Convergence in key month of phenology-based mangrove species classification using sentinel-2 imagery data: insights from structural and physiological indices
Accurate classification of mangrove species is essential for sustainable conservation and precise carbon sink estimation. Integrating phenological information holds promise for enhancing species distinguishability and improving classification accuracy. However, further research is needed to clarify...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2528651 |
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| author | Yangcan Bao Xiaofeng Lin Mingming Jia Zhongyong Xiao Cuiping Wang Jiangfu Liao Yinghui Zhang |
| author_facet | Yangcan Bao Xiaofeng Lin Mingming Jia Zhongyong Xiao Cuiping Wang Jiangfu Liao Yinghui Zhang |
| author_sort | Yangcan Bao |
| collection | DOAJ |
| description | Accurate classification of mangrove species is essential for sustainable conservation and precise carbon sink estimation. Integrating phenological information holds promise for enhancing species distinguishability and improving classification accuracy. However, further research is needed to clarify the roles of vegetation indices (VIs) and to identify the key phenological months for mangrove classification. In this study, a random forest algorithm (RF) and Sentinel-2 time series data were applied using the Google Earth Engine platform. Results showed that structural and physiological VIs significantly improved classification accuracy by 7–43% compared to the initial accuracy. Quantification of the variable importance using RF revealed that the structural and physiological VIs exhibited dynamic key month variations in spring and summer, but they maintained temporal stability during autumn and winter. Moreover, classification based on key phenological months reduced data redundancy and achieved higher accuracy than using all monthly VIs. The highest overall accuracy of the structural VIs achieved a stable accuracy (88 ± 4%), while the physiological VIs exhibited two-stage differentiation, with some achieving accuracies of greater than 91% and others with accuracies of less than 70%. The results of this study highlight the key phenological patterns and provide guidance for VI selection in future research. |
| format | Article |
| id | doaj-art-ba558dbead9e4f54ba176ae84cd7ea2e |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-ba558dbead9e4f54ba176ae84cd7ea2e2025-08-25T11:28:35ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2528651Convergence in key month of phenology-based mangrove species classification using sentinel-2 imagery data: insights from structural and physiological indicesYangcan Bao0Xiaofeng Lin1Mingming Jia2Zhongyong Xiao3Cuiping Wang4Jiangfu Liao5Yinghui Zhang6College of Harbour and Coastal Engineering, Jimei University, Xiamen, People’s Republic of ChinaCollege of Harbour and Coastal Engineering, Jimei University, Xiamen, People’s Republic of ChinaState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, People’s Republic of ChinaCollege of Harbour and Coastal Engineering, Jimei University, Xiamen, People’s Republic of ChinaCollege of Harbour and Coastal Engineering, Jimei University, Xiamen, People’s Republic of ChinaComputer Engineering College, Jimei University, Xiamen, People’s Republic of ChinaMNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, People’s Republic of ChinaAccurate classification of mangrove species is essential for sustainable conservation and precise carbon sink estimation. Integrating phenological information holds promise for enhancing species distinguishability and improving classification accuracy. However, further research is needed to clarify the roles of vegetation indices (VIs) and to identify the key phenological months for mangrove classification. In this study, a random forest algorithm (RF) and Sentinel-2 time series data were applied using the Google Earth Engine platform. Results showed that structural and physiological VIs significantly improved classification accuracy by 7–43% compared to the initial accuracy. Quantification of the variable importance using RF revealed that the structural and physiological VIs exhibited dynamic key month variations in spring and summer, but they maintained temporal stability during autumn and winter. Moreover, classification based on key phenological months reduced data redundancy and achieved higher accuracy than using all monthly VIs. The highest overall accuracy of the structural VIs achieved a stable accuracy (88 ± 4%), while the physiological VIs exhibited two-stage differentiation, with some achieving accuracies of greater than 91% and others with accuracies of less than 70%. The results of this study highlight the key phenological patterns and provide guidance for VI selection in future research.https://www.tandfonline.com/doi/10.1080/17538947.2025.2528651Mangrove species mappingphenologystructural vegetation indexphysiological vegetation indexkey months |
| spellingShingle | Yangcan Bao Xiaofeng Lin Mingming Jia Zhongyong Xiao Cuiping Wang Jiangfu Liao Yinghui Zhang Convergence in key month of phenology-based mangrove species classification using sentinel-2 imagery data: insights from structural and physiological indices International Journal of Digital Earth Mangrove species mapping phenology structural vegetation index physiological vegetation index key months |
| title | Convergence in key month of phenology-based mangrove species classification using sentinel-2 imagery data: insights from structural and physiological indices |
| title_full | Convergence in key month of phenology-based mangrove species classification using sentinel-2 imagery data: insights from structural and physiological indices |
| title_fullStr | Convergence in key month of phenology-based mangrove species classification using sentinel-2 imagery data: insights from structural and physiological indices |
| title_full_unstemmed | Convergence in key month of phenology-based mangrove species classification using sentinel-2 imagery data: insights from structural and physiological indices |
| title_short | Convergence in key month of phenology-based mangrove species classification using sentinel-2 imagery data: insights from structural and physiological indices |
| title_sort | convergence in key month of phenology based mangrove species classification using sentinel 2 imagery data insights from structural and physiological indices |
| topic | Mangrove species mapping phenology structural vegetation index physiological vegetation index key months |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2528651 |
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