Climate change, increasing population and changes in land use are all rapidly driving the need to be able to better understand surface water dynamics. The targets set by the United Nations under Sustainable Development Goal 6 in relation to freshwater ecosystems also make accurate surface water monitoring increasingly vital. However, the last decades have seen a steady decline in in situ hydrological monitoring and the availability of the growing volume of environmental data from free and open satellite systems is increasingly being recognized as an essential tool for largescale monitoring of water resources. The scientific literature holds many promising studies on satellite-based surface-water mapping, but a systematic evaluation has been lacking. Therefore, a round robin exercise was organized to conduct an intercomparison of 14 different satellite-based approaches for monitoring inland surface dynamics with Sentinel-1, Sentinel-2, and Landsat 8 imagery. The objective was to achieve a better understanding of the pros and cons of different sensors and models for surface water detection and monitoring. Results indicate that, while using a single sensor approach (applying either optical or radar satellite data) can provide comprehensive results for very specific localities, a dual sensor approach (combining data from both optical and radar satellites) is the most effective way to undertake largescale national and regional surface water mapping across bioclimatic gradients.

As global warming is causing a significant reduction in the extent and the thickness of Arctic sea ice, a narrative for future large-scale commercial shipping in the Arctic has started to evolve. However, while most efforts have aimed to assess the financial benefits along with potential savings in greenhouse gas emissions, few studies have explored risk patterns and safety issues associated with Arctic shipping and its current commercial alternatives in a geospatial manner. This study aims at addressing this gap in the literature, by assessing the risk factors associated with Arctic shipping, in particular, risks along the Northern Sea Route (NSR) connecting European and Asian ports. Its main component includes a global least-cost analysis, aiming to assess global and Arctic risk factors associated with commercial shipping, to derive the least risk paths from 10 major ports in Europe to 10 in Asia. To project the results into the future, and thus assess the future potential of the NSR as a viable and less risky alternative, a dataset on predicted sea ice thickness within 2010–2099, restricted the analysis based on the penetration capabilities of three selected vessel types. Ultimately, the study concludes that maritime transportation, bound for East-Asia, commencing from most northern European ports, could benefit from the NSR, not just in terms of distance savings, cost reduction and less CO2 production, but also as a safer alternative. Lastly, two recommendations drawn from our results aim to assist decision-makers in facilitating future Arctic transportation by addressing accessibility and safety issues. Highlights

The United Nations 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDG’s) presents a roadmap and a concerted platform of action towards achieving sustainable and inclusive development, leaving no one behind, while preventing environmental degradation and loss of natural resources. However, population growth, increased urbanisation, deforestation, and rapid economic development has decidedly modified the surface of the earth, resulting in dramatic land cover changes, which continue to cause significant degradation of environmental attributes. In order to reshape policies and management frameworks conforming to the objectives of the SDG’s, it is paramount to understand the driving mechanisms of land use changes and determine future patterns of change. This study aims to assess and quantify future land cover changes in Virunga National Park in the Democratic Republic of the Congo by simulating a future landscape for the SDG target year of 2030 in order to provide evidence to support data-driven decision-making processes conforming to the requirements of the SDG’s. The study follows six sequential steps: (a) creation of three land cover maps from 2010, 2015 and 2019 derived from satellite images; (b) land change analysis by cross-tabulation of land cover maps; (c) submodel creation and identification of explanatory variables and dataset creation for each variable; (d) calculation of transition potentials of major transitions within the case study area using machine learning algorithms; (e) change quantification and prediction using Markov chain analysis; and (f) prediction of a 2030 land cover. The model was successfully able to simulate future land cover and land use changes and the dynamics conclude that agricultural expansion and urban development is expected to significantly reduce Virunga’s forest and open land areas in the next 11 years. Accessibility in terms of landscape topography and proximity to existing human activities are concluded to be primary drivers of these changes. Drawing on these conclusions, the discussion provides recommendations and reflections on how the predicted future land cover changes can be used to support and underpin policy frameworks towards achieving the SDG’s and the 2030 Agenda for Sustainable Development.