Mapping and Monitoring the Multi-Decadal Dynamics of Australia’s Open Waterbodies Using Landsat

Abstract

Water detection algorithms are now being routinely applied to continental and global archives of satellite imagery. However, water resource management decisions typically take place at the waterbody rather than pixel scale. Here, we present a workflow for generating polygons of persistent waterbodies from Landsat observations, enabling improved monitoring and management of water assets across Australia. We use Digital Earth Australia’s (DEA) Water Observations from Space (WOfS) product, which provides a water classified output for every available Landsat scene, to determine the spatial locations and extents of waterbodies across Australia. We generated a polygon set of waterbodies that identified 295,906 waterbodies ranging in size from 3125 m² to 4820 km². Each polygon was used to generate a time series of WOfS, providing a history of the change in surface area of each waterbody every ~16 days since 1987. We demonstrate the applications of this new dataset, DEA Waterbodies, to understanding local through to national-scale surface water spatio-temporal dynamics. DEA Waterbodies provides new insights into Australia’s water availability and enables the monitoring of important landscape features such as lakes and dams, improving our ability to use earth observation data to make meaningful decisions.

1. Introduction

Water availability and security is a key consideration for humans and natural ecosystems world-wide. Competing demands for limited water resources between human consumptive use and ecosystems is already placing many natural systems under stress [1,2,3,4,5]. Water resource conflicts between neighbouring nations are already occurring, and these conflicts are likely to worsen as climate change increases the uncertainty in rainfall and evapotranspiration [6,7,8].

In Australia, the world’s driest inhabited continent, water availability is a key social, environmental and economic issue affecting the lives of millions of Australians. Australia has an extensive surface water monitoring network in place, consisting of stream gauges and publicly owned water storage monitoring. While these surface monitoring networks consist of tens of thousands of instruments, Australia’s large land area and ephemeral surface water mean that large parts of its surface water network remain unmetered.

Multi-decadal archives of satellite imagery provide insight into water resource availability in two key ways. Firstly, they have the ability to map and monitor catchment-wide and continent-wide surface water dynamics, supplementing or replacing surface networks in data-sparse regions [9,10,11]. Secondly, the multi-decadal perspective of these archives means that the changes in surface water that are currently taking place can be placed into the context of historical surface water dynamics [12,13]. Satellite data from public-good satellites such as the Landsat series are freely available and global in coverage [14,15,16], facilitating global-scale analysis of inland surface water [12,13,17,18]. Australia-specific surface water studies have also been carried out, which detect the frequency of water across the landscape [19,20], as well as map the locations of seasonal and permanent waterbodies [21,22].

Whilst information about the presence/absence of water provides valuable insight into multi-decadal surface water dynamics, further analysis is required to make the information content more accessible to water managers. This is because water management decisions are typically based on waterbodies (dams, lakes, river reaches, refugial pools) rather than pixels. Waterbody mapping with satellite imagery has almost exclusively been done on a pixel-by-pixel [12,13,18,20,21,23,24] (or sub-pixel [25,26,27,28] basis), with only a few studies delineating waterbodies as vector objects [29,30,31,32]. The delineation of waterbodies provides an object-based analysis, which characterises the dynamics of the whole waterbody, not its individual pixels. This allows analysis of waterbody characteristics such as the change in surface area over time [24,33], facilitating study of the events observed to be impacting individual waterbodies [33]. This in turn allows environmental water managers to evaluate the efficacy of environmental flow events in providing aquatic ecosystem provision objectives [34].

Analysing surface water dynamics with respect to individual waterbodies depends on the availability of a waterbody polygon set. In previous studies waterbody polygons have been generated from pre-existing cartographic coverages [35], or from a select number of cloud-free images [31,36]. The key limitation of these approaches is that the polygon sets are dependent on line work derived from either a single point in time [35] or reference imagery from a narrow range of dates [31,36]. This limits the ability of these approaches to identify ephemeral or recently formed waterbodies.

The combination of ‘analysis ready data’ [37] with large scale Earth observation analytics platforms such as Google Earth Engine [38] and Open Data Cube [39] now make it possible to analyse all available water observations to derive a waterbody polygon set.

