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With growing world population and concentration in urban and coastal areas, the exposure to natural hazards is increasing and results in higher risk of human and economic losses. Improving the identification of areas, population and assets potentially exposed to natural hazards is essential to reduce the consequences of such events. Disaster risk is a function of hazard, exposure and vulnerability. Modelling risk at the global level requires accessing and processing a large number of data, from numerous collaborating centres. These data need to be easily updated, and there is a need for centralizing access to this information as well as simplifying its use for non GIS specialists. The Hyogo Framework for Action provides the mandate for data sharing, so that governments and international development agencies can take appropriate decision for disaster risk reduction. Timely access and easy integration of geospatial data are essential to support efforts in Disaster Risk Reduction. However various issues in data availability, accessibility and integration limit the use of such data. In consequence, a framework that facilitate sharing and exchange of geospatial data on natural hazards should improve decision-making process. The PREVIEW Global Risk Data Platform is a highly interactive web-based GIS portal supported by a Spatial Data Infrastructure that offers free and interoperable access to more than 60 global data sets on nine types of natural hazards (tropical cyclones and related storm surges, drought, earthquakes, biomass fires, floods, landslides, tsunamis and volcanic eruptions) and related exposure and risk. This application portrays an easy-to-use online interactive mapping interface so that users can easily work with it and seamlessly integrate data in their own data flow using fully compliant OGC Web Services (OWS).

The Sustainable Development Goals (SDGs) established to be achieved by 2030 are an ensemble of 17 goals to address global environmental and social economic concerns [1]. SDG 15 concerns the protection of terrestrial ecosystems to halt biodiversity loss. Target 15.9 states that by 2020, ecosystem and biodiversity values should be integrated into national and local planning, and is related to Aichi Biodiversity Target 2 of the Strategic Plan for Biodiversity 2011-2020, which also involves integrating biodiversity values into national accounting and reporting systems [2]. The importance of maintaining ecosystem integrity is becoming widely recognized, not only to halt biodiversity loss, but also to preserve Nature's benefits to human well-being, and has been included in many other targets such as the EU 2020 Biodiversity Strategy's target 2, which requires the restoration of at least 15% of degraded ecosystems as well as the establishment of green infrastructures to enhance ecosystem services (ES) [3]. The Green Infrastructures (GI) framework is used as a policy tool and promotes the multi-functional use of landscapes to improve biodiversity conservation and benefits to society. It is formulated as a "strategically planned network of natural and semi-natural areas" [4] and is based on three main pillars: key habitats for target species, connectivity and ES [5]. As part of ERA-PLANET's GEOEssential project (Essential Variables workflows for resource efficiency and environmental management), our study aims at demonstrating how the GI framework can be implemented at any geographical area or time-period through reproducible modeling workflows from field data to Essential Variables (EV) data products and policy relevant indicators to monitor and inform advances towards environmental targets. A proof of concept workflow was already set in place for computing the indicator 15.1.2: Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem, while other workflows will follow. The execution platform is the GEOEssential Virtual Laboratory, a cloud-based virtual platform which enables access to, and execution of workflows for the ecosystem science community of practice and even more. REFERENCES: 1. UNSD, 2016. Sustainable Development Goals Report. https://unstats.un.org/sdgs/report/2016/ (accessed 18 May 2018). 2. CBD Secretariat, 2010. The Strategic Plan for Biodiversity 2011-2020, and the Aichi Biodiversity Targets. Secretariat of the Convention on Biological Diversity, Nagoya. 3. European Commission, 2011. Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions - Our life insurance, our natural capital: an EU biodiversity strategy to 2020, Brussels. 4. European Commission, 2013. Green infrastructure (GI) - Enhancing Europe's Natural Capital, Brussels. 5. Liquete, C., Kleeschulte, S., Dige, G., Maes, J., Grizzetti, B., Olah, B., & Zulian, G., 2015. Mapping green infrastructure based on ecosystem services and ecological networks: A Pan-European case study. Environmental Science & Policy, 54, 268–280.

