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Exacerbated by climate change, Europe has experienced series of hot and dry summer since the beginning of the 21st century. The importance of land conditions became an international concern with a dedicated sustainable development goal (SDG), the SDG 15. It calls for developing and finding innovative solutions to follow and evaluate impacts of changing land conditions induced by various driving forces. In Switzerland, drought risk will significantly increase in the coming decades with severe consequences on agriculture, energy production and vegetation. In this paper, we used a 35-year satellite-derived annual and seasonal times-series of normalized difference water index (NDWI) to follow vegetation water content evolution at different spatial and temporal scales across Switzerland and related them to temperature and precipitation to investigate possible responses of changing climatic conditions. Results indicate that there is a small and slow drying tendency at the country scale with a NDWI mean decreasing slope of −0.22%/year for the 23% significant pixels across Switzerland. This tendency is mostly visible below 2000 m above sea level (m.a.s.l.) and in all biogeographical regions. The Southern Alps regions appear to be more responsive to changing drying conditions with a significant and slight negative NDWI trend (−0.39%/year) over the last 35 years. Moreover, NDWI values are mostly a function of temperature at elevations below the tree line rather than precipitation. Findings suggest that multi-annual and seasonal NDWI can be a valuable indicator to monitor vegetation water content at different scales, but other components such as land cover type and evapotranspiration should be considered to better characterize NDWI variability. Satellite Earth Observations data can provide valuable complementary observations for national statistics on the ecological state of vegetation to support SDG 15 to monitor land affected by drying conditions.

This paper discusses opportunities to use remote sensing (RS) technologies in addressing the persistent global challenges related to the artisanal and small-scale gold mining (ASGM) sector. The paper uses a systematic literature review to identify, analyze, and synthesize various uses of RS on the detection and monitoring of ASGM activities across the globe. The study covers the use of spaceborne sensors and available opportunities for data access and processing and emphasizes the important role that freely-available data has played in understanding ASGM activities. It discusses applications and opportunities offered in assessing the geospatial and temporal characteristics of ASGM and its impacts on the surrounding environment. Furthermore, it examines different indicators for the detection of ASGM in the landscape. Finally, technological capabilities described in the study are illustrated with case studies in the Democratic Republic of Congo and in Colombia using cloud computing with the Open Data Cube. The case studies demonstrate the identification and quantification of impacts of ASGM activities on land degradation and water turbidity in remote areas and results are dissiminated using the MapX platform. This facilitates policy development, implementation, and evaluation in the ASGM context.

To tackle Big Data challenges such as Volume, Variety, and Velocity, the Earth Observations Data Cube (EODC) concept has emerged as a solution for lowering barriers and offering new possibilities to harness the information power of satellite EO data. However, installing, configuring, and managing an EODC instance is still difficult requiring specific knowledge and capabilities. Consequently, facilitating and automating the generation and provision of EODC given specific user’s requirements can be beneficial.In response to this issue, this paper presents the Data Cube on Demand (DCoD) approach, a proof-of-concept that aims at facilitating the generation and use of an EODC instance virtually anywhere in the World. Users are only required to specify an area of interest; select the types of sensors between Landsat 5-7-8 and Sentinel-2; choose a desired temporal frame; and provide their email address to receive notifications. Then automatically an empty ODC instance is instantiated and desired data are ingested.The proposed approach has been successfully tested in two sites in Bolivia and DRC in the field of environmental monitoring. It has lowered many complexity barriers of such a new technology; greatly facilitated the generation and use of the Data Cube technology; enhanced data sovereignty; and ultimately can help reaching large adoption and acceptance.

This paper describes MapX, a spatial data infrastructure that aims to support the sustainable use of natural resources and the environment by increasing access to the best available geospatial information and related monitoring technologies. MapX is supported by an online platform that provides authoritative spatial data at local, national and global scales, an authentication data integrity framework using a scorecard and a set of on-line tools to visualize, analyze and access geospatial data. Originally created for stakeholders involved in the extractives sector, MapX has recently expanded to other fields where spatial data can help inform stakeholder dialog, prioritization of investments and impact monitoring such as disaster risk reduction, chemicals management, biodiversity planning, renewable energy and environmental security. This paper describes the open source software stack of the platform, its key functionalities as well as its planned features and concludes by capturing some of the key lessons learned. Keywords: Spatial data infrastructure, Open data, Data integrity, Natural resources