The aim of this paper is to describe the use of Digital Earth Australia (the Australian instance of Open Data Cube) [39] to identify inland waterbodies across Australia and to characterise the change in their surface area from 1987–2018 for the purposes of providing river regulators, environmental water managers, agricultural water users and catchment managers with a common, transparent, and shared understanding of Australia’s surface water resources. The specific objectives are to:

• Use Water Observations from Space (WOfS) [19] inundation frequency from 1987–2018 to generate waterbody polygons delineating each waterbody’s maximum surface extent over this period.
• Quantify the time series of water surface area for each polygon for all available Landsat observations (1987–present).
• Demonstrate how these polygon-specific time series can be aggregated to provide insight into water availability.

The DEA Waterbodies interface is publicly available via DEA Maps (https://maps.dea.ga.gov.au/, accessed on 31 March 2021), ensuring that routine, robust, and repeatable information about Australia’s waterbodies remains current and openly available.

2. Materials and Methods

2.1. Study Area

Australia is the driest inhabited continent on Earth. Australia’s climate spans Koppen climate zones from tropical rainforest to hot desert and temperate climates (Figure 1). Australia experiences highly variable rainfall patterns driven by multiple large-scale climate drivers [40] as well as shorter-lived weather events [41,42]. The amount of surface water across the Australian continent at any point in time is hard to determine, however some estimates exist from remotely sensed data [19,43] and surface hydrology networks [44].

2.2. Satellite Imagery

DEA is a data platform created within Geoscience Australia that generates analysis-ready satellite data that can be used to monitor changes in Australia’s land surface over time [39,46]. DEA provides free and open earth observation data from two public-good satellite programs: Landsat [47] and Sentinel 2 [48]. The Landsat program, operated by the United States Geological Survey and National Aeronautics and Space Administration (NASA), consists of three multi-spectral satellites/sensors: Landsat 5 TM (1984–2013), Landsat 7 ETM+ (1999–present), and Landsat 8 OLI (2013–present). These sensors have a 30 m pixel resolution, and image the Earth every 16 days on average. Landsat 5 TM was operational from 1984, however routinely collected data from 1987 to 2011, before being decommissioned in 2013 [49]. The Landsat 7 ETM+ and Landsat 8 OLI missions are still operational, however a failure of the scan-line corrector in Landsat 7 ETM+ in May 2003 means that imagery acquired by the satellite after this date has stripes of missing data, as no gap-fill algorithm is applied to this data. These missing data have some effect on the overall quality of the data from this satellite, however the data present can still provide valuable insights into surface changes across Australia [50], particularly when combined with data from Landsat 8 OLI.

The raw satellite data from the Landsat missions have been corrected, standardised, and orthorectified to produce an archive of analysis-ready data at 25 m pixel resolution [39,46]. This data can be accessed using the Open Data Cube API [51] via DEA, allowing automatic extraction and processing of the Earth observation archives.

2.3. Water Observations from Space (WOfS)

Water Observations from Space (WOfS) is a water detection algorithm that classifies each cloud-masked, observed pixel into wet, dry or invalid (i.e., cloud obscured, high slope) for all available observations through time [19]. WOfS uses a decision tree based on a series of Landsat band ratios and ancilliary datasets such as valley bottom flatness and pixel quality. This additional information provides an extra degree of validation for the decision tree classifications, over more basic band ratio water classifiers like the normalised difference water index (NDWI).

The WOfS classifier has a high overall degree of accuracy (97%) in correctly classifying the presence of water, however this accuracy decreases where there are water and other targets, particularly vegetation, occurring within the same pixel. WOfS is most effective where there is a clear, unobstructed water surface that has been sampled by the satellites, and is known to misclassify areas where water and vegetation co-exist as dry, such as wetlands and riparian zones.

WOfS is run on the Landsat archive by DEA, providing a 32-year history of water classified pixels for all of Australia. We make use of this archive to identify where pixels have been frequently mapped as wet using the WOfS classifier to generate a map of surface waterbodies across Australia.

3. Workflow

Our methodology takes advantage of WOfS data within DEA to locate and extract information about persistent waterbodies across Australia. The workflow is written in Python, and has been almost completely automated to minimise subjective decision making and ensure reproducibility, allowing the workflow to be run repeatedly with the same outcome. The code used to produce DEA Waterbodies is open source, and provided for use under an Apache Licence, Version 2.0 (see Appendix A).

