A Novel Workflow for Seasonal Wetland Identification Using Bi-Weekly Multiple Remote Sensing Data

Abstract

Accurate wetland mapping is essential for their protection and management; however, it is difficult to accurately identify seasonal wetlands because of irregular rainfall and the potential lack of water inundation. In this study, we propose a novel method to generate reliable seasonal wetland maps with a spatial resolution of 20 m using a seasonal-rule-based method in the Zhalong and Momoge National Nature Reserves. This study used Sentinel-1 and Sentinel-2 data, along with a bi-weekly composition method to generate a 15-day image time series. The random forest algorithm was used to classify the images into vegetation, waterbodies, bare land, and wet bare land during each time period. Several rules were incorporated based on the intra-annual changes in the seasonal wetlands and annual wetland maps of the study regions were generated. Validation processes showed that the overall accuracy and kappa coefficient were above 89.8% and 0.87, respectively. The seasonal-rule-based method was able to identify seasonal marshes, flooded wetlands, and artificial wetlands (e.g., paddy fields). Zonal analysis indicated that seasonal wetland types, including flooded wetlands and seasonal marshes, accounted for over 50% of the total wetland area in both Zhalong and Momoge National Nature Reserves; and permanent wetlands, including permanent water and permanent marsh, only accounted for 11% and 12% in the two reserves, respectively. This study proposes a new method to generate reliable annual wetland maps that include seasonal wetlands, providing an accurate dataset for interannual change analyses and wetland protection decision-making.