Detailed Hierarchical Classification of Coastal Wetlands Using Multi-Source Time-Series Remote Sensing Data Based on Google Earth Engine

Abstract

Highlights: What are the main findings? A strategy integrating the strengths of pixel- and object-based classification was developed, achieving high classification performance and fine differentiation of coastal wetlands. A set of composite feature variables was constructed and optimized that effectively captures the differential dynamic characteristics of coastal wetland types. What are the implication of the main finding? The proposed classification strategy effectively addresses key challenges in coastal wetland mapping, establishing a reliable framework for large-scale, high-precision applications. Fine-grained wetland classification characterizes ecological functional distinctions among wetland types, facilitating targeted management of coastal wetlands. Accurate and detailed mapping of coastal wetlands is essential for effective wetland resource management. However, due to periodic tidal inundation, frequent cloud cover, and spectral similarity of land cover types, reliable coastal wetland classification methods remain limited. To address these issues, we developed an integrated pixel- and object-based hierarchical classification strategy based on multi-source remote sensing data to achieve fine-grained coastal wetland classification on Google Earth Engine. With the random forest classifier, pixel-level classification was performed to classify rough wetland and non-wetland types, followed by object-based classification to differentiate artificial and natural attributes of water bodies. In this process, multi-dimensional features including water level, phenology, variation, topography, geography, and geometry were extracted from Sentinel-1/2 time-series images, topographic data and shoreline data, which can fully capture the variability and dynamics of coastal wetlands. Feature combinations were then optimized through Recursive Feature Elimination and Jeffries–Matusita analysis to ensure the model’s ability to distinguish complex wetland types while improving efficiency. The classification strategy was applied to typical coastal wetlands in central Jiangsu in 2020 and finally generated a 10 m wetland map including 7 wetland types and 3 non-wetland types, with an overall accuracy of 92.50% and a Kappa coefficient of 0.915. Comparative analysis with existing datasets confirmed the reliability of this strategy, particularly in extracting intertidal mudflats, salt marshes, and artificial wetlands. This study can provide a robust framework for fine-grained wetland mapping and support the inventory and conservation of coastal wetland resources.