The workflow is built off the WOfS Statistics dataset [52]. The WOfS Statistics dataset provides a pixel-by-pixel count of clear observations (i.e., not impacted by cloud, cloud shadow, or other satellite acquisition issues), wet observation counts (total number of times a pixel was observed as wet over the archive period), and a frequency statistic that provides the number of wet observations as a percentage of the number of clear observations over the period 1987–2018. The frequency statistic provides an indicator of how often water is observed in each pixel over this time period, and it is this product that forms the basis of this workflow.

In order to turn the WOfS frequency statistic into automatically generated polygons of waterbody locations, a series of steps need to be applied to remove erroneous data, and to map only the areas of water that meet specified thresholds. The decision tree used to produce DEA Waterbodies is shown in Figure 2. A detailed description of the methodology is included in Appendix A.

The workflow for DEA Waterbodies consists of ten steps:

• Check each pixel has been observed ≥128 times over the period 1987–2018 (equates to at least four observations per year of analysis) to exclude pixels that are very infrequently observed.
• Check each pixel meets a wet frequency threshold, to define waterbodies only where water persists in the landscape. We used two thresholds to generate two wet layers: 5% and 10%. These two thresholds were used in combination to ensure that infrequently inundated portions of larger waterbodies were included in the final polygons (see Appendix A).
• Turn wet pixels into polygons. This facilitates an object-based, rather than a pixel-based analysis.
• Merge polygons artificially cut by tile boundaries. Tile boundaries are an artefact of the way the data are stored and adjoining waterbodies need to be connected.
• Filter polygons by minimum size. We applied a five Landsat pixel (25 by 25 m) minimum size limit (3125 m²).
• Remove polygons containing ocean. We applied a custom ocean mask to remove ocean and ocean-connected waterbodies from the final dataset.
• Remove polygons within city centres. The WOfS classifer can mistake deep shadows from high-rise buildings for water, so we remove any waterbodies identified within high-rise city regions.
• Find polygons within the 5% wet frequency threshold layer that intersect with the 10% wet frequency threshold layer. The locations of the waterbody polygons are defined by the 10% wet frequency threshold, but the extent of the polygon is defined by the 5% wet frequency threshold.
• Use the Polsby–Popper test [53] to identify and manually split very large polygons. The Polsby–Popper statistic was used as an objective means of identifying very long and/or complicated polygons for manual curation.
• Generate a geohash identifier for each polygon. A geohash was used to generate a unique identifier for each mapped waterbody polygon.

The resulting waterbody polygon dataset represents the maximum extent of waterbodies across the Australian landscape from 1987–2018. The wet frequency thresholds have been chosen to include locations that are infrequently, but repeatedly wet, while excluding short-lived and infrequent events like flash flooding (see Appendix A).

4. Time Series Extraction for Each Polygon

Each individual waterbody polygon was interrogated to return a time series of wet surface area through time. The WOfS classifier can be run on individual Landsat scenes to get a snapshot of water/not water for every satellite acquisition since 1987. We used the waterbodies dataset to perform polygon drills, whereby the individual waterbody polygons were used to ‘drill’ through the stack of water classified Landsat imagery through time, to produce a 32-year history of water surface area (Figure 3). Only scenes where at least 80% of the total waterbody was clearly observed were included to minimise erroneous results caused by cloud, Landsat 7 scan-line off missing values or swath boundaries. Each waterbody time series provides insights into the percentage of each total polygon area that is observed as wet for each satellite observation. Given that the waterbody polygons represent a maximum area for each waterbody, it is common for only part of the whole polygon to be observed as wet. Depending on the shape and nature of each polygon, some polygons will regularly be observed as 100% wet area or 100% dry area (e.g., constructed on-farm storages), while some polygons will never be completely dry (e.g., most large water reservoirs) or completely wet (e.g., large inland lakes like Kati Thanda–Lake Eyre).

The time series associated with each waterbody is stored in a separate CSV file, named according to the polygon geohash. Each CSV file is updated as new WOfS data become available, providing an operational picture of the changing wet surface area of each waterbody over time.

Due to the 2D nature of satellite imagery, this information is only available for surface area, and not for waterbody volume. For steep-sided waterbodies (for example, constructed water storages), a negligible change in surface area may equate to a large change in total waterbody volume. Despite this, the surface area change in water extent provides powerful insights into the timing of filling and emptying events.

