Tropical Species Classification with Structural Traits Using Handheld Laser Scanning Data
Information about tree species plays a pivotal role in sustainable forest management. Light detection and ranging (LiDAR) technology has demonstrated its potential to obtain species information using the structural features of trees. Several studies have explored the structural properties of boreal...
| Հիմնական հեղինակներ: | , , |
|---|---|
| Ձևաչափ: | Հոդված |
| Լեզու: | English |
| Հրապարակվել է: |
MDPI AG
2022-04-01
|
| Շարք: | Remote Sensing |
| Խորագրեր: | |
| Առցանց հասանելիություն: | https://www.mdpi.com/2072-4292/14/8/1948 |
| _version_ | 1827626791174406144 |
|---|---|
| author | Meilian Wang Man Sing Wong Sawaid Abbas |
| author_facet | Meilian Wang Man Sing Wong Sawaid Abbas |
| author_sort | Meilian Wang |
| collection | DOAJ |
| description | Information about tree species plays a pivotal role in sustainable forest management. Light detection and ranging (LiDAR) technology has demonstrated its potential to obtain species information using the structural features of trees. Several studies have explored the structural properties of boreal or temperate trees from terrestrial laser scanning (TLS) data and applied them to species classification, but the study of structural properties of tropical trees for species classification is rare. Compared to conventional static TLS, handheld laser scanning (HLS) is able to effectively capture point clouds of an individual tree with flexible movability. Therefore, in this study, we characterized the structural features of tropical species from HLS data as 23 LiDAR structural parameters, involving 6 branch, 11 crown and 6 entire tree parameters, and used these parameters to classify the species via 5 machine-learning (ML) models, respectively. The performance of each parameter was further evaluated and compared. Classification results showed that the employed parameters can achieve a classification accuracy of 84.09% using the support vector machine with a polynomial kernel. The evaluation of parameters indicated that it is insufficient to classify four species with only one and two parameters, but ten parameters were recommended in order to achieve satisfactory accuracy. The combination of different types of parameters, such as branch and crown parameters, can significantly improve classification accuracy. Finally, five sets of optimal parameters were suggested according to their importance and performance. This study also showed that the time- and cost-efficient HLS instrument could be a promising tool for tree-structure-related studies, such as structural parameter estimation, species classification, forest inventory, as well as sustainable tree management. |
| first_indexed | 2024-03-09T13:02:31Z |
| format | Article |
| id | doaj.art-337d02c0f8ab4b60b1036b935d6e99ef |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T13:02:31Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-337d02c0f8ab4b60b1036b935d6e99ef2023-11-30T21:51:47ZengMDPI AGRemote Sensing2072-42922022-04-01148194810.3390/rs14081948Tropical Species Classification with Structural Traits Using Handheld Laser Scanning DataMeilian Wang0Man Sing Wong1Sawaid Abbas2Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaDepartment of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, ChinaInformation about tree species plays a pivotal role in sustainable forest management. Light detection and ranging (LiDAR) technology has demonstrated its potential to obtain species information using the structural features of trees. Several studies have explored the structural properties of boreal or temperate trees from terrestrial laser scanning (TLS) data and applied them to species classification, but the study of structural properties of tropical trees for species classification is rare. Compared to conventional static TLS, handheld laser scanning (HLS) is able to effectively capture point clouds of an individual tree with flexible movability. Therefore, in this study, we characterized the structural features of tropical species from HLS data as 23 LiDAR structural parameters, involving 6 branch, 11 crown and 6 entire tree parameters, and used these parameters to classify the species via 5 machine-learning (ML) models, respectively. The performance of each parameter was further evaluated and compared. Classification results showed that the employed parameters can achieve a classification accuracy of 84.09% using the support vector machine with a polynomial kernel. The evaluation of parameters indicated that it is insufficient to classify four species with only one and two parameters, but ten parameters were recommended in order to achieve satisfactory accuracy. The combination of different types of parameters, such as branch and crown parameters, can significantly improve classification accuracy. Finally, five sets of optimal parameters were suggested according to their importance and performance. This study also showed that the time- and cost-efficient HLS instrument could be a promising tool for tree-structure-related studies, such as structural parameter estimation, species classification, forest inventory, as well as sustainable tree management.https://www.mdpi.com/2072-4292/14/8/1948structural propertiestropical specieshandheld laser scanningmachine-learning classifiersoptimal parameter sets |
| spellingShingle | Meilian Wang Man Sing Wong Sawaid Abbas Tropical Species Classification with Structural Traits Using Handheld Laser Scanning Data Remote Sensing structural properties tropical species handheld laser scanning machine-learning classifiers optimal parameter sets |
| title | Tropical Species Classification with Structural Traits Using Handheld Laser Scanning Data |
| title_full | Tropical Species Classification with Structural Traits Using Handheld Laser Scanning Data |
| title_fullStr | Tropical Species Classification with Structural Traits Using Handheld Laser Scanning Data |
| title_full_unstemmed | Tropical Species Classification with Structural Traits Using Handheld Laser Scanning Data |
| title_short | Tropical Species Classification with Structural Traits Using Handheld Laser Scanning Data |
| title_sort | tropical species classification with structural traits using handheld laser scanning data |
| topic | structural properties tropical species handheld laser scanning machine-learning classifiers optimal parameter sets |
| url | https://www.mdpi.com/2072-4292/14/8/1948 |
| work_keys_str_mv | AT meilianwang tropicalspeciesclassificationwithstructuraltraitsusinghandheldlaserscanningdata AT mansingwong tropicalspeciesclassificationwithstructuraltraitsusinghandheldlaserscanningdata AT sawaidabbas tropicalspeciesclassificationwithstructuraltraitsusinghandheldlaserscanningdata |