%0 Dataset
%T Australian Intertidal mudflat Spatial Distribution Data Set (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 intertidal ecosystem, mudflat have unique environmental regulation services and ecological benefits such as maintaining the stability of coastline, accelerating material exchange and promoting carbon cycle. Accurate and timely assessment of the current status of intertidal wetlands is crucial for achieving sustainable management goals. This paper uses Google Earth Engine (GEE) cloud computing platform, selects Sentinel-2 dense time series remote sensing images in 2020, integrates the Maximum spectral index composite (MSIC) algorithm and Otsu algorithm to build a multi-level decision tree classification model, and realizes the rapid and automatic extraction of mudflat in the intertidal zone of Australia. The spatial distribution data set of mudflat in Australia's high resolution intertidal zone in 2020 was obtained through vectorization. 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 and a spatial resolution of 10m, and a data volume of 87.8M.