5. Results

5.1. Validation

5.1.1. Polygon Validation

We compared polygons from DEA Waterbodies to those from the Bureau of Meteorology Australian Hydrological Geospatial Fabric (Geofabric). The Geofabric is Australia’s most comprehensive hydrological cartography product: a digital database of surface and groundwater hydrological features, including the spatial relationships between them [54]. It is described as a ‘digital street directory of Australia’s important water features’ ([55], p. 2). Waterbody polygons contained within the Geofabric are taken from the Surface Hydrology Database [56], which is composed of datasets from state government agencies from satellite imagery, aerial imagery, fieldwork, digital elevation models and other data sources. The Geofabric represents the best available, nationally recognised and extensively quality controlled dataset of Australian waterbodies, and as such, is the most suitable dataset for validation of DEA Waterbodies polygons.

The overall distribution of waterbody size is very similar between DEA Waterbodies and the Geofabric. DEA Waterbodies misses waterbodies smaller than 5 Landsat pixels (3125 m²) which we explicitly filtered out (Figure 4). However, DEA Waterbodies maps more small waterbodies above this threshold. There are likely two reasons for this: first, complex waterbody boundaries may result in many small discrete waterbodies, rather than a single object; second, DEA Waterbodies identifies additional new waterbodies not mapped in the Geofabric. Other discrepancies may come from the different methods by which the products were created: waterbodies mapped in DEA Waterbodies must have been observed to be wet in at least 10% of observations since 1987, as opposed to Geofabric waterbodies, which are visually interpreted and may be no longer active or may not have existed at the time of production.

We also performed a closer comparison on two regions, one near Moree NSW with well-defined but dynamic farm dams, and one area dominated by salt lakes near Cranbrook WA. The comparison between DEA Waterbodies and Geofabric polygons near Moree is shown in Figure 5 and the comparison with Cranbrook salt lakes is shown in Figure A7.

The Moree comparison shows that almost all Geofabric waterbodies are also included in DEA Waterbodies, with only minor differences along the edge of the polygon. These minor differences are due to the pixel grid of Landsat compared to the smooth and manually-curated Geofabric polygons. The Geofabric-only polygons in the northeast are rarely active and do not fulfil the >10% wet pixel threshold to be included in DEA Waterbodies. Additionally, DEA Waterbodies includes two large reservoirs that are not present in the Geofabric. The larger reservoir is often dry for entire years, and the smaller reservoir was wet only from 2016 to 2018, explaining why they were missed in the Geofabric. There is also a large creek in the northwest which the Geofabric does not include, as it manages streams separately to waterbodies. The Cranbrook comparison shows little difference in which polygons are identified, but some polygons are smaller in DEA Waterbodies than in the Geofabric (Figure A7). This is due to salt lakes being misidentified as cloud in the underlying WOfS dataset.

To perform a statistical comparison between DEA Waterbodies and the Geofabric, we associated the polygons in DEA Waterbodies and Geofabric by searching for their intersections, and assumed that intersecting polygons represented the same waterbody. If for a given polygon in one dataset there were multiple intersecting polygons in the other dataset, we assumed that all intersecting polygons were part of the same physical waterbody. We compared the areas covered by the waterbody in each dataset. The distribution of the differences in area for each waterbody is shown in Figure 6. While most waterbodies are very close in area between the two datasets (with a median deviation of 0.73% for Moree and –8.11% for Cranbrook) DEA Waterbodies and the Geofabric are much more consistent on constructed farm storages, compared to salt lakes, which tend to be smaller in DEA Waterbodies. This highlights the difficulties in defining a boundary for natural waterbodies, with DEA Waterbodies defining the polygon limit based on observed wet history, compared to Geofabric, which is manually curated.

We tested the asymmetric difference between all polygons in the two test areas, and computed the area of the result. This describes how much of each waterbody is contained in the Geofabric but not in DEA Waterbodies and vice versa. The distributions of these areas are shown in Figure A8. For the Cranbrook salt lakes, the difference in area is primarily due to the difference in the polygon boundary as defined in the Geofabric that is not included in DEA Waterbodies, largely due to the edges of salt lakes being masked as cloud. The Moree test site is much more symmetric, with similar rates of omission and commission between the datasets.

