Evaluation of Tree Object Segmentation Performance for Individual Tree Recognition Using Remote Sensing Techniques Based on Urban Forest Green Structures
This study evaluated whether tree object segmentation using remote sensing techniques could be effectively conducted according to the green structures of urban forests. The remote sensing techniques used were handheld LiDAR and UAV-based photogrammetry. The data collected from both methods were merg...
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MDPI AG
2024-11-01
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| author | Uk-Je Sung Jeong-Hee Eum Kyung-Jin Chung |
| author_facet | Uk-Je Sung Jeong-Hee Eum Kyung-Jin Chung |
| author_sort | Uk-Je Sung |
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| description | This study evaluated whether tree object segmentation using remote sensing techniques could be effectively conducted according to the green structures of urban forests. The remote sensing techniques used were handheld LiDAR and UAV-based photogrammetry. The data collected from both methods were merged to complement each other’s limitations. The green structures of the study area were classified into three types based on the distance between canopy trees and the presence of shrubs. The ability to individually classify trees within each of the three types of green structures was then evaluated. The evaluation method was to assess the success rate by comparing the actual number of trees, which were visually counted in the field, with the number of tree objects classified in the study. To perform semantic segmentation of tree objects, a preprocessing step was conducted to extract only the data related to tree structures from the data collected through remote sensing techniques. The preprocessing steps included data merging, noise removal, separation of DTM and DSM, and separation of green areas and structures. The analysis results showed that tree object recognition was not efficient when the green structures were complex and mixed, and the recognition rate was highest when only canopy trees were present, and the canopies did not overlap. Therefore, when observing in high-density areas, the semantic segmentation algorithm’s variables should be adjusted to narrow the object recognition range, and additional observations in winter are needed to compensate for areas obscured by leaves. By improving data collection methods and systematizing the analysis methods according to the green structures, the object recognition process can be enhanced. |
| format | Article |
| id | doaj-art-3b0bcd13946f4a14874b661ab07849ea |
| institution | Kabale University |
| issn | 2073-445X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-3b0bcd13946f4a14874b661ab07849ea2024-11-26T18:09:31ZengMDPI AGLand2073-445X2024-11-011311185610.3390/land13111856Evaluation of Tree Object Segmentation Performance for Individual Tree Recognition Using Remote Sensing Techniques Based on Urban Forest Green StructuresUk-Je Sung0Jeong-Hee Eum1Kyung-Jin Chung2Department of Landscape Architecture, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of KoreaDepartment of Landscape Architecture, Kyungpook National University, 80 Daehak-ro, Buk-gu, Daegu 41566, Republic of KoreaHuron Network Co., Ltd., 5, Gunpocheomdansaneop 2-ro 22beon-gil, Gunpo-si 15880, Gyeonggi-do, Republic of KoreaThis study evaluated whether tree object segmentation using remote sensing techniques could be effectively conducted according to the green structures of urban forests. The remote sensing techniques used were handheld LiDAR and UAV-based photogrammetry. The data collected from both methods were merged to complement each other’s limitations. The green structures of the study area were classified into three types based on the distance between canopy trees and the presence of shrubs. The ability to individually classify trees within each of the three types of green structures was then evaluated. The evaluation method was to assess the success rate by comparing the actual number of trees, which were visually counted in the field, with the number of tree objects classified in the study. To perform semantic segmentation of tree objects, a preprocessing step was conducted to extract only the data related to tree structures from the data collected through remote sensing techniques. The preprocessing steps included data merging, noise removal, separation of DTM and DSM, and separation of green areas and structures. The analysis results showed that tree object recognition was not efficient when the green structures were complex and mixed, and the recognition rate was highest when only canopy trees were present, and the canopies did not overlap. Therefore, when observing in high-density areas, the semantic segmentation algorithm’s variables should be adjusted to narrow the object recognition range, and additional observations in winter are needed to compensate for areas obscured by leaves. By improving data collection methods and systematizing the analysis methods according to the green structures, the object recognition process can be enhanced.https://www.mdpi.com/2073-445X/13/11/1856LiDARphotogrammetrysemantic segmentationpoint cloud |
| spellingShingle | Uk-Je Sung Jeong-Hee Eum Kyung-Jin Chung Evaluation of Tree Object Segmentation Performance for Individual Tree Recognition Using Remote Sensing Techniques Based on Urban Forest Green Structures Land LiDAR photogrammetry semantic segmentation point cloud |
| title | Evaluation of Tree Object Segmentation Performance for Individual Tree Recognition Using Remote Sensing Techniques Based on Urban Forest Green Structures |
| title_full | Evaluation of Tree Object Segmentation Performance for Individual Tree Recognition Using Remote Sensing Techniques Based on Urban Forest Green Structures |
| title_fullStr | Evaluation of Tree Object Segmentation Performance for Individual Tree Recognition Using Remote Sensing Techniques Based on Urban Forest Green Structures |
| title_full_unstemmed | Evaluation of Tree Object Segmentation Performance for Individual Tree Recognition Using Remote Sensing Techniques Based on Urban Forest Green Structures |
| title_short | Evaluation of Tree Object Segmentation Performance for Individual Tree Recognition Using Remote Sensing Techniques Based on Urban Forest Green Structures |
| title_sort | evaluation of tree object segmentation performance for individual tree recognition using remote sensing techniques based on urban forest green structures |
| topic | LiDAR photogrammetry semantic segmentation point cloud |
| url | https://www.mdpi.com/2073-445X/13/11/1856 |
| work_keys_str_mv | AT ukjesung evaluationoftreeobjectsegmentationperformanceforindividualtreerecognitionusingremotesensingtechniquesbasedonurbanforestgreenstructures AT jeongheeeum evaluationoftreeobjectsegmentationperformanceforindividualtreerecognitionusingremotesensingtechniquesbasedonurbanforestgreenstructures AT kyungjinchung evaluationoftreeobjectsegmentationperformanceforindividualtreerecognitionusingremotesensingtechniquesbasedonurbanforestgreenstructures |