Maintaining records of artisanal and small-scale mining sites in developing countries requires considerable effort, so it would be beneficial if Earth observation data from space could assist in the identifying and monitoring of such sites. Artificial light emissions are common at industrial-scale mining sites and have been associated with small-scale illegal mining in some contexts. Here, we examine whether known artisanal and small-scale mining sites in the Democratic Republic of the Congo (DRC) are associated with observations of night light emissions by the Visible Infrared Imaging Radiometer Suite Day/Night Band (DNB). Light emissions from the mining sites were not observed: the radiance observed from the sites was near zero and nearly identical to that observed for a set of randomly-chosen locations in the same region. While it is the case that DNB night lights' products provide useful data in other resource extraction contexts, they do not appear to be useful for identifying artisanal mining sites in the DRC.

Maintaining records of artisanal and small-scale mining sites in developing countries requires considerable effort, so it would be beneficial if Earth observation data from space could assist in the identifying and monitoring of such sites. Artificial light emissions are common at industrial-scale mining sites and have been associated with small-scale illegal mining in some contexts. Here, we examine whether known artisanal and small-scale mining sites in the Democratic Republic of the Congo (DRC) are associated with observations of night light emissions by the Visible Infrared Imaging Radiometer Suite Day/Night Band (DNB). Light emissions from the mining sites were not observed: the radiance observed from the sites was near zero and nearly identical to that observed for a set of randomly-chosen locations in the same region. While it is the case that DNB night lights' products provide useful data in other resource extraction contexts, they do not appear to be useful for identifying artisanal mining sites in the DRC.

Environmental sustainability is nowadays a major global issue that requires efficient and effective responses from governments. Essential variables (EV) have emerged in different scientific communities as a means to characterize and follow environmental changes through a set of measurements required to support policy evidence. To help track these changes, our planet has been under continuous observation from satellites since 1972. Currently, petabytes of satellite Earth observation (EO) data are freely available. However, the full information potential of EO data has not been yet realized because many big data challenges and complexity barriers hinder their effective use. Consequently, facilitating the production of EVs using the wealth of satellite EO data can be beneficial for environmental monitoring systems. In response to this issue, a comprehensive list of EVs that can take advantage of consistent time-series satellite data has been derived. In addition, a set of use-cases, using an Earth Observation Data Cube (EODC) to process large volumes of satellite data, have been implemented to demonstrate the practical applicability of EODC to produce EVs. The proposed approach has been successfully tested showing that EODC can facilitate the production of EVs at different scales and benefiting from the spatial and temporal dimension of satellite EO data for enhanced environmental monitoring.

Many countries will rely on the extractive sector to generate the inputs and revenues necessary to advance progress towards the sustainable development goals (SDGs). While the last decade has seen a strong push for financial transparency in the extractive sector, it is becoming equally necessary to also include the social and environmental performance of the extractive industries across the entire value chain. However, to maximize the value of this broad range of data for improved stakeholder dialogue and decision making, a geo-spatial approach is needed for effective data integration, management, analysis, and monitoring. This requires capacity building to extractive companies and to the various transparency initiatives to ensure that reporting and disclosure data is spatially enabled as well as inter-operable, open, quality controlled and published to a spatial data infrastructure (SDI) that is publically accessible. Ideally, this SDI can then inform and benefit many stakeholder dialogues, support reforms in natural resource governance, promote more equitable benefit-sharing, and enhance the performance of monitoring of the sector at the concession level. We discuss here the benefits and challenges of SDIs in the extractive sector. This is done using the experience gained by the authors in the design and implementation of a new Open Data Platform for the Extractive Sector called MAP-X (Mapping and Assessing the Performance of eXtractive Industries) in the Democratic Republic of Congo.