5.1.2. Surface Area Time Series Validation

Validation of the DEA Waterbodies time series (a wet surface area time series) is made difficult by the lack of a comparable dataset to validate against. We chose to compare a subset of DEA Waterbodies time series against gauged reservoir volume time series available through the Bureau of Meteorology’s Water Data Online (WDO) service. WDO provides volume and depth measurements for gauges in approximately 360 reservoirs, dams, and weirs around Australia. We selected a subset of 139 gauges for validation, based on the length of the gauge record, the availability and quality of depth and volume measurements, and the waterbody type (See Appendix B). We compared each volume time series to the surface area time series from DEA Waterbodies using the Spearman ρ coefficient. The Spearman coefficient measures the correlation of two datasets which have a positive monotonic relationship, regardless of whether that relationship is linear or otherwise. It can be assumed that the relationship between waterbody surface area and volume is positive monotonic, making this statistic suitable for comparison of two different measurements (surface area vs. volume), without the need to convert between them.

The locations and ρ values of the 139 gauges are shown in Figure 7. Most waterbodies show good correlation with the gauge data, with ρ coefficients > 0.8. Waterbodies in Tasmania show an overall poor relationship between DEA Waterbodies and WDO gauges. The highland lakes in Tasmania have high tannin content, making them look dark, resulting in very low levels of water leaving radiance. When combined with low solar angles this results in near-zero surface reflectance values across both the visible and near/shortwave infrared wavelengths. The normalised difference indices within the WOfS classification algorithm are extremely sensitive to noise, resulting in areas of water being misclassified as land. This causes the waterbody’s wet surface area observations to fluctuate as misclassified land pixels are counted amongst the correctly classified water pixels Figure 7d).

To help visualise the comparison between WDO and DEA Waterbodies, we estimated the surface area time series for each WDO volume time series using the volume and depth gauge data. Figure 7c and d show the estimated WDO surface area time series plotted alongside the DEA Waterbodies surface area time series for Lake Burrendong (New South Wales) and Lake St. Clair (Tasmania) respectively. The Burrendong time series (Figure 7c) shows very close agreement between the surface area time series modelled from the reservoir gauge and the surface area time series retrieved from Landsat. This is reflected in its high Spearman ρ of 0.99. The time series diverge when the dam is close to completely full. This may be due to steep slopes at the edges of the reservoir, or trees obscuring the edges of reservoir blocking the water signal and generating mixed signal pixels. The Landsat time series does not capture the 2012 filling event as this occurred in the gap between Landsat 5 and Landsat 8.

The Lake St. Clair time series (Figure 7d) shows poor agreement between the surface area time series modelled from the reservoir gauge and the surface area time series retrieved from Landsat. This is reflected in its relatively low Spearman ρ of 0.33, and is a result of a misclassification of water as land by WOfS. Based on the results of our analysis, DEA waterbodies surface area time series for Tasmanian highland lakes should be treated with caution. Such a pattern mainly occurs in the highland lakes of Tasmania, where Lake St. Clair is located. This spatial pattern is apparent in Figure 7a. In future, the WOfS water classifier will be revised to address the inaccuracies that can occur over dark waterbodies at low solar angles.

5.2. Case Studies of DEA Waterbodies Insights

DEA Waterbodies provides an unprecedented level of insights into the spatial and temporal dynamics of surface water across the Australian continent. The combination of waterbody polygons with accompanying wet area observational time series provides an opportunity to explore surface water dynamics at an individual waterbody, through to national scale. The following section presents a series of case study results that demonstrate the power of this dataset, though these do not represent an exhaustive analysis of the insights available from DEA Waterbodies.

5.2.1. Per Waterbody

Individual waterbody time series provide insights into the change in wet surface area of each waterbody across Australia, providing detailed information about the behaviour of the waterbody over time and providing indirect insights into the nature of the waterbody itself. The time series produced for each polygon corresponds well with the observed percentage of the total surface area of each waterbody observed as wet at each time step (Figure 8). These time series are most useful for indicating the timing of a change in the surface wet area, since they do not provide insights into volume. The timing of filling and emptying events is an important metric for understanding how waterbodies change over time. These changes are not attributed in DEA Waterbodies, and could result from, rainfall events, watercourse flooding, human management of a waterbody (e.g., pumping into/out of a storage), groundwater processes etc.

