Remote Sensing Extraction Method of <i>Illicium verum</i> Based on Functional Characteristics of Vegetation Canopy
With the rapid development of remote sensing technology, researchers have attempted to improve the accuracy of tree species classifications from both data sources and methods. Although previous studies on tree species recognition have utilized the spectral and textural features of remote sensing ima...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/24/6248 |
| _version_ | 1827637125409931264 |
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| author | Zhuoyao Zhang Xiangnan Liu Lihong Zhu Junji Li Yue Zhang |
| author_facet | Zhuoyao Zhang Xiangnan Liu Lihong Zhu Junji Li Yue Zhang |
| author_sort | Zhuoyao Zhang |
| collection | DOAJ |
| description | With the rapid development of remote sensing technology, researchers have attempted to improve the accuracy of tree species classifications from both data sources and methods. Although previous studies on tree species recognition have utilized the spectral and textural features of remote sensing images, they are unable to effectively extract tree species due to the problems of “same object with different spectrum” and “foreign object with the same spectrum”. Therefore, this study introduces vegetation functional datasets to further improve tree species classification. Using vegetation functional datasets, Sentinel-2 (S2) spectral datasets, and environmental datasets, combined with a Random Forest (RF) model, the classification of six types of land cover in Leye, Guangxi was completed and the planting distribution of <i>Illicium verum</i> in Leye County was extracted. Our results showed that the combination of vegetation functional datasets, S2 spectral datasets, and environmental datasets provided the highest overall accuracy (OA) (0.8671), Kappa coefficient (0.8382), and F1-Score (0.79). We believe that the vegetation functional datasets can enhance the accuracy of <i>Illicium verum</i> classification and provide new directions for tree species identification research. If vegetation functional datasets from more tree species are obtained in the future, we can extend them to the level of multiple tree species, and this approach may help to extract more information about forest species from remote sensing data in future studies. |
| first_indexed | 2024-03-09T15:54:56Z |
| format | Article |
| id | doaj.art-e15859f6a0774e53b8bd5c89b26f021f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T15:54:56Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e15859f6a0774e53b8bd5c89b26f021f2023-11-24T17:46:32ZengMDPI AGRemote Sensing2072-42922022-12-011424624810.3390/rs14246248Remote Sensing Extraction Method of <i>Illicium verum</i> Based on Functional Characteristics of Vegetation CanopyZhuoyao Zhang0Xiangnan Liu1Lihong Zhu2Junji Li3Yue Zhang4School of Information Engineering, China University of Geosciences, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaSchool of Information Engineering, China University of Geosciences, Beijing 100083, ChinaWith the rapid development of remote sensing technology, researchers have attempted to improve the accuracy of tree species classifications from both data sources and methods. Although previous studies on tree species recognition have utilized the spectral and textural features of remote sensing images, they are unable to effectively extract tree species due to the problems of “same object with different spectrum” and “foreign object with the same spectrum”. Therefore, this study introduces vegetation functional datasets to further improve tree species classification. Using vegetation functional datasets, Sentinel-2 (S2) spectral datasets, and environmental datasets, combined with a Random Forest (RF) model, the classification of six types of land cover in Leye, Guangxi was completed and the planting distribution of <i>Illicium verum</i> in Leye County was extracted. Our results showed that the combination of vegetation functional datasets, S2 spectral datasets, and environmental datasets provided the highest overall accuracy (OA) (0.8671), Kappa coefficient (0.8382), and F1-Score (0.79). We believe that the vegetation functional datasets can enhance the accuracy of <i>Illicium verum</i> classification and provide new directions for tree species identification research. If vegetation functional datasets from more tree species are obtained in the future, we can extend them to the level of multiple tree species, and this approach may help to extract more information about forest species from remote sensing data in future studies.https://www.mdpi.com/2072-4292/14/24/6248tree species classificationvegetation functional datasetsrandom forest modelGoogle Earth EngineSentinel-2 imagery<i>Illicium verum</i> |
| spellingShingle | Zhuoyao Zhang Xiangnan Liu Lihong Zhu Junji Li Yue Zhang Remote Sensing Extraction Method of <i>Illicium verum</i> Based on Functional Characteristics of Vegetation Canopy Remote Sensing tree species classification vegetation functional datasets random forest model Google Earth Engine Sentinel-2 imagery <i>Illicium verum</i> |
| title | Remote Sensing Extraction Method of <i>Illicium verum</i> Based on Functional Characteristics of Vegetation Canopy |
| title_full | Remote Sensing Extraction Method of <i>Illicium verum</i> Based on Functional Characteristics of Vegetation Canopy |
| title_fullStr | Remote Sensing Extraction Method of <i>Illicium verum</i> Based on Functional Characteristics of Vegetation Canopy |
| title_full_unstemmed | Remote Sensing Extraction Method of <i>Illicium verum</i> Based on Functional Characteristics of Vegetation Canopy |
| title_short | Remote Sensing Extraction Method of <i>Illicium verum</i> Based on Functional Characteristics of Vegetation Canopy |
| title_sort | remote sensing extraction method of i illicium verum i based on functional characteristics of vegetation canopy |
| topic | tree species classification vegetation functional datasets random forest model Google Earth Engine Sentinel-2 imagery <i>Illicium verum</i> |
| url | https://www.mdpi.com/2072-4292/14/24/6248 |
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