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遥感技术与应用  2021, Vol. 36 Issue (4): 751-759    DOI: 10.11873/j.issn.1004-0323.2021.4.0751
湿地遥感专栏     
基于Google Earth Engine的南方滨海盐沼植被时空演变特征分析
陈康明1,2(),朱旭东1,2,3,4()
1.滨海湿地生态系统教育部重点实验室(厦门大学),福建 厦门 361102
2.厦门大学环境与生态学院,福建 厦门 361102
3.厦门大学海洋与海岸带发展研究院,福建 厦门 361102
4.南方海洋科学与工程广东省实验室(珠海),广东 珠海 519000
Remote Sensing of Spatio-temporal Dynamics of Saltmarsh Vegetation along South China Coast based on Google Earth Engine
Kangming Chen1,2(),Xudong Zhu1,2,3,4()
1.Key Laboratory of the Coastal and Wetland Ecosystems (Ministry of Education),Xiamen 361102,China
2.College of the Environment and Ecology,Xiamen University,Xiamen 361102,China
3.Coastal and Ocean Management Institute,Xiamen University,Xiamen 361102,China
4.Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai),Zhuhai 519000,China
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摘要:

掌握滨海盐沼植被时空演变规律是科学开展滨海湿地生态系统管理的基础。滨海盐沼植物互花米草在中国海岸潮间带快速入侵与扩散,显著改变了原有滨海湿地的结构与功能,给滨海湿地保护与管理带来巨大的挑战。目前针对滨海盐沼植被时空动态的大尺度遥感分析还十分有限,人们对滨海盐沼植被空间分布的历史演变规律及其控制机制还缺乏足够的了解。实验基于Google Earth Engine平台和Landsat长时序历史影像,利用连续变化检测和分类算法反演近30 a中国南方(浙江以南)滨海盐沼植被的时空分布,分析潮汐淹水对滨海盐沼植被时空分布的影响。结果表明:①滨海盐沼植被总面积在2000~2004年出现短暂下降,之后呈现持续增长趋势;②滨海盐沼植被面积存在3种增长模式——波动、线性和指数增长;③滨海盐沼植被面积与淹水概率之间近似呈正态分布规律,植被时空分布表现为从低淹水区逐渐向高淹水区扩散的演变趋势。研究结果有助于理解滨海盐沼植被时空演变规律,为滨海湿地的科学管理提供决策支持。

关键词: 滨海湿地盐沼植被连续变化检测和分类Google Earth EngineLandsat    
Abstract:

Understanding the spatial and temporal evolution of coastal saltmarsh wetland distribution is the baseofscientific management of coastal wetland ecosystems. Spartina alterniflora has rapidly invaded and spread in the coastal intertidal zone of China, which has significantly changed the structure and function of the native coastal wetlands, leading to great challenges to coastal wetland protection and management. At present, the large-scale remote sensing analysis of the spatial and temporal dynamics of coastal saltmarsh vegetation is very limited, and there is still insufficient understanding of the historical evolution of saltmarsh spatial distribution and its control mechanisms. Based on the Google Earth Engine platform and Landsat imagery, this study usedcontinuous change detection and classification algorithm to obtain the spatial and temporal distribution of saltmarsh vegetation in coastal wetlands in southern China (south of Zhejiang Province) during the past three decades, and then analyzed the impact of tidal flooding on the spatial and temporal distribution of saltmarsh vegetation. The results showed that: (1) The total distribution area of saltmarsh vegetation decreased from 2000 to 2004, and then showed a continuously growing trend; (2) There were three growthmodes of saltmarsh vegetation: fluctuating, linear, and exponential growth; (3) The distribution of saltmarsh vegetation and the frequency of flooding showed a hump-like spatial pattern, and the spatial and temporal distribution of saltmarsh vegetation evolved from less to more inundated area over the intertidal zone. This study helps to understand the spatial and temporal evolution of coastal wetland vegetation and provides decision support for the scientific management of coastal wetlands.

Key words: Coastal wetlands    Saltmarsh vegetation    Continuous change detection and classification    Google Earth Engine    Landsat
收稿日期: 2020-05-21 出版日期: 2021-09-26
ZTFLH:  TP79  
基金资助: 国家自然科学基金项目(31600368);厦门大学校长基金(20720190112)
通讯作者: 朱旭东     E-mail: 33120171151461@stu.xmu.edu.cn;xdzhu@xmu.edu.cn
作者简介: 陈康明(1994-),男,江西吉安人,硕士研究生,主要从事滨海湿地遥感研究。E?mail:33120171151461@stu.xmu.edu.cn
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引用本文:

陈康明,朱旭东. 基于Google Earth Engine的南方滨海盐沼植被时空演变特征分析[J]. 遥感技术与应用, 2021, 36(4): 751-759.

Kangming Chen,Xudong Zhu. Remote Sensing of Spatio-temporal Dynamics of Saltmarsh Vegetation along South China Coast based on Google Earth Engine. Remote Sensing Technology and Application, 2021, 36(4): 751-759.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2021.4.0751        http://www.rsta.ac.cn/CN/Y2021/V36/I4/751

图1  研究区与Landsat影像覆盖范围审图号:GS(2020)4632
图2  基于CCDC算法拟合NDVI时序数据示意图
图3  研究区Landsat影像总像元、清晰像元数量的空间分布与Landsat影像总数量的时间变化审图号:GS(2020)4632
每类中实际的样本数量
盐沼植被红树林光滩水体森林建筑其他养殖塘用户精度/%

每类

中预

测的

样本

数量

盐沼植被1 330250027198.6
红树林11 33410304398.9
光滩031 3393000399.2
水体0051 338000599.1
森林60001 30322396.6
建筑000001 32520398.2
其他482029461 2154190.1
养殖塘15712221 29796.1
生产者精度/%98.698.998.399.697.396.192.595.497.1
表1  随机森林分类精度评估
图4  典型区地物分类空间分布与研究区盐沼植被总面积年际变化审图号:GS(2020)4632
图5  南方各省盐沼植被总面积年际变化
图6  铁山港(TS)盐沼植被总面积年际变化与空间分布历史演变
图7  云霄(YX)盐沼植被总面积年际变化与空间分布历史演变
图8  九龙江(JL)盐沼植被总面积年际变化与空间分布历史演变
图9  不同年份盐沼植被分布面积随淹水概率的变化
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