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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/hess-2020-198
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2020-198
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  30 Jun 2020

30 Jun 2020

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This preprint is currently under review for the journal HESS.

Assessing historic water extents in rapidly changing lakes: a hybrid remote sensing classification approach

Connor Mullen and Marc F. Muller Connor Mullen and Marc F. Muller
  • Department of Civil and Environmental Engineering and Earth Sciences at University of Notre Dame

Abstract. The empirical attribution of rapid hydrologic change presents a unique data availability challenge in terms of establishing baseline prior conditions. On the one hand, one cannot go back in time to collect the necessary in situ data if it were not serendipitously collected when the change was taking place. On the other hand, modern satellite monitoring missions are often too recent to capture changes that are ancient enough to provide sufficient observations for adequate statistical inference. In that context, the four decades of continuous global high resolution monitoring enabled by the Landsat missions are an unrivaled source of information to study hydrologic change globally. However, extracting the relevant time series information in a systematic way across Landsat missions remains a monumental challenge. Cloud masking and inconsistent image quality often complicate the automatized interpretation of optical imagery.

Focusing on the monitoring of lake water extents, we address this challenge by coupling supervised and unsupervised image classification techniques. Unsupervised classification is first used to detect water on unmasked (cloudless and high quality) pixels. Classification results are then compiled across images to estimate the inundation frequency of each pixel, hinging on the assumption that different pixels will be masked at different times. Inundation frequency is then leveraged to infer the inundation status of masked pixels on individual images through supervised classification. Applied to a representative set of global and rapidly changing lakes, the approach successfully captured water extent fluctuations obtained from in situ gauges (when applicable), or from other Landsat missions during overlapping time periods.

Connor Mullen and Marc F. Muller

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Connor Mullen and Marc F. Muller

Connor Mullen and Marc F. Muller

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Latest update: 28 Sep 2020
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Short summary
The level of lake water is rapidly changing globally and long-term, consistent observations of lake water extents are essential to ascertain and attribute these changes. This data is rarely collected and challenging to obtain from satellite imagery. The proposed method addresses these challenges without any local data and successfully validated against lakes with and without ground data. The algorithm stands as a valuable tool for the reliable historical water extent of changing lakes.
The level of lake water is rapidly changing globally and long-term, consistent observations of...
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