2026-10-15T04:35:59Z https://www.scidb.cn/oai

10.11922/sciencedb.00481 2020-12-24T17:32:21Z

2020-12-24 10-m-resolution tidal flat map of China’s mangrove distribution area for 2017 derived from multi-temporal SAR images doi:10.11922/sciencedb.00481 en Coastal ecosystems are under increased pressure from climate change and anthropogenic impacts. Tidal flat maps are necessary for managing, protecting, and restoring coastal ecosystems. Most of the existing tidal flat mapping approaches that are dependent on modelled tidal elevations, suffer high uncertainty over offshore, especially over areas where the tidal conditions have been changed by human-made structures and anthropogenic impacts. Meanwhile, these approaches separate the tidal flats in estuaries by a coastal distance rather than actual distance of saltwater intrusion. We propose a tidal flat mapping approach, using a new method to avoid the dependence on tidal elevations. The proposed approach synthesizes large-area satellite observations over a time period, to acquire the high and low tidal datum, and then classifies land and water under each tidal datum utilizing an adaptive binary classifier. The tidal flats are identified as areas being water during high tides and being land during low tides. Furthermore, the proposed approach uses the distribution of salt vegetation (i.e., mangroves) as a salinity proxy to particularly identify the tidal flats in estuaries. The proposed approach was applied for mapping the tidal flats in southern China (covering over a half of the coastline of China) utilizing Sentinel-1 Synthetic Aperture Radar (SAR) data from July 2016 to July 2018. The results achieved an overall accuracy of 92.4% and an agreement rate of 70.5% with the most recent global tidal flat map (30-m resolution). The results can be used in mutual comparison for tidal flats, constraining mangroves, etc. tidal flat map; SAR; mangroves; mangroves of China Chuanpeng Zhao Chengzhi Qin PUBLIC https://creativecommons.org/licenses/by/4.0/ dataset http://www.doi.org/10.1016/j.isprsjprs.2019.11.022 Science Data Bank