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River Red Gum (Eucalyptus camaldulensis) is widely distributed throughout many water courses and floodplains within inland Australia. In recent years, accelerated decline of River Red Gum condition has been observed in many locations, and field observations of the degradation are consistent with the reduction of flooding. However, there are few publications that quantitatively investigate the relationships between River Red Gum condition and flooding history. We applied Classification and Regression Tree (CART) to model the minimum flooding requirement of River Red Gum forest/woodland in Yanga National Park, located on the Lower Murrumbidgee Floodplain, southeast Australia, using crown conditions derived from historical aerial photographs spanning more than 40 years. The model produced has a moderate reliability with an overall accuracy of 64Ð1% and a Kappa index of 0Ð543. The model brings in important insights about the relationship between River Red Gum community type, flood frequency and flood duration. Our results demonstrated that (1) CART analysis is a simple yet powerful technique with significant potential for application in river and environmental flow management; (2) River Red Gum communities on the Lower Murrumbidgee Floodplain require periodic inundation (3-5 years) for a duration of up to 64 days to be in moderate to good conditions; (3) Although the crown conditions of different community types displayed similar degradation trends, they have distinct flooding requirements; and (4) The River Red Gum community in Yanga National Park may be managed as hydrological units given limited environmental water allocations.

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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.