A Robust and Automatic Algorithm for TLS–ALS Point Cloud Registration in Forest Environments Based on Tree Locations
Fusing of terrestrial laser scanning (TLS) and airborne laser scanning (ALS) point cloud data has been recognized as an effective approach in forest studies. In this regard, co-registration of point clouds is considered one of the crucial steps in the integration process. Co-registering point clouds...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10401975/ |
| _version_ | 1797322563770122240 |
|---|---|
| author | Fariborz Ghorbani Yi-Chen Chen Markus Hollaus Norbert Pfeifer |
| author_facet | Fariborz Ghorbani Yi-Chen Chen Markus Hollaus Norbert Pfeifer |
| author_sort | Fariborz Ghorbani |
| collection | DOAJ |
| description | Fusing of terrestrial laser scanning (TLS) and airborne laser scanning (ALS) point cloud data has been recognized as an effective approach in forest studies. In this regard, co-registration of point clouds is considered one of the crucial steps in the integration process. Co-registering point clouds in forest environments faces various challenges, including unstable features, extensive occlusions, different viewpoints, and differences in point cloud densities. To address these intricate challenges, this study introduces an automated and robust method for co-registering TLS and ALS point clouds based on the correspondence of individual tree locations in forest environments. Initially, the positions of individual trees in both TLS and ALS data are extracted. Then, a filtering approach is applied to eliminate positions with low potential for corresponding matches in the TLS and ALS dataset. Since larger trees in the TLS data have a higher potential for corresponding matches in the ALS data, an iterative process is applied to identify correspondences between trees in both datasets. After estimating transformation parameters, the co-registration process is executed. The proposed method is applied on six datasets with varying forest complexities. The results demonstrate a high success rate up to 100% if the starting position of the TLS plots are located within ∼4 hectares (∼2000 trees). Additionally, the potential of the proposed method for co-registering TLS data with ALS data across different search areas and varying number of trees is evaluated in detail. The outcomes indicate that successful co-registration of TLS plot with 50 m diameter to ALS data is successful in the best case within a search radius of approximately 113 hectares (∼60,000 tree locations) and in the worst case for around 20 hectares (∼10,000 tree locations) depending on the forest complexity. |
| first_indexed | 2024-03-08T05:16:07Z |
| format | Article |
| id | doaj.art-7b09b7f4d1824d2cbba7b7b423ca6a11 |
| institution | Directory Open Access Journal |
| issn | 2151-1535 |
| language | English |
| last_indexed | 2024-03-08T05:16:07Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj.art-7b09b7f4d1824d2cbba7b7b423ca6a112024-02-07T00:00:11ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing2151-15352024-01-01174015403510.1109/JSTARS.2024.335517310401975A Robust and Automatic Algorithm for TLS–ALS Point Cloud Registration in Forest Environments Based on Tree LocationsFariborz Ghorbani0https://orcid.org/0000-0002-5942-4014Yi-Chen Chen1https://orcid.org/0000-0002-9867-3769Markus Hollaus2https://orcid.org/0000-0001-6063-7239Norbert Pfeifer3https://orcid.org/0000-0002-2348-7929Department of Photogrammetry and Remote Sensing, Geomatics Engineering Faculty, K. N. Toosi University of Technology, Tehran, IranDepartment of Geodesy and Geoinformation, Technische Universität Wien, Vienna, AustriaDepartment of Geodesy and Geoinformation, Technische Universität Wien, Vienna, AustriaDepartment of Geodesy and Geoinformation, Technische Universität Wien, Vienna, AustriaFusing of terrestrial laser scanning (TLS) and airborne laser scanning (ALS) point cloud data has been recognized as an effective approach in forest studies. In this regard, co-registration of point clouds is considered one of the crucial steps in the integration process. Co-registering point clouds in forest environments faces various challenges, including unstable features, extensive occlusions, different viewpoints, and differences in point cloud densities. To address these intricate challenges, this study introduces an automated and robust method for co-registering TLS and ALS point clouds based on the correspondence of individual tree locations in forest environments. Initially, the positions of individual trees in both TLS and ALS data are extracted. Then, a filtering approach is applied to eliminate positions with low potential for corresponding matches in the TLS and ALS dataset. Since larger trees in the TLS data have a higher potential for corresponding matches in the ALS data, an iterative process is applied to identify correspondences between trees in both datasets. After estimating transformation parameters, the co-registration process is executed. The proposed method is applied on six datasets with varying forest complexities. The results demonstrate a high success rate up to 100% if the starting position of the TLS plots are located within ∼4 hectares (∼2000 trees). Additionally, the potential of the proposed method for co-registering TLS data with ALS data across different search areas and varying number of trees is evaluated in detail. The outcomes indicate that successful co-registration of TLS plot with 50 m diameter to ALS data is successful in the best case within a search radius of approximately 113 hectares (∼60,000 tree locations) and in the worst case for around 20 hectares (∼10,000 tree locations) depending on the forest complexity.https://ieeexplore.ieee.org/document/10401975/Forestindividual tree locationsiterativepoint cloud fusionpoint cloudsreducing dependency |
| spellingShingle | Fariborz Ghorbani Yi-Chen Chen Markus Hollaus Norbert Pfeifer A Robust and Automatic Algorithm for TLS–ALS Point Cloud Registration in Forest Environments Based on Tree Locations IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Forest individual tree locations iterative point cloud fusion point clouds reducing dependency |
| title | A Robust and Automatic Algorithm for TLS–ALS Point Cloud Registration in Forest Environments Based on Tree Locations |
| title_full | A Robust and Automatic Algorithm for TLS–ALS Point Cloud Registration in Forest Environments Based on Tree Locations |
| title_fullStr | A Robust and Automatic Algorithm for TLS–ALS Point Cloud Registration in Forest Environments Based on Tree Locations |
| title_full_unstemmed | A Robust and Automatic Algorithm for TLS–ALS Point Cloud Registration in Forest Environments Based on Tree Locations |
| title_short | A Robust and Automatic Algorithm for TLS–ALS Point Cloud Registration in Forest Environments Based on Tree Locations |
| title_sort | robust and automatic algorithm for tls x2013 als point cloud registration in forest environments based on tree locations |
| topic | Forest individual tree locations iterative point cloud fusion point clouds reducing dependency |
| url | https://ieeexplore.ieee.org/document/10401975/ |
| work_keys_str_mv | AT fariborzghorbani arobustandautomaticalgorithmfortlsx2013alspointcloudregistrationinforestenvironmentsbasedontreelocations AT yichenchen arobustandautomaticalgorithmfortlsx2013alspointcloudregistrationinforestenvironmentsbasedontreelocations AT markushollaus arobustandautomaticalgorithmfortlsx2013alspointcloudregistrationinforestenvironmentsbasedontreelocations AT norbertpfeifer arobustandautomaticalgorithmfortlsx2013alspointcloudregistrationinforestenvironmentsbasedontreelocations AT fariborzghorbani robustandautomaticalgorithmfortlsx2013alspointcloudregistrationinforestenvironmentsbasedontreelocations AT yichenchen robustandautomaticalgorithmfortlsx2013alspointcloudregistrationinforestenvironmentsbasedontreelocations AT markushollaus robustandautomaticalgorithmfortlsx2013alspointcloudregistrationinforestenvironmentsbasedontreelocations AT norbertpfeifer robustandautomaticalgorithmfortlsx2013alspointcloudregistrationinforestenvironmentsbasedontreelocations |