Water observations from space: Mapping surface water from 25  years of Landsat imagery across Australia

Mueller, Norman; Lewis, Adam; Roberts, Dale; Ring, Shannon Jeanne; Melrose, Rachel; Sixsmith, Joshua; Lymburner, Leo; McIntyre, A.; Tan, Peter J.; Curnow, Steve; Ip, Alex

doi:10.1016/j.rse.2015.11.003

Cited by 408 publications

(276 citation statements)

References 23 publications

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“…The concept has been proven and validated by Geoscience Australia together with CSIRO and the National Computing Infrastructure of Australia (NCI) who implemented the Australian Geoscience Data Cube, a national/continental scale DC of thousands of terabytes of EO data (Landsat, MODIS, Sentinel-2) making it quicker and easier to provide information on environmental issues that can affect all Australians Lewis et al, 2016;Purss et al, 2015). It has allowed mapping the extent of surface water across the entire Australian continent using 27 years of Landsat imagery (Mueller et al, 2016), gaining knowledge on flood dynamics over Australia (Tulbure, Broich, Stehman, & Kommareddy, 2016), or extracting the intertidal extent and topography of the Australian coastline (Sagar, Roberts, Bala, & Lymburner, 2017).…”

Section: Background: Setting the Scene For The Swiss Data Cubementioning

confidence: 99%

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

et al. 2017

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Section: Background: Setting the Scene For The Swiss Data Cubementioning

confidence: 99%

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

et al. 2017

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“…It tries to identify valley bottoms using a slope classification constrained to convergent areas, which is more likely to be occupied by water than simply low places. It has been used as an important data layer for mapping water bodies from remote sensing imagery in many studies [24][25][26]. A representative index in the drainage based group is the Height Above Nearest Drainage (HAND) index, which was presented by Rennó et al [27] and implemented by Nobre et al [28].…”

Section: Study Sitesmentioning

confidence: 99%

A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data

Huang

Nguyen

Zhang

et al. 2017

IJGI

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Abstract:The Sentinel-1 mission provides frequent coverage of global land areas and is hence able to monitor surface water dynamics at a fine spatial resolution better than any other Synthetic Aperture Radar (SAR) mission before. However, SAR data acquired by Sentinel-1 also suffer from terrain effects when being used for mapping surface water, just as other SAR data do. Terrain indices derived from Digital Elevation Models (DEMs) are easy but effective approaches to reduce this kind of interference, considering the close relationship between surface water movement and topography. This study compares two popular terrain indices, namely the Multi-resolution Valley Bottom Flatness (MrVBF) and the Height Above Nearest Drainage (HAND), toward their performance on assisting surface water mapping using Sentinel-1 SAR data. Four study sites with different terrain characteristics were selected to cover a very wide range of topographic conditions. For two of these sites that are floodplain dominated, both normal and flooded scenarios were examined. MrVBF and HAND values for the whole study areas, as well as statistics of these values within water areas were compared. The sensitivity of applying different thresholds for MrVBF and HAND to mask out terrain effect was investigated by adopting quantity disagreement and allocation disagreement as the accuracy indicators. It was found that both indices help improve water mapping, reducing the total disagreement by as much as 1.6%. The HAND index performs slightly better in most of the study cases, with less sensitivity to thresholding. MrVBF classifies low-lying areas with more details, which sometimes makes it more effective in eliminating false water bodies in rugged terrain. It is therefore recommended to use HAND for large scale or global scale water mapping. However, for water detection in complex terrain areas, MrVBF also performs very well.

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“…There is great potential for tracking long-term surface water dynamics given Landsat's <16-day revisit period from 1983 to the present (e.g., Feng et al, 2016;Mueller et al, 2016;Tulbure et al, 2016). The presence and duration of flooding can be informative in lieu of more intensive field measurements (Elphick, 2008;Farmer and Parent, 1997;Reiter et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds

Schaffer‐Smith

Swenson

Barbaree

et al. 2017

Remote Sensing of Environment

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Satellite measurements of surface water offer promise for understanding wetland habitat availability at broad spatial and temporal scales; reliable habitat is crucial for the persistence of migratory shorebirds that depend on wetland networks. We analyzed water extent dynamics within wetland habitats at a globally important shorebird stopover site for a 1983-2015 Landsat time series, and evaluated the effect of climate on water extent. A range of methods can detect open water from imagery, including supervised classification approaches and thresholds for spectral bands and indices. Thresholds provide a time advantage; however, there is no universally superior index, nor single best threshold for all instances. We used random forest to model the presence or absence of water from >6200 reference pixels, and derived an optimal water probability threshold for our study area using receiver operating characteristic curves. An optimized mid-infrared (1.5-1.7 μm) threshold identified open water in the Sacramento Valley of California at 30-m resolution with an average of 90% producer's accuracy, comparable to approaches that require more intensive user input. SLC-off Landsat 7 imagery was integrated by applying a customized interpolation that mapped water in missing data gaps with 99% user's accuracy. On average we detected open water on ~26000 ha (~3% of the study area) in early April at the peak of shorebird migration, while water extent increased five-fold after the migration rush. Over the last three decades, late March water extent declined by ~1300 ha per year, primarily due to changes in the extent and timing of agricultural flood-irrigation. Water within shorebird habitats was significantly associated with an index of water availability at the peak of migration. Our approach can be used to optimize thresholds for time series analysis and near-real-time mapping in other regions, and requires only marginally more time than generating a confusion matrix.

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Water observations from space: Mapping surface water from 25 years of Landsat imagery across Australia

Cited by 408 publications

References 23 publications

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

Building an Earth Observations Data Cube: lessons learned from the Swiss Data Cube (SDC) on generating Analysis Ready Data (ARD)

A Comparison of Terrain Indices toward Their Ability in Assisting Surface Water Mapping from Sentinel-1 Data

Three decades of Landsat-derived spring surface water dynamics in an agricultural wetland mosaic; Implications for migratory shorebirds

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