Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region

Tulbure, Mirela G.; Broich, Mark; Stehman, Stephen V.; Kommareddy, Anil

doi:10.1016/j.rse.2016.02.034

Cited by 235 publications

(181 citation statements)

References 68 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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“…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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“…Remote-sensing data have been widely used for mapping the lake-water extent over time and space [10][11][12][13][14][15][16][17], with Landsat image data being one of the most common data types for monitoring and analyzing long-term lake-water extent changes, due to their high spatial resolution (30-60 m) and long data record [11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…One approach is the use of water indices, such as the Modified Normalized Difference Water Index (MNDWI) [18], the Normalized Difference Water Index (NDWI) [19], and the Automated Water Extraction Index (AWEI) [20]. However, an ideal single threshold for water indices, in order to distinguish between water bodies and non-water bodies, is difficult to determine because the spectral signature of water varies in space and time [13]. Automatic threshold methods have been adopted for determining the optimal value, like Otsu [21,22].…”

Section: Introductionmentioning

confidence: 99%

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Spatio-Temporal Change of Lake Water Extent in Wuhan Urban Agglomeration Based on Landsat Images from 1987 to 2015

et al. 2017

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Abstract:Urban lakes play an important role in urban development and environmental protection for the Wuhan urban agglomeration. Under the impacts of urbanization and climate change, understanding urban lake-water extent dynamics is significant. However, few studies on the lake-water extent changes for the Wuhan urban agglomeration exist. This research employed 1375 seasonally continuous Landsat TM/ETM+/OLI data scenes to evaluate the lake-water extent changes from 1987 to 2015. The random forest model was used to extract water bodies based on eleven feature variables, including six remote-sensing spectral bands and five spectral indices. An accuracy assessment yielded a mean classification accuracy of 93.11%, with a standard deviation of 2.26%. The calculated results revealed the following: (1) The average maximum lake-water area of the Wuhan urban agglomeration was 2262.17 km 2 from 1987 to 2002, and it decreased to 2020.78 km 2 from 2005 to 2015, with a loss of 241.39 km 2 (10.67%). (2) The lake-water areas of loss of Wuhan, Huanggang, Xianning, and Xiaogan cities, were 114.83 km 2 , 44.40 km 2 , 45.39 km 2 , and 31.18 km 2 , respectively, with percentages of loss of 14.30%, 11.83%, 13.16%, and 23.05%, respectively. (3) The lake-water areas in the Wuhan urban agglomeration were 226.29 km 2 , 322.71 km 2 , 460.35 km 2 , 400.79 km 2 , 535.51 km 2 , and 635.42 km 2 under water inundation frequencies of 5%-10%, 10%-20%, 20%-40%, 40%-60%, 60%-80%, and 80%-100%, respectively. The Wuhan urban agglomeration was approved as the pilot area for national comprehensive reform, for promoting resource-saving and environmentally friendly developments. This study could be used as guidance for lake protection and water resource management.

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Surface water extent dynamics from three decades of seasonally continuous Landsat time series at subcontinental scale in a semi-arid region

Cited by 235 publications

References 68 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)

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

Spatio-Temporal Change of Lake Water Extent in Wuhan Urban Agglomeration Based on Landsat Images from 1987 to 2015

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