Comparative Evaluation of a Newly Developed Trunk-Based Tree Detection/Localization Strategy on Leaf-Off LiDAR Point Clouds with Varying Characteristics
LiDAR data acquired by various platforms provide unprecedented data for forest inventory and management. Among its applications, individual tree detection and segmentation are critical and prerequisite steps for deriving forest structural metrics, especially at the stand level. Although there are va...
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| Format: | Article |
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MDPI AG
2022-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/15/3738 |
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| author | Tian Zhou Renato César dos Santos Jidong Liu Yi-Chun Lin William Changhao Fei Songlin Fei Ayman Habib |
| author_facet | Tian Zhou Renato César dos Santos Jidong Liu Yi-Chun Lin William Changhao Fei Songlin Fei Ayman Habib |
| author_sort | Tian Zhou |
| collection | DOAJ |
| description | LiDAR data acquired by various platforms provide unprecedented data for forest inventory and management. Among its applications, individual tree detection and segmentation are critical and prerequisite steps for deriving forest structural metrics, especially at the stand level. Although there are various tree detection and localization approaches, a comparative analysis of their performance on LiDAR data with different characteristics remains to be explored. In this study, a new trunk-based tree detection and localization approach (namely, height-difference-based) is proposed and compared to two state-of-the-art strategies—DBSCAN-based and height/density-based approaches. Leaf-off LiDAR data from two unmanned aerial vehicles (UAVs) and Geiger mode system with different point densities, geometric accuracies, and environmental complexities were used to evaluate the performance of these approaches in a forest plantation. The results from the UAV datasets suggest that DBSCAN-based and height/density-based approaches perform well in tree detection (F1 score > 0.99) and localization (with an accuracy of 0.1 m for point clouds with high geometric accuracy) after fine-tuning the model thresholds; however, the processing time of the latter is much shorter. Even though our new height-difference-based approach introduces more false positives, it obtains a high tree detection rate from UAV datasets without fine-tuning model thresholds. However, due to the limitations of the algorithm, the tree localization accuracy is worse than that of the other two approaches. On the other hand, the results from the Geiger mode dataset with low point density show that the performance of all approaches dramatically deteriorates. Among them, the proposed height-difference-based approach results in the greatest number of true positives and highest F1 score, making it the most suitable approach for low-density point clouds without the need for parameter/threshold fine-tuning. |
| first_indexed | 2024-03-09T12:13:27Z |
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| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T12:13:27Z |
| publishDate | 2022-08-01 |
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| series | Remote Sensing |
| spelling | doaj.art-011ba64cf83646ab8e52f0d2c2f95b642023-11-30T22:49:27ZengMDPI AGRemote Sensing2072-42922022-08-011415373810.3390/rs14153738Comparative Evaluation of a Newly Developed Trunk-Based Tree Detection/Localization Strategy on Leaf-Off LiDAR Point Clouds with Varying CharacteristicsTian Zhou0Renato César dos Santos1Jidong Liu2Yi-Chun Lin3William Changhao Fei4Songlin Fei5Ayman Habib6Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USADepartment of Cartography and Graduate Program on Cartographic Sciences (PPGCC), São Paulo State University, Presidente Prudente 19060-900, SP, BrazilLyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USALyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USALyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USADepartment of Forestry and Natural Resources, Purdue University, West Lafayette, IN 47907, USALyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USALiDAR data acquired by various platforms provide unprecedented data for forest inventory and management. Among its applications, individual tree detection and segmentation are critical and prerequisite steps for deriving forest structural metrics, especially at the stand level. Although there are various tree detection and localization approaches, a comparative analysis of their performance on LiDAR data with different characteristics remains to be explored. In this study, a new trunk-based tree detection and localization approach (namely, height-difference-based) is proposed and compared to two state-of-the-art strategies—DBSCAN-based and height/density-based approaches. Leaf-off LiDAR data from two unmanned aerial vehicles (UAVs) and Geiger mode system with different point densities, geometric accuracies, and environmental complexities were used to evaluate the performance of these approaches in a forest plantation. The results from the UAV datasets suggest that DBSCAN-based and height/density-based approaches perform well in tree detection (F1 score > 0.99) and localization (with an accuracy of 0.1 m for point clouds with high geometric accuracy) after fine-tuning the model thresholds; however, the processing time of the latter is much shorter. Even though our new height-difference-based approach introduces more false positives, it obtains a high tree detection rate from UAV datasets without fine-tuning model thresholds. However, due to the limitations of the algorithm, the tree localization accuracy is worse than that of the other two approaches. On the other hand, the results from the Geiger mode dataset with low point density show that the performance of all approaches dramatically deteriorates. Among them, the proposed height-difference-based approach results in the greatest number of true positives and highest F1 score, making it the most suitable approach for low-density point clouds without the need for parameter/threshold fine-tuning.https://www.mdpi.com/2072-4292/14/15/3738tree detection and localizationLiDARGeiger-modeunmanned aerial vehicles (UAV)forest inventory |
| spellingShingle | Tian Zhou Renato César dos Santos Jidong Liu Yi-Chun Lin William Changhao Fei Songlin Fei Ayman Habib Comparative Evaluation of a Newly Developed Trunk-Based Tree Detection/Localization Strategy on Leaf-Off LiDAR Point Clouds with Varying Characteristics Remote Sensing tree detection and localization LiDAR Geiger-mode unmanned aerial vehicles (UAV) forest inventory |
| title | Comparative Evaluation of a Newly Developed Trunk-Based Tree Detection/Localization Strategy on Leaf-Off LiDAR Point Clouds with Varying Characteristics |
| title_full | Comparative Evaluation of a Newly Developed Trunk-Based Tree Detection/Localization Strategy on Leaf-Off LiDAR Point Clouds with Varying Characteristics |
| title_fullStr | Comparative Evaluation of a Newly Developed Trunk-Based Tree Detection/Localization Strategy on Leaf-Off LiDAR Point Clouds with Varying Characteristics |
| title_full_unstemmed | Comparative Evaluation of a Newly Developed Trunk-Based Tree Detection/Localization Strategy on Leaf-Off LiDAR Point Clouds with Varying Characteristics |
| title_short | Comparative Evaluation of a Newly Developed Trunk-Based Tree Detection/Localization Strategy on Leaf-Off LiDAR Point Clouds with Varying Characteristics |
| title_sort | comparative evaluation of a newly developed trunk based tree detection localization strategy on leaf off lidar point clouds with varying characteristics |
| topic | tree detection and localization LiDAR Geiger-mode unmanned aerial vehicles (UAV) forest inventory |
| url | https://www.mdpi.com/2072-4292/14/15/3738 |
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