19 forest inventory plots were scanned with both a personal and a high-resolution terrestrial laser scanner (TLS) for reference purposes. The voxelated point clouds obtained from the personal laser scanner were converted into raster images, providing either the canopy height, the total number of filled voxels (containing at least one LiDAR point), or the ratio of filled voxels to the total number of voxels. Local maxima in these raster images, assumed to be likely to contain tree saplings, were then used as seed points for a raster-based tree segmentation, which was employed to derive the final regeneration coverage estimate. The results showed that the estimates differed from the reference in a range of approximately −10 to +10 percentage points, with an average deviation of around 0 percentage points. In contrast, visually estimated regeneration coverages on the same forest plots deviated from the reference by between −20 and +30 percentage points, approximately −2 percentage points on average. These findings highlight the potential of PLS data for automated forest regeneration quantification, which could be further expanded to include a broader range of data collected during LiDAR-based forest inventory campaigns.https://www.mdpi.com/2072-4292/17/2/269forest regenerationpersonal laser scanningLiDAR dataregeneration coverage estimationforest inventory |
| spellingShingle | Sarah Witzmann Christoph Gollob Ralf Kraßnitzer Tim Ritter Andreas Tockner Lukas Moik Valentin Sarkleti Tobias Ofner-Graff Helmut Schume Arne Nothdurft Quantification of Forest Regeneration on Forest Inventory Sample Plots Using Point Clouds from Personal Laser Scanning Remote Sensing forest regeneration personal laser scanning LiDAR data regeneration coverage estimation forest inventory |
| title | Quantification of Forest Regeneration on Forest Inventory Sample Plots Using Point Clouds from Personal Laser Scanning |
| title_full | Quantification of Forest Regeneration on Forest Inventory Sample Plots Using Point Clouds from Personal Laser Scanning |
| title_fullStr | Quantification of Forest Regeneration on Forest Inventory Sample Plots Using Point Clouds from Personal Laser Scanning |
| title_full_unstemmed | Quantification of Forest Regeneration on Forest Inventory Sample Plots Using Point Clouds from Personal Laser Scanning |
| title_short | Quantification of Forest Regeneration on Forest Inventory Sample Plots Using Point Clouds from Personal Laser Scanning |
| title_sort | quantification of forest regeneration on forest inventory sample plots using point clouds from personal laser scanning |
| topic | forest regeneration personal laser scanning LiDAR data regeneration coverage estimation forest inventory |
| url | https://www.mdpi.com/2072-4292/17/2/269 |
| work_keys_str_mv | AT sarahwitzmann quantificationofforestregenerationonforestinventorysampleplotsusingpointcloudsfrompersonallaserscanning AT christophgollob quantificationofforestregenerationonforestinventorysampleplotsusingpointcloudsfrompersonallaserscanning AT ralfkraßnitzer quantificationofforestregenerationonforestinventorysampleplotsusingpointcloudsfrompersonallaserscanning AT timritter quantificationofforestregenerationonforestinventorysampleplotsusingpointcloudsfrompersonallaserscanning AT andreastockner quantificationofforestregenerationonforestinventorysampleplotsusingpointcloudsfrompersonallaserscanning AT lukasmoik quantificationofforestregenerationonforestinventorysampleplotsusingpointcloudsfrompersonallaserscanning AT valentinsarkleti quantificationofforestregenerationonforestinventorysampleplotsusingpointcloudsfrompersonallaserscanning AT tobiasofnergraff quantificationofforestregenerationonforestinventorysampleplotsusingpointcloudsfrompersonallaserscanning AT helmutschume quantificationofforestregenerationonforestinventorysampleplotsusingpointcloudsfrompersonallaserscanning AT arnenothdurft quantificationofforestregenerationonforestinventorysampleplotsusingpointcloudsfrompersonallaserscanning |