Based on the daily overpass times of high temporal resolution Planet remote sensing satellite,this paper selects imagery cor-responding to the highest and lowest tides over the past three years for five study areas.The U-Net deep learning algorithm is em-ployed to identify the land-water boundary at high and low tide and extract the mudflat extent in each study area.Coastal mudflat sub-division types are identified through visual interpretation.The results from the five study areas demonstrate that high temporal resolu-tion Planet remote sensing data can be used to rapidly extract coastal mudflat extents by the land-water boundaries at the highest and lowest tides and identify subdivision types of coastal mudflat natural resources.This approach addresses current challenges in subdivi-ding coastal mudflat natural resource units for registration and provides new technical support for physical measurement of coastal mud-flats and natural resource registration in the inventory of natural resource assets.
NETL
NSTL
万方数据