In constructed waterbodies (e.g., Figure 8) the wet area time series provides an indicative measure of the date the waterbody was constructed. The date that each waterbody was first observed as having at least 50% of the total waterbody area classified as wet was used as a proxy for construction date. Waterbodies that are natural and therefore not built, or waterbodies constructed prior to 1987, can be expected to return a ‘build date’ that corresponds to the first time they filled since our records began in 1987. A moderate La Niña event occurred in 1988–1989, which could be expected to fill present waterbodies across Australia’s east coast. We therefore discard any ‘build dates’ before 1990 to remove the natural and existing waterbodies from this construction analysis, but recognise that early dates may reflect water availability, rather than waterbody existence.

Prior to the construction of the storage in Figure 8, the associated time series shows almost no open water signal. Examination of satellite imagery (not shown) indicates this waterbody commenced construction in April 2000, and first began to fill with water in November 2000, registering in the time series as nearly 100% ‘wet’ for the first time in December 2000. This method can lead to bias towards very wet years, particularly in cases where waterbodies were constructed, but not filled until a wet season a few years later.

5.2.2. Per Region

Individual waterbody time series can be combined at the catchment scale to provide insights into regional-scale catchment behaviours and trends. Water management is done at multiple scales, and catchment-scale water management in Australia regularly corresponds to government policies and decisions that have been implemented at this scale [57].

The Fitzroy Basin in Queensland is one of the state’s largest basins covering an area of approximately 142,600 km². The growth in the number of waterbodies mapped within the Fitzroy Basin reflects the development of agriculture and mining in this region (Figure 9). The recently constructed waterbodies in the northern part of the Fitzroy Basin are primarily tailings dams from mining operations [58]. Insights into the growth of waterbodies within a region provide an independent assessment of infrastructure growth, and can be used to verify the installation of approved works and potentially flag unapproved works as well. A recent paper by Malerba et al. [59] used DEA Waterbodies to explore the rate of large storage construction across Australia since 1987.

5.2.3. National/Continental

Analysis of all the waterbody time series together allows us to develop a picture of the changing nature of open surface water across Australia. The total area of waterbody polygons is 87,548.7 km², which is approximately 1% of the total land area of Australia. The seasonal average wet area within all mapped waterbodies across Australia suggests that there is never an occasion where all mapped waterbodies contain at least some water, and there is a large degree of spatial variability in the spatial and temporal patterns of waterbody wet events (Figure 10). Two similarly extensive wet events in 1989 and 2010 show large variations in the spatial patterns of wet waterbodies across Australia. The 1989 event shows wet areas dominating in the south of the continent, corresponding with a weak La Niña event, while the 2010/11 event that coincided with a large La Niña event shows wet waterbodies mainly in eastern Australia (Figure 10). Future work will explore the spatiotemporal relationships between DEA Waterbodies and large-scale climate drivers like El Nino Southern Oscillation, Indian Ocean Dipole, and the Southern Annular Mode.

6. Discussion

6.1. Accuracies and Limitations of DEA Waterbodies

Accuracies and limitations of DEA Waterbodies are largely inherited from WOfS, with DEA Waterbodies a reanalysis and mapping product built off the WOfS datasets. WOfS has a number of known limitations and these manifest as incorrectly mapped waterbodies within this analysis. WOfS uses the spectral signature of water to classify wet pixels and is known to be suboptimal in locations where water and vegetation are mixed. This includes locations such as rivers with vegetated riparian zones and vegetated wetlands (Figure 11). The effect of this can be seen by the discontinuity of narrower river features identified within this analysis (Figure 11a), and by an under-representation of water within vegetated wetlands, such as the Macquarie Marshes, NSW (Figure 11b).

Other known WOfS limitations have been addressed through the filtering processes used to produce the map of waterbodies. The size of mapped waterbodies is limited to at least five Landsat pixels, removing small, isolated ‘water’ pixels. The size filtering reduces the noise associated with the dataset by only focusing on larger groups of pixels. Misclassification of water in deep shadows in high density cities has been handled by removing any waterbody polygons identified within central business districts (CBDs). Intermittently misclassified features, which return valid results only a handful of times over the 32-year study period, are also filtered out by testing for the number of valid observations returned for each pixel.

