Measuring inundation over long timeframes is essential for understanding the responses of large floodplain wetlands on regulated rivers, such as the internationally Ramsar listed Macquarie Marshes (2000 km²) in central-eastern Australia. We used near-spring Landsat images (Multispectral Scanner (MSS) and Thematic Mapper (TM) imagery) over 28 years (1979–2006) and classified for inundation, integrating water and vegetation response using Iterative Self-Organizing Data Analysis (ISODATA) clustering. A spatially explicit inundation index showed that zones inundated with high frequency were mostly in the northern region. Change detection of inundation indices over three consecutive water management periods (period 1 (1979–1987), period 2 (1988–1996) and period 3 (1997–2006)) showed that zones inundated with high frequency across the Macquarie Marshes contracted, equating to the loss of three or more spring floods from each 9-year period, despite no corresponding change in annual catchment or local rainfall. Landsat represents the only effective available long-term information for analysing long-term changes in inundation patterns of floodplain wetlands.

Wetlands around the world provide crucial ecosystem services and are under increasing pressure from multiple sources including climate change, changing flow and flooding regimes, and encroaching human populations. The Landsat satellite imagery archive provides a unique observational record of how wetlands have responded to these impacts during the last three decades. Information stored within this archive has historically been difficult to access due to its petabyte-scale and the challenges in converting Earth observation data into biophysical measurements that can be interpreted by wetland ecologists and catchment managers. This paper introduces the Wetlands Insight Tool (WIT), a workflow that generates WIT plots that present a multidecadal view of the biophysical cover types contained within individual Australian wetlands. The WIT workflow summarises Earth observation data over 35 years at 30 m resolution within a user-defined wetland boundary to produce a time-series plot (WIT plot) of the percentage of the wetland covered by open water, areas of water mixed with vegetation (‘wet’), green vegetation, dry vegetation, and bare soil. We compare these WIT plots with documented changes that have occurred in floodplain shrublands, alpine peat wetlands, and lacustrine and palustrine wetlands, demonstrating the power of satellite observations to supplement ground-based data collection in a diverse range of wetland types. The use of WIT plots to observe and manage wetlands enables improved evidence-based decision making.

Monitoring inundation in flow-dependent floodplain wetlands is important for understanding the outcomes of environmental water deliveries that aim to inundate different floodplain wetland vegetation types. The most effective way to monitor inundation across large landscapes is with remote sensing. Spectral water indices are often used to detect water in the landscape, but there are challenges in using them to map inundation within the complex vegetated floodplain wetlands. The current method used for monitoring inundation in the large floodplain wetlands that are targets for environmental water delivery in the New South Wales portion of the Murray–Darling Basin (MDB) in eastern Australia considers the complex mixing of water with vegetation and soil, but it is a time-consuming process focused on individual wetlands. In this study, we developed the automated inundation monitoring (AIM) method to enable an efficient process to map inundation in floodplain wetlands with a focus on the lower Lachlan floodplain utilising 25 Sentinel-2 image dates spanning from 2019 to 2023. A local adaptive thresholding (ATH) approach of a suite of spectral indices combined with best available DEM and a cropping layer were integrated into the AIM method. The resulting AIM maps were validated against high-resolution drone images, and vertical and oblique aerial images. Although instances of omission and commission errors were identified in dense vegetation and narrow creek lines, the AIM method showcased high mapping accuracy with overall accuracy of 0.8 measured. The AIM method could be adapted to other MDB wetlands that would further support the inundation monitoring across the basin.