Introduction -- Landslide/ Slope failures/ Flood and glacial lake outburst floods (GLOFS) /Drought/ Desertification status mapping/ Tsunami/ Lightening/ Forest Fire/ Earthquake / Volcanic eruptions -- Statistical, multi-criteria decision making (MCDM), machine learning models (spatial prediction models) -- Challenges and future needs.

In Africa, natural resources are degrading, while being at the same time essential for maintaining or improving people's livelihood. The well-being of African communities is highly correlated to changes in local ecosystem services. Their vulnerability to degradation of natural resources is extremely high and resilience against natural changes (e.g. climate variability) and socio-economic changes (e.g. fluctuations in food markets) is low. Nowadays, it is widely accepted that reversing these trends and adapting to climate change require integrated responses tackling the underlying social, economic, political and institutional drivers of unsustainable use of natural resources. Integrated approaches intrinsically ask for cooperation, exchange of information and communication to better understand complex interactions and assess environmental issues. Understanding these interactions requires collecting and integrating various data describing physical, chemical, biological and socio-economic conditions. However, two common obstacles are currently preventing the implementation of such integrated approaches: (1) difficulties to find data, and (2) difficulties to integrate data. In response to these issues, this paper presents the Africa Discovery Broker, a web-based tool that enables users working in different domains to search through and access 32442 heterogeneous African geospatial resources (e.g. remote sensing, geospatial data, socio- economic data) coming from 17 international, regional, national and research projects repositories.

During the past two centuries, the world has undergone deep societal, political, and economical changes that heavily affected human life. The above changes contributed to an increased awareness about the deep impact that policy decisions have at the local and the global level. Therefore, there is a strong need that policy-making and decision-making processes for a sustainable development be based on the best available knowledge about Earth system and environment. The recent advance of information technologies enables running complex models that use the large amount of Earth Observation datasets available. However, data and model interoperability are still limited to the syntactic level allowing to access and process datasets independently of their structural characteristics (data format, co- ordinate reference systems, service interface, .) but with no clear reference to their content (the semantic level) and context of use (the pragmatic level). This poses heavy limitations to the reusability of scientific processes and related workflows. The paper presents a general framework to address this issue through the design of a Knowledge Base sup- porting data and model semantic (and pragmatic) interoperability. In this framework, a general ontology rep- resents the knowledge generation process for policy relevant decision-making, while multiple vocabularies formalize the semantics of data and models, identifying different types of observables, process variables, and indicators/indices. To evaluate the proposed approach to semantic interoperability of data and models, the Knowledge Base has been integrated with an advanced model-sharing framework, and a proof-of-concept has been developed for the assessment of one of the indicators of the Sustainable Development Goals defined by the United Nations.

Large sub-Saharan African cities are characterized by serious and persistent environmental problem of Solid Waste Management (SWM). The city of Kinshasa, in Democratic Republic of the Congo has a long lasting and major concern of SWM. More worryingly, with rapid population growth and urbanization, waste generation, both domestic and industrial, is expected to rise with great potential of health and environmental problems. Therefore, with an objective of bringing a possible solution that reduces the increasing problem of SWM, we explore in the present study the use of crowdsourcing as a possible mechanism to identify, localize, characterize solid waste landfills. The proposed approach allows (i) creating a spatial and temporal database through a participatory process to support data collection and information generation, and (ii) visualizing the spatial and temporal distribution of observations through an interactive map. Our database holds 187 observations of solid waste disposals across Kinshasa within two years with estimated volume of solid waste accounts of 587'920 m3. These observations include 61 public disposals (e.g., transit center), and 151 wild dumps, of which, 174 active and 3 inactive disposals. The approach developed in this study is a proof-of- concept and a successful implementation was achieved. Our conception and results provide new tool and mechanism to collect data and generate useful information on solid waste landfills in the city of Kinshasa that was not available before. The results of this study indicate that crowdsourcing can be a valuable mechanism to involve citizens in collecting data on solid waste landfills in the city of Kinshasa, which can be applied in similar urban cities.

