地理学报 ›› 2021, Vol. 76 ›› Issue (7): 1680-1692.doi: 10.11821/dlxb202107008

• 气候变化与植被生态 • 上一篇    下一篇

基于Sentinel-2遥感时间序列植被物候特征的盐城滨海湿地植被分类

刘瑞清1,2(), 李加林1,3(), 孙超1, 孙伟伟1, 曹罗丹1, 田鹏1   

  1. 1.宁波大学地理科学与旅游文化学院,宁波 315211
    2.华东师范大学河口海岸学国家重点实验室,上海 200062
    3.宁波大学东海研究院,宁波 315211
  • 收稿日期:2020-09-10 修回日期:2021-03-26 出版日期:2021-07-25 发布日期:2021-09-25
  • 通讯作者: 李加林(1973-), 男, 浙江台州人, 教授, 博导, 主要从事海岸带开发与保护研究。E-mail: nbnj2001@163.com
  • 作者简介:刘瑞清(1995-), 女, 山西忻州人, 博士生, 研究方向为海岸海洋资源利用与保护。E-mail: liuruiqingwh@163.com
  • 基金资助:
    国家自然科学基金项目(U1609203);国家自然科学基金项目(41901121);浙江省自然科学基金项目(LQ20D010006);宁波市自然科学基金项目(2019A610105)

Classification of Yancheng coastal wetland vegetation based on vegetation phenological characteristics derived from Sentinel-2 time-series

LIU Ruiqing1,2(), LI Jialin1,3(), SUN Chao1, SUN Weiwei1, CAO Luodan1, TIAN Peng1   

  1. 1. Faculty of Geography, Tourism and Culture, Ningbo University, Ningbo 315211, Zhejiang, China
    2. State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200062, China
    3. Donghai Institute, Ningbo University, Ningbo 315211, Zhejiang, China
  • Received:2020-09-10 Revised:2021-03-26 Published:2021-07-25 Online:2021-09-25
  • Supported by:
    National Natural Science Foundation of China(U1609203);National Natural Science Foundation of China(41901121);Natural Science Foundation of Zhejiang Province(LQ20D010006);Natural Science Foundation of Ningbo(2019A610105)

摘要:

滨海湿地是具有重要功能的特殊海陆过渡带生态系统,精准获取滨海湿地植被时空分布信息具有重要意义。传统的湿地遥感观测研究集中于高空间、高光谱分辨率影像分类,往往受限于数据成本和覆盖范围,仅适用于小区域湿地监测。Sentinel-2A/B卫星影像时空分辨率高且免费共享,为大区域滨海湿地动态监测提供了可能。本文采用2018年Sentinel-2影像,提出像元级SAVI时间序列及双Logistic植被物候特征拟合重构模型,采用随机森林算法进行盐城滨海湿地植被分类,探讨Sentinel-2遥感时间序列植被物候特征分类方法的适用性。结果显示,分类总体精度达87.61%,Kappa系数为0.8358,分类结果与湿地实况相吻合,比常规单一时相分类精度总体提高19.57%。植被判别物候特征参数可为影像数据缺失或不足的滨海湿地分类提供不同植被的判别依据。研究表明,基于像元级时间序列植被物候特征的分类方法能实现植被群落混生带的精准分类以及对“异物同谱”植被的有效区分,对大区域滨海湿地植被分类具有很好的适用性,有效提高了滨海湿地植被分类精度。

关键词: Sentinel-2影像, 时间序列, 植被物候特征, 盐城滨海湿地, 分类制图

Abstract:

Coastal wetlands are special land-sea transitional ecosystems with important functions. It is of great significance to obtain the spatiotemporal distribution data of coastal wetland vegetation accurately. Previous wetland mapping studies focusing mainly on high spatial and spectral resolution images often have difficulties such as high data acquisition costs and limited coverage, so these methods are only suitable for small regions. Sentinel-2A/B satellite images with high spatial and temporal resolution and free sharing, make it possible for us to dynamically monitor large-area coastal wetlands. Based on Sentinel-2 images in 2018, this study proposed the pixel-level SAVI time series and double logistic vegetation phenological feature fitting reconstruction model, used a random forest algorithm to classify Yancheng coastal wetland vegetation in Jiangsu, East China, and then discussed the applicability of vegetation phenological characteristics (VPC) classification method. The results show that the overall accuracy of mapping based on VPC was 87.61%, which was 19.57% higher than that of the conventional single image classification, and the results were consistent with the actual distribution of wetlands. The vegetation discriminant phenological parameters can provide the basis for differentiating various types of vegetation, which can be applied to coastal wetland classification in the case of missing or insufficient image data. The phenological parameters have improved the method based on VPC, which can be applied to the rapid and accurate extraction of coastal vegetation and also provides new ideas to solve the problem of insufficient data in coastal wetland classification research. The method based on VPC in the pixel-level time series can achieve the accurate classification of the mixed zone of vegetation communities and the effective differentiation of "the same spectrum with different objects", which is applicable to the coastal wetland classification in large regions and improves the mapping accuracy of coastal wetland vegetation effectively.

Key words: Sentinel-2, time series, vegetation phenology characteristics, Yancheng coastal wetland, classification mapping