The absolute sea level trend from May 1995 to May 2019 in the Baltic Sea is analyzed by means of a regional monthly gridded dataset based on a dedicated processing of satellite altimetry data. In addition, we evaluate the role of the North Atlantic Oscillation and the wind patterns in shaping differences in sea level trend and variability at a sub-basin scale. To compile the altimetry dataset, we use information collected in coastal areas and from leads within sea-ice. The dataset is validated by comparison with tide gauges and the available global gridded altimetry products. The agreement between trends computed from satellite altimetry and tide gauges improves by 9%. The rise in sea level is statistically significant in the entire region of study and higher in winter than in summer. A gradient of over 3 mm/yr in sea level rise is observed, with the north and east of the basin rising more than the south-west. Part of this gradient (about 1 mm/yr) is directly explained by a regression analysis of the wind contribution on the sea level time series. A sub-basin analysis comparing the northernmost part (Bay of Bothnia) with the south-west reveals that the differences in winter sea level anomalies are related to different phases of the North-Atlantic Oscillation (0.71 correlation coefficient). Sea level anomalies are higher in the Bay of Bothnia when winter wind forcing pushes waters through Ekman transport from the south-west toward east and north. The study also demonstrates the maturity of enhanced satellite altimetry products to support local sea level studies in areas characterized by complex coastlines or sea-ice coverage. The processing chain used in this study can be exported to other regions, in particular to test the applicability in regions affected by larger ocean tides.

A new quantitative method extracts a landscape heterogeneity map (LaHMa) from hyper-temporal remote-sensing data. The feature extraction method is data-driven, unbiased, and builds on the commonly used data reduction technique of Iterative Self-Organizing Data Analysis (ISODATA) clustering with the support of divergence separability indices. First, the relevant spatial-temporal variation in normalized difference vegetation index (NDVI) is classified through ISODATA clustering. Second, a series of prepared cluster maps are overlaid to examine and detect the frequency with which boundaries between clusters occur at the same location. This step identifies the boundary strength between clusters and detects spatial heterogeneity within them. Results of the method are explored for the typical agriculture-defined landscape of the Mekong delta, Vietnam, using NDVI-imagery time-series from SPOT-Vegetation and MODIS-Terra. The method extracts useful landscape heterogeneity features and can support land-cover mapping requiring information on fragmentation and land-cover gradients.

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The coast is home to unique ecosystems, where complex ecological processes take place through the interaction of terrestrial, aquatic, atmospheric, and human landscapes. However, there are considerable knowledge and data gaps in achieving effective and future change-proof sustainable management of coastal zones around the world due to both technical and social barriers, as well as governance challenges. Currently, the role of Earth observation (EO) in addressing many of the recognised information gaps is small and under-utilised. While EO can provide much of the spatiotemporal information required for historical analysis and current status mapping, and offers the advantage of global coverage; its uptake can be limited by technical and methodological challenges associated mostly with lack of capacity and infrastructure, product accuracy and accessibility, costs, and institutional acceptance. While new initiatives and recent technological progress in the EO and information technology arena aim to tackle some of these issues so that EO products can be more easily used by non-EO experts, uptake is still limited. This paper discusses how EO can potentially inform transformative practices of planning in the coastal water zone, by using examples to demonstrate the EO potential in providing information relevant to decision-making framed by international agreements, such as the United Nations Agenda 2030, the Convention on Biological Diversity, and the Sendai Framework for Risk Reduction. By presenting evidence for how EO can contribute to innovative opportunities and data synergies at scale, the paper discusses opportunities and challenges for a more solution-led approach to sustainable coastal management.