Data concerning the occurrence of emerging biological contaminants such as antibiotic resistance genes (ARGs) and fecal indicator bacteria (FIB) in aquatic environments in Sub-Saharan African countries is limited. On the other hand, antibiotic resistance remains a worldwide problem which may pose serious potential risks to human and animal health. Consequently, there is a growing number of reports concerning the prevalence and dissemination of these contaminants into various environmental compartments. Sediments provide the opportunity to reconstruct the pollution history and evaluate impacts so this study investigates the abundance and distribution of toxic metals, FIB, and ARGs released from hospital effluent wastewaters and their presence in river sediments receiving systems. ARGs (blaTEM, blaCTX-M, blaSHV, and aadA), total bacterial load, and selected bacterial species FIB (E. coli, Enterococcus (ENT)) and Pseudomonas species (Psd) were quantified by targeting species specific genes using quantitative PCR (qPCR) in total DNA extracted from the sediments recovered from 4 hospital outlet pipes (HOP) and their river receiving systems in the City of Kinshasa in the Democratic Republic of the Congo. The results highlight the great concentration of toxic metals in HOP, reaching the values (in mg kg-1) of 47.9 (Cr), 213.6 (Cu), 1434.4 (Zn), 2.6 (Cd), 281.5 (Pb), and 13.6 (Hg). The results also highlight the highest (P˂0.05) values of 16S rRNA, FIB, and ARGs copy numbers in all sampling sites including upstream (control site), discharge point, and downstream of receiving rivers, indicating that the hospital effluent water is not an exclusive source of the biological contaminants entering the urban rivers. Significant correlation were observed between (i) all analyzed ARGs and total bacterial load (16S rRNA) 0.51 to 0.72 (p<0.001, n=65); (ii) ARGs (except blaTEM) and FIB and Psd 0.57 < r < 0.82 (p<0.001, n=65); and (iii) ARGs (except blaTEM) and toxic metals (Cd, Cr, Cu, and Zn) 0.44 to 0.72, (p<0.001, ...

60 Pags., 8 Figs. The definitive version is available at: http://www.sciencedirect.com/science/journal/01436228 ; Understanding, monitoring and mitigating drought is a very difficult task as a consequence of the intrinsic nature of the phenomenon. In addition, assessing the impact of drought on ecosystems and societies is also a complex task, because the same drought severity may have different consequences in different regions and systems due to the underlying vulnerabilities. New technologies based on geospatial information are available to determine the risk and vulnerability of a system to a drought and to develop monitoring and early warning systems based on real-time information to support decision making. To improve drought preparedness and mitigation, geospatial datasets based on climate information, Earth Observation Systems and statistical and dynamical modelling methodologies can make a noticeably difference in mitigating drought impacts in Africa. In this article we illustrate how the development of drought information systems based on geospatial technology, that combines static and real-time information, could improve the possibilities of drought mitigation in Africa. We stress that it is necessary to go beyond past attempts to manage drought risk based on a reactive crisis-response approach, by promoting drought mitigation and preparedness at the national and regional levels. For this purpose the development of drought information tools is fundamental for the implementation of drought management plans and to support real-time decision-making. ; This work has been supported by the research projects CGL2006-11619/HID, CGL2008-01189/BTE, CGL2011-27574-CO2-02, CGL2011-27753-CO2-01 and CGL2011-27536 financed by the Spanish Commission of Science and Technology and FEDER, EUROGEOSS (FP7-ENV-2008-1-226487), AfroMaison (Grant agreement no 266379) and ACQWA (Grant Agreement n°212250) financed by the 7th Framework Programme of the European Commission, "La nieve en el Pirineo Aragonés y su respuesta a la variabilidad climática" and "Efecto de los escenarios de cambio climático sobre la hidrología superficial y la gestión de embalses del Pirineo Aragonés" financed by "Obra Social La Caixa" and the Aragón Government and Influencia del cambio climático en el turismo de nieve. CTTP01/10, Financed by the Comisión de Trabajo de los Pirineos. ; Peer reviewed