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Marine ecosystems are under high demand for human use, giving concerns about how pressures from human activities may affect their structure, function, and status. In Europe, recent developments in mapping of marine habitats and human activities now enable a coherent spatial evaluation of potential combined effects of human activities. Results indicate that combined effects from multiple human pressures are spread to 96% of the European marine area, and more specifically that combined effects from physical disturbance are spread to 86% of the coastal area and 46% of the shelf area. We compare our approach with corresponding assessments at other spatial scales and validate our results with European-scale status assessments for coastal waters. Uncertainties and development points are identified. Still, the results suggest that Europe’s seas are widely disturbed, indicating potential discrepancy between ambitions for Blue Growth and the objective of achieving good environmental status within the Marine Strategy Framework Directive.

Due to high terrestrial runoff, the Baltic Sea is rich in dissolved organic carbon (DOC), the light-absorbing fraction of which is referred to as colored dissolved organic matter (CDOM). Inputs of DOC and CDOM are predicted to increase with climate change, affecting coastal ecosystems. We found that the relationships between DOC, CDOM, salinity, and Secchi depth all differed between the two coastal areas studied; the W Gulf of Bothnia with high terrestrial input and the NW Baltic Proper with relatively little terrestrial input. The CDOM:DOC ratio was higher in the Gulf of Bothnia, where CDOM had a greater influence on the Secchi depth, which is used as an indicator of eutrophication and hence important for Baltic Sea management. Based on the results of this study, we recommend regular CDOM measurements in monitoring programmes, to increase the value of concurrent Secchi depth measurements.Electronic supplementary materialThe online version of this article (doi:10.1007/s13280-015-0658-4) contains supplementary material, which is available to authorized users.

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(2017) Retrieval of suspended particulate matter from turbidity -model development, validation, and application to MERIS data over the Baltic Sea, International Journal of Remote Sensing, 38:7, 1983, DOI: 10.1080/01431161.2016 Turbidity is related to the concentration of SPM which usually is measured gravimetrically, a rather time-consuming method.Measuring turbidity is quick and easy, and therefore also more cost-effective. When derived from remote sensing data the method becomes even more cost-effective because of the good spatial resolution of satellite data and the synoptic capability of the method. Turbidity is also listed in the European Union's Marine Strategy Framework Directive as a supporting monitoring parameter, especially in the coastal zone. In this study, we aim to provide a new Baltic Sea algorithm to retrieve SPM concentration from in situ turbidity and investigate how this can be applied to satellite data. An in situ dataset was collected in Swedish coastal waters to develop a new SPM model. The model was then tested against independent datasets from both Swedish and Lithuanian coastal waters. Despite the optical variability in the datasets, SPM and turbidity were strongly correlated (r = 0.97). The developed model predicts SPM reliably from in situ turbidity (R 2 = 0.93) with a mean normalized bias (MNB) of 2.4% for the Swedish and 14.0% for the Lithuanian datasets, and a relative error (RMS) of 25.3% and 37.3%, respectively. In the validation dataset, turbidity ranged from 0.3 to 49.8 FNU (Formazin Nephelometric Unit) and correspondingly, SPM concentration ranged from 0.3 to 34.0 g m -3which covers the ranges typical for Baltic Sea waters. Next, the medium-resolution imaging spectrometer (MERIS) standard SPM product MERIS Ground Segment (MEGS) was tested on all available match-up data (n = 67). The correlation between SPM retrieved from MERIS and in situ SPM was strong for the Swedish dataset with r = 0.74 (RMS = 47.4 and MNB = 11.3%; n = 32) and very strong for the Lithuanian dataset with r = 0.94 (RMS = 29.5% and MNB = −1.5%; n = 35). Then, the turbidity was derived from the MERIS standard SPM product using the new in situ SPM model, but retrieving turbidity from SPM instead. The derived image was then compared to existing in situ data and showed to be in the right range of values for each sub-area. The new SPM model REMOTE SENSING, 2017 VOL. 38, NO. 7, 1983 http://dx.doi.org/10.1080/01431161.2016 © 2016 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http:// creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.provides a robust and cost-efficient method to determine SPM from in situ turbidity measurements (or vice versa). The developed SPM model...