Abstract

The Ramsar-listed wetlands of the Magela Creek floodplain, situated in the World Heritage Kakadu National Park, in northern Australia are recognised for their biodiversity and cultural values. The floodplain is also a downstream receiving environment for Ranger uranium mine, which is entering closure and rehabilitation phases. Vegetation on the floodplain is spatially and temporally variable which is related to the hydrology of the region, primarily the extent and level of inundation and available soil moisture. Time-series mapping of the floodplain vegetation will provide a contemporary baseline of annual vegetation dynamics to assist with determining whether change is natural or a result of the potential impacts of mine closure activities such as increased suspended sediment moving downstream. The research described here used geographic object-based image analysis (GEOBIA) to classify the upper Magela Creek floodplain vegetation from WorldView-2 imagery captured over four years (2010–2013) and ancillary data including a canopy height model. A step-wise rule set was used to implement a decision tree classification. The resulting maps showed the 12 major vegetation communities that exist on the Magela Creek floodplain and their distribution for May 2010, May 2011, June 2012 and June 2013 with overall accuracies of over 80% for each map. Most of the error appears to be associated with confusion between vegetation classes that are spectrally similar such as the classes dominated by grasses. Object-based change detection was then applied to the maps to analyse change between dates. Results indicate that change between dates was detected for large areas of the floodplain. Most of the change is associated with the amount of surface water present, indicating that although imagery was captured at the same time of year, the imagery represents different stages of the seasonal cycle of the floodplain.

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