A global inventory of lakes based on high-resolution satellite imagery

Verpoorter, Charles; Kutser, Tiit; Seekell, David A.; Tranvik, Lars J.

doi:10.1002/2014gl060641

Cited by 1,176 publications

(939 citation statements)

References 23 publications

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“…The NRSCC and GIW estimates differ by 25,977 km 2 (0.7% of global inland water area), despite changes from the GLS2000 epoch to the 2010-2011 span of the NRSCC record. In contrast, Verpoorter et al (2014) recently estimated 5 × 10 6 km 2 of inland water area globally. The NRSCC and our results suggest that the Verpoorter et al (2014) estimate is likely an overestimate -especially since their comparatively large estimate focused on lakes but excluded rivers.…”

Section: Comparison With National Land-cover Datasetsmentioning

confidence: 95%

A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

Feng

Sexton

Channan

et al. 2015

International Journal of Digital Earth

281

190

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The science and management of terrestrial ecosystems require accurate, high-resolution mapping of surface water. We produced a global, 30-m-resolution inland surface water dataset with an automated algorithm using Landsat-based surface reflectance estimates, multispectral water and vegetation indices, terrain metrics, and prior coarse-resolution water masks. The dataset identified 3,650,723 km 2 of inland water globally -nearly three quarters of which was located in North America (40.65%) and Asia (32.77%), followed by Europe (9.64%), Africa (8.47%), South America (6.91%), and Oceania (1.57%). Boreal forests contained the largest portion of terrestrial surface water (25.03% of the global total), followed by the nominal 'inland water' biome (16.36%), tundra (15.67%), and temperate broadleaf and mixed forests (13.91%). Agreement with respect to the Moderate-resolution Imaging Spectroradiometer water mask and Landsat-based national land-cover datasets was very high, with commission errors <4% and omission errors <14% relative to each. Most of these were accounted for in the seasonality of water cover, snow and ice, and clouds -effects which were compounded by differences in image acquisition date relative to reference datasets. The Global Land Cover Facility (GLCF) inland surface water dataset is available for open access at the GLCF website (http://www.landcover.org).

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Section: Comparison With National Land-cover Datasetsmentioning

confidence: 95%

A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

Feng

Sexton

Channan

et al. 2015

International Journal of Digital Earth

281

190

View full text Add to dashboard Cite

show abstract

“…Regarding lakes and reservoirs, tropical ( (Verpoorter et al, 2014). Ebullition typically accounted for 50 to more than 90 % of the flux from the water bodies, while contributions from ebullition appear lower from rivers, although this is currently debated (e.g.…”

Section: Other Inland Water Systems (Lakes Ponds Rivers Estuaries)mentioning

confidence: 99%

“…Several aspects will need consideration to reduce the remaining uncertainty in the freshwater fluxes, including the generation of flux measurement that is more representative in time and space and an update of global lake area databases (e.g. GLOWAB, Verpoorter et al, 2014).…”

Section: Other Inland Water Systems (Lakes Ponds Rivers Estuaries)mentioning

confidence: 99%

The global methane budget 2000–2012

Saunois

Bousquet

Poulter

et al. 2016

Earth Syst. Sci. Data

933

733

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Abstract. The global methane (CH 4 ) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH 4 over the past decade. Emissions and concentrations of CH 4 are continuing to increase, making CH 4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH 4 sources that overlap geographically, and from the destruction of CH 4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (∼ biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). . Top-down inversions consistently infer lower emissions in China (∼ 58 Tg CH 4 yr −1 , range 51-72, −14 %) and higher emissions in Africa (86 Tg CH 4 yr −1 , range 73-108, +19 %) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models.The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40 % on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions.

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“…In recent years, several global inundation analyses have been released that have moved from static, coarse scale (>1 km) products (e.g., Bontemps et al, 2011;Lehner and Döll, 2004;Verpoorter et al, 2014) to more dynamic offerings, such as the Global Inundation Extent from Multi-Satellites (Fluet-Chouinard et al, 2015) and the MODIS water mask (Carroll et al 2009). Studies of wetland dynamics at global scales have typically focused on relatively short time periods with coarse spatial resolution (e.g., Papa et al, 2010;Prigent et al, 2007;25-km grid cells), and therefore have been unable to capture smaller wetland features, or account for longer term cyclical hydrologic variability.…”

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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A global inventory of lakes based on high-resolution satellite imagery

Cited by 1,176 publications

References 23 publications

A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm

The global methane budget 2000–2012

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

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