%0 Dataset
%T Dataset of spatial distribution of intertidal mudflats in Australia in 2020
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/6cf064e8-fbc3-4c43-9374-a627499c1ef2
%W NCDC
%R 10.12072/ncdc.rs.db2883.2023
%A jia mingming
%A li huiying
%A yu hao
%K Mudflat
%X As an important part of the Intertidal zone ecosystem, mudflat have unique environmental regulation services and ecological benefits such as maintaining the stability of the coastline, accelerating material exchange and promoting Carbon cycle. Accurate and timely assessment of the status of Intertidal zone wetlands is crucial to achieving sustainable management objectives. This paper uses Google Earth Engine (GEE) cloud computing platform, selects the Sentinel-2 dense time series remote sensing images in 2020, integrates the Maximum spectral index composite (MSIC) algorithm and Otsu's method algorithm to build a multi-level decision tree classification model, and realizes the rapid and automatic extraction of mudflat in Intertidal zone in Australia. After vectorization, the Australian high-resolution Intertidal zone mudflat spatial distribution data set in 2020 was obtained. The extracted mudflat area was 10708.22 km2, the overall accuracy was 95.32%, and the Kappa coefficient was 0.94. The storage format of this dataset is. shp, with a time resolution of years, a spatial resolution of 10 meters, and a data volume of 87.8 M.