Investigating mangrove canopy phenology in coastal areas of China using time series Sentinel-1/2 images
Investigating mangrove canopy phenology in coastal areas of China using time series Sentinel-1/2 images
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DOI：
10.1016/j.ecolind.2023.110815
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发表时间：
2023-08-19
影响因子：
6.9
通讯作者：
Liu, Kai
中科院分区：
文献类型：
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作者：
Cao, Jingjing;Xu, Xin;Liu, Kai
植被生产力高的红树林在沿海蓝碳生态系统中发挥着至关重要的作用。准确监测红树林冠层物候对于提高恢复种植成活率、维持红树林生态系统的健康和可持续性至关重要。遥感技术已普遍用于监测植被物候，但在探索中国沿海红树林遥感物候方面仍存在知识空白。本研究利用基于Sentinel-1和Sentinel-2时间序列的遥感指数研究了2016-2020年中国沿海两个典型红树林的物候特征，比较了不同遥感指数和空间尺度的红树林物候检测性能，并基于气象数据探讨了环境因素的影响。结果表明，增强植被指数（EVI）、红边带指数（NDRE2）、物候指数（NDPI）和雷达植被指数（RVI）可以有效地表征红树林物候轨迹。 Sentinel-2数据可以精确描述红树林的物候特征，与Landsat-8和MODIS数据相比，与地面观测物候数据的相关性最高（r = -0.581）。降水、湿度和风速等气象因素主要导致两个研究地点红树林物候的差异。这一发现可以提高我们对中国沿海红树林物候特征的认识，有助于地方政府制定适当的红树林恢复和管理政策。
Mangrove forests with high vegetation productivity play crucial roles in the coastal blue carbon ecosystem. Accurate monitoring of mangrove canopy phenology is essential to improve the survival rate of restoration plantings and maintain the health and sustainability of mangrove ecosystems. Remote sensing technology has been commonly used for monitoring vegetation phenology, while there remain knowledge gaps in exploring the remotely-sensed phenology of mangrove forests in coastal China. In this study, we investigated the phenological characteristics of two typical mangrove sites in coastal China using remote sensing indices based on Sentinel-1 and Sentinel-2 time series during 2016-2020, compared the performances of mangrove phenology detection across different remote sensing indices and spatial scales, and explored the influences of environmental factors based on meteorological data. The results demonstrated that enhanced vegetation index (EVI), red-edge band index (NDRE2), phenology index (NDPI), and radar vegetation index (RVI) were efficient in characterizing mangrove phenological trajectories. Sentinel-2 data can precisely describe the phenological characteristics of mangroves and has the highest correlation with ground-observed phenology data (r = -0.581), when compared to Landsat-8 and MODIS data. The meteorological factors of precipitation, humidity, and wind speed mainly led to the differences in mangrove phenology across the two study sites. This finding can improve our understanding of the phenological characteristics of mangrove forests in coastal China, which facilitates local governments to develop appropriate mangrove restoration and management policies.