AUTHOR=Zhang Xinyue , Wang Tiejun , Han Xingxing 

TITLE=Spatiotemporal monitoring in beidagang wetland using Landsat time-series images and Google Earth Engine during 2000–2022

JOURNAL=Frontiers in Remote Sensing

VOLUME=Volume 6 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/remote-sensing/articles/10.3389/frsen.2025.1569617

DOI=10.3389/frsen.2025.1569617

ISSN=2673-6187

ABSTRACT=Wetlands are composed of the interaction of water, soil and suitable vegetation, which has rich biological resources and strong ecological benefits. Due to increasing human disturbance and the effects of climate change, wetlands are being dramatically degraded and destroyed. However, the existing wetland products lack the ability to capture and update the dynamic changes in time and space, with less attention to the classification based on hydrological processes and vegetation types. Therefore, we developed a Decision Tree (DT)-based classification method, incorporating water frequency (WF) and vegetation frequency (VF) calibrated with field observations, to monitor wetland dynamics using Landsat-5/7/8/9 time-series images (2000–2022) and Google Earth Engine (GEE). Taking Beidagang Wetland as the study area, six classes were extracted with high overall accuracy (0.89) and Kappa coefficient (0.85) in 2022. Interannual dynamics during 2000–2022 revealed two distinct periods: terrestrial vegetation (TerV) dominance with permanent water (PW) below 10% (2000–2014), and PW exceeding 20% while temporary vegetation (TemV) decreased (2015–2022). Spatially, land cover types radiated outward from Tiane Lake, with northwestern regions primarily covered by TerV and southeastern regions by TemV and barren (B). Frequent type conversions occurred between adjacent classes, with the most significant changes in Guanqi Lake. Despite declining wetland water volumes due to rising temperatures and reduced precipitation, ecological compensation measures, including functional zoning, water replenishment, and phragmites restoration, have continuously improved the wetland environment. This study presents a promising method combining Landsat time-series images, DT and GEE for continuous land cover monitoring. Threshold optimization using local data and interpretability based on vegetation physiological characteristics demonstrate enhanced applicability for large-scale wetland classification. The generated annual maps represent the most current dataset for Beidagang Wetland, providing scientific support for wetland monitoring, protection and management.