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Article: Synergistic estimation of mangrove canopy height across coastal China: Integrating SDGSAT-1 multispectral data with Sentinel-1/2 time-series imagery

TitleSynergistic estimation of mangrove canopy height across coastal China: Integrating SDGSAT-1 multispectral data with Sentinel-1/2 time-series imagery
Authors
KeywordsMangrove canopy height (MCH)
SDGSAT-1 Multispectral Imager for Inshore (MII)
Sential-1 SAR
Time series Sentinel-2 MSI
UAV-LiDAR
XGBoost regression model
Issue Date1-Jun-2025
PublisherElsevier
Citation
Remote Sensing of Environment, 2025, v. 323 How to Cite?
Abstract

Mangrove canopy height (MCH) is a critical indicator used to evaluate blue carbon sequestration and biodiversity conservation. However, mapping MCH is challenging because of the dense tree canopy and fluctuating tide conditions. To solve the issue, this study developed a novel approach to retrieve MCH by training a robust XGBoost regression model using UAV-LiDAR, SDGSAT-1, and time series Sentinel-1 SAR and Sentinel-2 MultiSpectral Instrument imagery. The approach was applied to mangrove forests along China's coast. The study resulted in a 10 m resolution MCH map and so named China's mangrove canopy height (CMCH). The accuracy of CMCH was assessed using in-situ and UAV-LiDAR data, achieving an R2 of 0.84 and an RMSE of 1.19 m. Band 6 from SDGSAT-1, the only available 10 m resolution red edge spectral band of current available satellite data, was identified as the most crucial feature for predicting MCH. After analyzing the geographic characteristics of CMCH at species level, we had three innovative and quantitative discoveries. Firstly, the mean height of mangrove forests in China was 6.0 m, significantly lower than the global average of 12.7 m. Secondly, the height of mangrove forests in China was found to decrease with increasing latitude. Thirdly, the exotic S. apetala was identified as the tallest mangrove species in China, with the highest trees in 18.7 m along the coasts of Inner Deep Bay. To the best of our knowledge, this is the first national-scale study to investigate the geographic characteristics of MCH at species level. The resultant CMCH map and species-level findings provide essential information for managing mangrove ecosystems in China. The technical methodology employed has the potential to be expanded globally, thereby enhancing the execution of the UN's Sustainable Development Goals related to coastal and marine ecosystems. Additionally, it can contribute to the safeguarding of nature, fostering the preservation of biodiversity.


Persistent Identifierhttp://hdl.handle.net/10722/360756
ISSN
2023 Impact Factor: 11.1
2023 SCImago Journal Rankings: 4.310

 

DC FieldValueLanguage
dc.contributor.authorJia, Mingming-
dc.contributor.authorZhang, Rong-
dc.contributor.authorZhao, Chuanpeng-
dc.contributor.authorZhou, Yaming-
dc.contributor.authorRen, Chunying-
dc.contributor.authorMao, Dehua-
dc.contributor.authorLi, Huiying-
dc.contributor.authorSun, Genyun-
dc.contributor.authorZhang, Hongsheng-
dc.contributor.authorYu, Wensen-
dc.contributor.authorWang, Zongming-
dc.contributor.authorWang, Yeqiao-
dc.date.accessioned2025-09-13T00:36:13Z-
dc.date.available2025-09-13T00:36:13Z-
dc.date.issued2025-06-01-
dc.identifier.citationRemote Sensing of Environment, 2025, v. 323-
dc.identifier.issn0034-4257-
dc.identifier.urihttp://hdl.handle.net/10722/360756-
dc.description.abstract<p>Mangrove canopy height (MCH) is a critical indicator used to evaluate blue carbon sequestration and biodiversity conservation. However, mapping MCH is challenging because of the dense tree canopy and fluctuating tide conditions. To solve the issue, this study developed a novel approach to retrieve MCH by training a robust XGBoost regression model using UAV-LiDAR, SDGSAT-1, and time series Sentinel-1 SAR and Sentinel-2 MultiSpectral Instrument imagery. The approach was applied to mangrove forests along China's coast. The study resulted in a 10 m resolution MCH map and so named China's mangrove canopy height (CMCH). The accuracy of CMCH was assessed using in-situ and UAV-LiDAR data, achieving an R<sup>2</sup> of 0.84 and an RMSE of 1.19 m. Band 6 from SDGSAT-1, the only available 10 m resolution red edge spectral band of current available satellite data, was identified as the most crucial feature for predicting MCH. After analyzing the geographic characteristics of CMCH at species level, we had three innovative and quantitative discoveries. Firstly, the mean height of mangrove forests in China was 6.0 m, significantly lower than the global average of 12.7 m. Secondly, the height of mangrove forests in China was found to decrease with increasing latitude. Thirdly, the exotic S. apetala was identified as the tallest mangrove species in China, with the highest trees in 18.7 m along the coasts of Inner Deep Bay. To the best of our knowledge, this is the first national-scale study to investigate the geographic characteristics of MCH at species level. The resultant CMCH map and species-level findings provide essential information for managing mangrove ecosystems in China. The technical methodology employed has the potential to be expanded globally, thereby enhancing the execution of the UN's Sustainable Development Goals related to coastal and marine ecosystems. Additionally, it can contribute to the safeguarding of nature, fostering the preservation of biodiversity.</p>-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofRemote Sensing of Environment-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectMangrove canopy height (MCH)-
dc.subjectSDGSAT-1 Multispectral Imager for Inshore (MII)-
dc.subjectSential-1 SAR-
dc.subjectTime series Sentinel-2 MSI-
dc.subjectUAV-LiDAR-
dc.subjectXGBoost regression model-
dc.titleSynergistic estimation of mangrove canopy height across coastal China: Integrating SDGSAT-1 multispectral data with Sentinel-1/2 time-series imagery -
dc.typeArticle-
dc.identifier.doi10.1016/j.rse.2025.114719-
dc.identifier.scopuseid_2-s2.0-105001365707-
dc.identifier.volume323-
dc.identifier.eissn1879-0704-
dc.identifier.issnl0034-4257-

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