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  5. Selection of a network of large lakes and reservoirs suitable for global environmental change analysis using Earth Observation

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Article
English
2016

Selection of a network of large lakes and reservoirs suitable for global environmental change analysis using Earth Observation

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English
2016
International Journal of Remote Sensing
Vol 37 (13)
DOI: 10.1080/01431161.2016.1192702

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Mark Cutler
Mark Cutler

University Of Dundee

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Eirini Politi
Stuart N MacCallum
Mark Cutler
+3 more

Abstract

The GloboLakes project, a global observatory of lake responses to environmental change, aims to exploit current satellite missions and long remote-sensing archives to synoptically study multiple lake ecosystems, assess their current condition, reconstruct past trends to system trajectories, and assess lake sensitivity to multiple drivers of change. Here we describe the selection protocol for including lakes in the global observatory based upon remote-sensing techniques and an initial pool of the largest 3721 lakes and reservoirs in the world, as listed in the Global Lakes and Wetlands Database. An 18-year-long archive of satellite data was used to create spatial and temporal filters for the identification of waterbodies that are appropriate for remote-sensing methods. Further criteria were applied and tested to ensure the candidate sites span a wide range of ecological settings and characteristics; a total 960 lakes, lagoons, and reservoirs were selected. The methodology proposed here is applicable to new generation satellites, such as the European Space Agency Sentinel-series.

How to cite this publication

Eirini Politi, Stuart N MacCallum, Mark Cutler, Christopher J. Merchant, John S. Rowan, Terence P. Dawson (2016). Selection of a network of large lakes and reservoirs suitable for global environmental change analysis using Earth Observation. International Journal of Remote Sensing, 37(13), pp. 3042-3060, DOI: 10.1080/01431161.2016.1192702.

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Publication Details

Type

Article

Year

2016

Authors

6

Datasets

0

Total Files

0

Language

English

Journal

International Journal of Remote Sensing

DOI

10.1080/01431161.2016.1192702

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