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引用本文:

DOI:

10.11834/jrs.20211311

收稿日期:

2021-05-06

修改日期:

2021-07-31

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基于Sentinel-2密集时间序列数据和Google Earth Engine的潮间带湿地快速自动分类
程丽娜1,2, 钟才荣3, 李晓燕1, 贾明明2, 王宗明2,4, 毛德华2
1.吉林大学 地球科学学院;2.中国科学院 东北地理与农业生态研究所 湿地生态与环境重点实验室;3.海南省林业科学研究院海南省红树林研究院;4.国家地球系统科学数据中心
摘要:

潮间带湿地是滨海湿地的重要组成部分,具有维持生物多样性、促进碳汇等重要生态功能。及时、准确地掌握潮间带湿地现状是实现潮间带湿地可持续管理目标的基础。先前的潮间带湿地分类研究依赖于训练样本、人工设定阈值或后处理等,本研究基于Google Earth Engine(GEE)平台开发一种自动、精准、快速的潮间带湿地分类方法。该方法首先构建高质量密集时序Sentinel-2影像堆栈;然后,分析不同潮间带湿地的遥感特征,基于最大光谱指数合成算法(MSIC)和大津算法(Otsu)建立多层自动决策树分类模型。应用该方法对2020年福建漳江口红树林自然保护区的潮间带湿地进行分类,得到总体精度为95.48%,Kappa 系数为0.94。漳江口红树林保护区内潮间带湿地包括红树林、互花米草和滩涂三种类型,面积分别为82.46 hm2、218.26 hm2和496.84 hm2。本研究的方法能够实现潮间带湿地的自动、精准、快速分类,对潮间带和其他内陆湿地的精准分类研究具有重要的借鉴价值。

Rapid and automatic classification of intertidal wetlands based on intensive time series Sentinel-2 images and Google Earth Engine
Abstract:

Intertidal wetlands are an important part of coastal wetlands, which have crucial ecological functions such as maintaining biodiversity and promoting carbon sink. However, intertidal wetlands are severely threatened by coastal erosion, sea level rise, and human activities. Timely and accurately monitoring the status of intertidal wetlands is the basis for achieving the goal of sustainable management of intertidal wetlands. Periodic tidal inundation is one of the biggest challenges in mapping intertidal wetlands. To accurately extract information of intertidal wetlands, the key is to obtain the remote sensing images at the time with the lowest and highest tides rapidly and accurately. Nowadays, the dense temporal resolution of Sentinel-2 images with revisit interval of 2-5 days offers a great opportunity to capture the lowest and highest tides, which is vital to conduct accurate and robust delineation of intertidal wetlands. Previous efforts on intertidal wetland classification relied on either training samples, or manual intervened thresholds, or pre- and post-processing. The purpose of this work was to set up an automatic, rapid, and high precision procedure that made use of time series Sentinel-2 images to derive intertidal wetland information based on the Google Earth Engine (GEE) platform. The methodology includes four steps: (1) building a high-quality dense time-series image stack; (2) deeply analyzing the time series remote sensing characteristics of different wetland types and selecting appropriate spectral indexes; (3) creating the maximal water surface image, the minimal water surface image and vegetation difference enhanced image based on the maximum spectral index composite (MSIC) algorithm; and (4) establishing a multi-layer automatic decision tree classification model to extract different intertidal wetlands from simple types to complex types by using the Otsu algorithm. The procedure was utilized to classify the intertidal wetlands in the Fujian Zhangjiangkou National Mangrove Nature Reserve in 2020 with an overall accuracy of 95.48% and a Kappa coefficient of 0.94. The intertidal wetlands in the Zhangjiangkou Reserve consist of mangrove forest, Spartina alterniflora, and tidal flat, with an area of 82.46 hm2, 218.26 hm2, and 496.84 hm2, respectively. Abundant tidal flats resources were mainly located on the outer edge of mangrove forest and Spartina alterniflora. Mangroves were mainly concentrated on the southwest coast of the Zhangjiang River. The Spartina alterniflora was mainly distributed on the south of Zhangjiang River with good integrity, while part of them grew on the north side of Zhangjiang River with a banding distribution. The high-quality sentinel-2 dense time series image stack increases the opportunity to obtain the lowest and highest tides images and provides sufficient phenological information for the classification of mangrove and Spartina alterniflora. The maximal intertidal water surface can be easily obtained by combining with the mNDWI maximum value composite image and the method of extracting the largest patch area. The NDVI maximum value composite image well highlights the difference between tidal flats and water body and vegetated areas. The negative NDVI maximum value composite image plays a positive role in enhancing the characteristic difference between mangrove forest and Spartina alterniflora. The method proposed in this study can realize the automatic, rapid and accurate classification of intertidal wetlands, which has important reference value for the accurate classification research of intertidal and other inland wetlands.

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