Spectral volume index creation and performance evaluation: A preliminary test for tree species identification
To fully mine information regarding differences among various tree species from remote sensing data and improve the accuracy of tree species recognition, this study utilized the spectral reflection value, wavelength, and time as parameters and employed three algorithms to create an expression for th...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-06-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023044110 |
| _version_ | 1797805804898746368 |
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| author | Huaipeng Liu |
| author_facet | Huaipeng Liu |
| author_sort | Huaipeng Liu |
| collection | DOAJ |
| description | To fully mine information regarding differences among various tree species from remote sensing data and improve the accuracy of tree species recognition, this study utilized the spectral reflection value, wavelength, and time as parameters and employed three algorithms to create an expression for the spectral volume index (SVI). Then, data were obtained by applying RedEdge-MX to four phases, SVI features were extracted, and a mixed feature set of spectral band + texture + digital surface model + SVI was constructed. A random forest algorithm was employed to determine the importance of the SVI features and derive the optimal feature set for tree species classification. The additional objectives were to determine if the SVI features have an active role in tree species classification and which algorithm is more conducive for extracting useful SVI features. The SVI features extracted with volume constraints exhibit better performance in tree species recognition than those extracted without volume constraints. Moreover, the SVI features extracted using a variable-constrained volume were better than those extracted using a constant-constrained volume. The combination of SVI features could improve the accuracy of tree species recognition (the highest overall accuracy was 92.76%), but the improvement effect was limited (the value was 92.16% when SVI features were not combined). These findings show that the SVI obtained using this method could be used to mine the difference information of tree species in images to a certain extent and hence, could be used in tree species identification. |
| first_indexed | 2024-03-13T05:57:37Z |
| format | Article |
| id | doaj.art-1edebdbe3d674d89b20c00d8865e7fd6 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-13T05:57:37Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-1edebdbe3d674d89b20c00d8865e7fd62023-06-13T04:12:40ZengElsevierHeliyon2405-84402023-06-0196e17203Spectral volume index creation and performance evaluation: A preliminary test for tree species identificationHuaipeng Liu0School of Land and Tourism, Luoyang Normal University, Luoyang, Henan Province, 471934, ChinaTo fully mine information regarding differences among various tree species from remote sensing data and improve the accuracy of tree species recognition, this study utilized the spectral reflection value, wavelength, and time as parameters and employed three algorithms to create an expression for the spectral volume index (SVI). Then, data were obtained by applying RedEdge-MX to four phases, SVI features were extracted, and a mixed feature set of spectral band + texture + digital surface model + SVI was constructed. A random forest algorithm was employed to determine the importance of the SVI features and derive the optimal feature set for tree species classification. The additional objectives were to determine if the SVI features have an active role in tree species classification and which algorithm is more conducive for extracting useful SVI features. The SVI features extracted with volume constraints exhibit better performance in tree species recognition than those extracted without volume constraints. Moreover, the SVI features extracted using a variable-constrained volume were better than those extracted using a constant-constrained volume. The combination of SVI features could improve the accuracy of tree species recognition (the highest overall accuracy was 92.76%), but the improvement effect was limited (the value was 92.16% when SVI features were not combined). These findings show that the SVI obtained using this method could be used to mine the difference information of tree species in images to a certain extent and hence, could be used in tree species identification.http://www.sciencedirect.com/science/article/pii/S2405844023044110Four-seasonal RedEdge-MX dataTree species identificationSpectral volume index creationPerformance evaluationRandom forest algorithm |
| spellingShingle | Huaipeng Liu Spectral volume index creation and performance evaluation: A preliminary test for tree species identification Heliyon Four-seasonal RedEdge-MX data Tree species identification Spectral volume index creation Performance evaluation Random forest algorithm |
| title | Spectral volume index creation and performance evaluation: A preliminary test for tree species identification |
| title_full | Spectral volume index creation and performance evaluation: A preliminary test for tree species identification |
| title_fullStr | Spectral volume index creation and performance evaluation: A preliminary test for tree species identification |
| title_full_unstemmed | Spectral volume index creation and performance evaluation: A preliminary test for tree species identification |
| title_short | Spectral volume index creation and performance evaluation: A preliminary test for tree species identification |
| title_sort | spectral volume index creation and performance evaluation a preliminary test for tree species identification |
| topic | Four-seasonal RedEdge-MX data Tree species identification Spectral volume index creation Performance evaluation Random forest algorithm |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023044110 |
| work_keys_str_mv | AT huaipengliu spectralvolumeindexcreationandperformanceevaluationapreliminarytestfortreespeciesidentification |