Despite this, some errors remain in the final waterbodies dataset. Steep terrain shadows present a known difficulty for the WOfS classifier, due to the shadows produced. While WOfS has attempted to mitigate this issue, some misclassification remains. We have not specifically attempted to address these errors within this workflow, and as such, a negligible number of the identified waterbodies may in fact be artefacts caused by terrain shadow. A signal-to-noise ratio error over deeper water in the WOfS classifier has also not been addressed here, and may result in some pixels missing from the centre of deeper waterbodies, resulting in doughnut-shaped mapped polygons. Similarly, different water colours may interfere with the decision-tree classifier, resulting in very turbid or coloured waterbodies being omitted. For a full discussion of the limitations and accuracy of WOfS, see Mueller et al., 2016.

Not all waterbodies across Australia have been identified by this product, and there remain a number of known reasons why some waterbodies may have been missed:

• They might be too small: DEA Waterbodies only maps waterbodies larger than 3125 m² (five whole Landsat pixels).
• They might not have been wet enough: DEA Waterbodies only maps waterbodies that have been observed as wet at least 10% of the time between 1987 and 2018. If a waterbody fills very infrequently, it may not meet this threshold.
• The waterbodies might have too much vegetation surrounding them: The WOfS classifier does not work well where water is combined with vegetation. If there is vegetation obscuring the water (like a tree leaning across a river, or a wetland), the classifier will not see this as water and the waterbody may not be mapped.
• The waters in the waterbodies are not classified as water: Sediment rich water or other unusual water spectra [60], like those in algae-rich waterbodies can confuse the WOfS classifier, resulting in the pixels being incorrectly classified as not water.
• The waterbodies might be new: Waterbodies that have been constructed or modified after 2016 may not be captured within this tool as they will not have been observed as wet at least 10% of the time between 1987 and 2018. Future updates of this product should capture newer waterbodies.

The automated nature of DEA Waterbodies identification and mapping means that some artefacts exist in the waterbody outlines, which do not directly match the real-world waterbody shape. Waterbodies have been mapped by DEA Waterbodies using the resolution of Landsat, so the outlines of every waterbody are pixelated, and may not be an exact representation of the real-world shape of the waterbody. Since DEA Waterbodies uses an automatic workflow utilising Landsat data to objectively map waterbodies, we have chosen to leave the final waterbody line work pixelated to transparently identify which Landsat pixels have been mapped inside (and outside) of each waterbody (Figure 12). Future iterations of DEA Waterbodies could make use of sub-pixel methods [25,26,27,28] or higher resolution imagery to better capture the true waterbody shape.

The relatively coarse spatial resolution of Landsat and WOfS has resulted in the loss of some discontinuities like dam walls, causing multiple waterbodies to be mapped as a single polygon (Figure 12a). This is most common where separating features like dam walls or banks are relatively narrow, resulting in mixed water/land pixels being recorded over these features, and losing the clear distinction between two waterbodies within the Landsat data. Conversely, discontinuities in the water classifier caused by real world features such as small discrete water pools, or spatial variability in the frequency of inundation within a single waterbody, or limitations in the WOfS classifier as described above, have resulted in waterbodies being mapped as a large number of small polygons, rather than a single connected waterbody (Figure 12b), or the extent of the waterbody being incompletely captured by the polygon (Figure 12c).

The production of DEA Waterbodies using an objective, coded workflow means that as new data become available, the workflow can be easily re-run to update the analysis and improve the overall dataset quality. Future work that makes use of Sentinel 2 data will increase the spatial and temporal resolution of the analysis, and assist in developing a more accurate DEA Waterbodies map. Discontinuities caused by real world features could be minimised in future data releases by removing holes from ‘doughnut-shaped’ polygons, however this would require additional work to explore how much this process would help improve the quality of the DEA Waterbodies map, rather than introduce new sources of error.

