Detailed vegetation maps are needed for wetland conservation and restoration as different vegetation communities have distinct water requirements. It is a continuous challenge to map the distribution of different wetland types on a regional scale, and a trade-off between the categorical details and availability of resources to ensure broad applications is often necessary for operational mapping. Here, we evaluated the capacity and performance of statistical learning in discriminating wetland types using Landsat time series and geomorphological variables computed from Light Detection and Ranging (LiDAR) and Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM). Our study showed that there was a discrimination limit of statistical learning in wetland mapping. The approach was clearly inadequate in distinguishing certain wetland types. In semiarid Australia, our results suggested that the appropriate level for floodplain wetland mapping included four classes: tree-dominated woodlands, shrublands, vegetated swamps, and non-flood-dependent terrestrial communities. Our results also demonstrated that the geomorphological metrics significantly improved the accuracy of wetland classification. Furthermore, geomorphological metrics derived from the freely available coarser resolution SRTM DEM were as beneficial for wetland mapping as those extracted from finer scale commercially-based LiDAR DEM. The finding enables the widespread applications of our approach, as both data sources are freely available globally.
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- statistical learning
- geomorphological metrics
- predictive power
- floodplain wetland types