Mingming Jia , Rong Zhang , Chuanpeng Zhao , Yaming Zhou , Chunying Ren , Dehua Mao , Huiying Li , Genyun Sun , Hongsheng Zhang , Wensen Yu , Zongming Wang , Yeqiao Wang
{"title":"Synergistic estimation of mangrove canopy height across coastal China: Integrating SDGSAT-1 multispectral data with Sentinel-1/2 time-series imagery","authors":"Mingming Jia , Rong Zhang , Chuanpeng Zhao , Yaming Zhou , Chunying Ren , Dehua Mao , Huiying Li , Genyun Sun , Hongsheng Zhang , Wensen Yu , Zongming Wang , Yeqiao Wang","doi":"10.1016/j.rse.2025.114719","DOIUrl":null,"url":null,"abstract":"<div><div>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 <em>R</em><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 <em>S. apetala</em> 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.</div></div>","PeriodicalId":417,"journal":{"name":"Remote Sensing of Environment","volume":"323 ","pages":"Article 114719"},"PeriodicalIF":11.1000,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing of Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0034425725001233","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.