Identified waterbodies are a combination of natural and man-made waterbodies, and contain features such as rivers, lakes, water supply dams, on farm storages, wetlands, oxbow lakes, and ponds. The DEA Waterbodies product does not identify the nature of the waterbody (natural vs. man-made) nor its purpose. We also do not attempt to label features that have a known name and instead use the geohash as a unique identifier. This means that well known features such as Kati Thanda–Lake Eyre are not labelled with their common name in this product, and are instead referred to just using their geohash (e.g., r4ctk0hzm). DEA Waterbodies could be spatially joined with the Geofabric [61] to attach names from previously mapped features, however differences in the extents of mapped features between these two products may make this task difficult.

6.2. Comparison of DEA Waterbodies against Other Datasets

National-scale water mapping has been done previously over all of Australia, however DEA Waterbodies represents the first, national-scale, satellite-derived polygon dataset of waterbodies across the continent. Pekel et al., 2016 and Pickens et al., 2020 both present global surface water maps, comparable to WOfS. Both of these products look at the frequency of water for each individual pixel, but do not provide vector-based analyses of surface water, making it more difficult to interrogate whole waterbodies. Pekel et al., 2016 and Pickens et al., 2020 provide the ability to generate a time series of water history for each individual pixel, adding some time context to the water maps. While this functionality is similar to DEA Waterbodies, our ability to track the surface area of water over time across a whole waterbody provides insights into the behaviour of the waterbody as a whole, rather than an arbitrary pixel within it.

The Blue Dot Water Observatory (https://www.blue-dot-observatory.com/; [62] accessed on 31 March 2021) provides insights into the change in surface area of water, much like DEA Waterbodies, however on a much more limited spatial and temporal scale. The Blue Dot Observatory allows users to interrogate changes in the surface area of water for 188 identified waterbodies across Australia (40,330 globally) using the archive of Sentinel 2 data. The period of common data coverage between DEA Waterbodies and The Blue Dot Observatory shows a high degree of similarity between the results derived from the two products. This acts as an additional validation of our methods, which source different waterbody extent polygons, use different water detection algorithms and use different satellites to produce the outputs. The use of Landsat to derive the waterbody extents and time series histories in DEA Waterbodies extends the length of the analysis back to 1987, compared to Blue Dot Water Observatory’s five-year record from Sentinel 2.

6.3. DEA Waterbodies Applied Case Study

The development of a satellite-based waterbody map for all of Australia means that regional and national-scale monitoring of the state of Australia’s surface waterbodies can be carried out routinely. DEA Waterbodies is being used in the New South Wales (NSW) Department of Primary Industries Monthly Seasonal Conditions State Seasonal Update [63]. The State Seasonal Update is a monthly report containing an overview of the current climate and drought state of NSW provided to government, industry, landholders and the public. Data from DEA Waterbodies are resampled to a parish scale (to de-identify the data) and mapped according to the average wet surface area within all storages in that parish. This update provides an indication of stock water availability, as well as the potential risk to agricultural water supply in the region. Insights gained from the State Seasonal Update are being used by government to help inform drought mitigation strategies and financial support [64].

7. Conclusions

DEA Waterbodies maps and monitors almost 300,000 waterbodies across Australia, providing updating insights into the change in the wet surface area within each waterbody over time. DEA Waterbodies also provides insights into the behaviour of individual waterbodies over time, and can be used to gain insights into drought, regional-scale development, water management practices, and water spatial variability. Future versions of this product will make use of the higher temporal and spatial resolution of Sentinel 2 satellites to improve both the quality of the waterbody polygons themselves, and the frequency with which they are observed. The provision of both the code used to develop DEA Waterbodies and the outputs datasets under open-source licenses will facilitate future work in this area, allowing others to build off the results presented here.

While DEA Waterbodies provides new insights into the change in surface water area within waterbodies over time, water managers need to understand changes in water volume to input into water balance accounts. Future work will integrate DEA Waterbodies with LiDAR data to derive surface area/volume curves for individual waterbodies, allowing an uncertainty-bounded estimate of volume changes to be provided alongside changes in wet surface area. This will make the product much more useful to water managers and the water industry, who need to monitor and understand the movement of water volumes within a catchment over time.

National-scale reporting on water availability is a critical metric for the United Nation Sustainable Development Goal (SDG) six: clean water and sanitation. DEA Waterbodies provides a mechanism for reporting against SDGs by providing snapshots in time of the amount and spatial locations of surface water across Australia. Development of the product to include volume estimates will also increase the relevance of DEA Waterbodies to this type of national-scale reporting.