收稿：2021-05-21，
纸质出版：2022-02-07
移动端阅览
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GEE（Google Earth Engine）平台开发一种自动、快速、高精度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，得到的总体精度为96.5%，Kappa系数为0.95。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3种类型，面积分别为82.46 hm
2
、218.26 hm
2
和496.84 hm
2
。本研究的方法能够实现潮间带湿地的自动、快速、高精度分类，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。
Intertidal wetlands are an important part of coastal wetlands and 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 largest challenges in mapping intertidal wetlands. The key is to obtain the remote sensing images at the time with the lowest and highest tides rapidly and accurately to accurately extract information of intertidal wetlands. At present
the dense temporal resolution of Sentinel-2 images with revisit interval of 3—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
manual intervened thresholds or pre and postprocessing. This work aimed to set up an automatic
rapid
and high precision procedure that uses time series Sentinel-2 images to derive intertidal wetland information based on the Google Earth Engine 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 on the basis of the maximum spectral index composite algorithm; and (4) establishing a multilayer 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 96.5% and a kappa coefficient of 0.95. The intertidal wetlands in the Zhangjiangkou Reserve consist of mangrove forest
Spartina alterniflora
and tidal flat
with an area of 82.46
218.26
and 496.84 hm
2
respectively. Abundant tidal flat resources were mainly located on the outer edge of mangrove forest and
S. alterniflora
. Mangroves were mainly concentrated on the southwest coast of the Zhangjiang River.
S. alterniflora
was mainly distributed on the south of Zhangjiang River with good integrity
whereas 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 tide images and provides sufficient phenological information for the classification of mangrove and
S. alterniflora
. The maximal intertidal water surface can be easily obtained by combining with the modified normalized difference water index maximum value composite image and the method of extracting the largest patch area. The Normalized Difference Vegetation Index (NDVI) maximum value composite image well highlights the difference between tidal flats
water bodies
and vegetated areas. The negative NDVI maximum value composite image plays a positive role in enhancing the characteristic difference between mangrove forest and
S. alterniflora
. The proposed method 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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