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 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 hm2, 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.