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Trends in the Seaward Extent of Saltmarshes across Europe from Long-Term Satellite Data

1
NIOZ Royal Netherlands Institute for Sea Research, Department of Estuarine and Delta Systems, and Utrecht University, P.O. Box 140, 4400 AC Yerseke, The Netherlands
2
Shanghai Key Laboratory for Urban Ecological Processes and Eco-Restoration, School of Ecological and Environmental Science, East China Normal University, Shanghai 200241, China
3
Center for Global Change and Ecological Forecasting, School of Ecological and Environmental Science, East China Normal University, Shanghai 200241, China
4
Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(14), 1653; https://doi.org/10.3390/rs11141653
Received: 20 May 2019 / Revised: 4 July 2019 / Accepted: 9 July 2019 / Published: 11 July 2019
(This article belongs to the Special Issue Satellite-Based Wetland Observation)
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Abstract

Saltmarshes provide crucial functions for flora, fauna, and humankind. Thus far, studies of their dynamics and response to environmental drivers are limited in space and time. Satellite data allow for looking at saltmarshes on a large scale and over a long time period. We developed an unsupervised decision tree classification method to classify satellite images into saltmarsh vegetation, mudflat and open water, integrating additional land cover information. By using consecutive stacks of three years, we considered trends while taking into account water level variations. We used Landsat 5 TM data but found that other satellite data can be used as well. Classification performance for different periods of the Western Scheldt was almost perfect for this site, with overall accuracies above 90% and Kappa coefficients of over 0.85. Sensitivity analysis characterizes the method as being robust. Generated time series for 125 sites across Europe show saltmarsh area changes between 1986 and 2010. The method also worked using a global approach for these sites. We reveal transitions between saltmarsh, mudflat and open water, both at the saltmarsh lower edge and interior, but our method cannot detect changes at the saltmarsh-upland boundary. Resulting trends in saltmarsh dynamics can be coupled to environmental drivers, such as sea level, tidal currents, waves, and sediment availability. View Full-Text
Keywords: unsupervised classification; decision tree; Landsat; remote sensing; time series; saltmarsh dynamics; saltmarsh-mudflat interface; habitat change; Europe unsupervised classification; decision tree; Landsat; remote sensing; time series; saltmarsh dynamics; saltmarsh-mudflat interface; habitat change; Europe
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Laengner, M.L.; Siteur, K.; van der Wal, D. Trends in the Seaward Extent of Saltmarshes across Europe from Long-Term Satellite Data. Remote Sens. 2019, 11, 1653.

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