Understanding citizen science and environmental monitoring: final report on behalf of UK Environmental Observation Framework

Citizen science can broadly be defined as the involvement of volunteers in science. Over the past decade there has been a rapid increase in the number of citizen science initiatives. The breadth of environmental-based citizen science is immense. Citizen scientists have surveyed for and monitored a broad range of taxa, and also contributed data on weather and habitats reflecting an increase in engagement with a diverse range of observational science. Citizen science has taken many varied approaches from citizen-led (co-created) projects with local community groups to, more commonly, scientist-led mass participation initiatives that are open to all sectors of society. Citizen science provides an indispensable means of combining environmental research with environmental education and wildlife recording. Here we provide a synthesis of extant citizen science projects using a novel cross-cutting approach to objectively assess understanding of citizen science and environmental monitoring including: 1. Brief overview of knowledge on the motivations of volunteers. 2. Semi-systematic review of environmental citizen science projects in order to understand the variety of extant citizen science projects. 3. Collation of detailed case studies on a selection of projects to complement the semi-systematic review. 4. Structured interviews with users of citizen science and environmental monitoring data focussing on policy, in order to more fully understand how citizen science can fit into policy needs. 5. Review of technology in citizen science and an exploration of future opportunities

TIME-RELATED QUALITY DIMENSIONS OF URBAN REMOTELY SENSED BIG DATA

Abstract. Our rapidly changing world requires new sources of image based information. The quickly changing urban areas, the maintenance and management of smart cities cannot only rely on traditional techniques based on remotely sensed data, but also new and progressive techniques must be involved. Among these technologies the volunteer based solutions are getting higher importance, like crowd-sourced image evaluations, mapping by satellite based positioning techniques or even observations done by unskilled people. Location based intelligence has become an everyday practice of our life. It is quite enough to mention the weather forecast and traffic monitoring applications, where everybody can act as an observer and acquired data – despite their heterogeneity in quality – provide great value. Such value intuitively increases when data are of better quality. In the age of visualization, real-time imaging, big data and crowd-sourced spatial data have revolutionary transformed our general applications. Most important factors of location based decisions are the time-related quality parameters of the used data. In this paper several time-related data quality dimensions and terms are defined. The paper analyses the time sensitive data characteristics of image-based crowd-sourced big data, presents quality challenges and perspectives of the users. The data quality analyses focus not only on the dimensions, but are also extended to quality related elements, metrics. The paper discusses the connection of data acquisition and processing techniques, considering even the big data aspects. The paper contains not only theoretical sections, strong practice-oriented examples on detecting quality problems are also covered. Some illustrative examples are the OpenStreetMap (OSM), where the development of urbanization and the increasing process of involving volunteers can be studied. This framework is continuing the previous activities of the Remote Sensing Data Quality Working Group (ICWGIII/IVb) of the ISPRS in the topic focusing on the temporal variety of our urban environment.</p

A strategic framework to support the implementation of citizen science for environmental monitoring. Final report to SEPA

In this report we provide a decision framework that can be used to guide whether and when to use a citizen science approach for environmental monitoring. Before using the decision framework we recommend that five precursors to a citizen science approach are considered

Citizens and cities: Leveraging citizen science and big data for sustainable urban development

3noopenCitizen science (CS), that is, the involvement of citizens in data collection or analysis for research projects, is becoming more widespread. This is due to the increasing digitalization of the general public and due to the increasing number of grand challenges that society is facing. Thanks to the contributions of common citizens in data collection and data analysis conducted through technology-mediated interactions, CS can produce a number of benefits for researchers, public organizations, policymakers, citizens, and society as a whole. Given the high density of socio-economic activities in cities, CS can be implemented in a particularly effective way in urban environments to help tackle many “grand challenges”, namely, the pressing environmental and social issues that societies are facing at present. However, CS still has untapped potential to be explored. Indeed, we contend that even though CS involves citizens for precisely defined scientific objectives, the interaction that occurs can also be leveraged to collect data beyond the original aim, thereby producing big data (BD). Through a multiple case studies analysis, we highlight how CS can be used to collect BD as well, which can be a valuable resource for researchers, public organizations, and policymakers. With this aim in mind, this study proposes the definition of a citizen-sourcing framework that jointly employs CS and BD, and it highlights which processes can be implemented to favor the sustainable development of urban environments. Moreover, we also discuss the looming dangers associated with citizen-sourcing as a result of technology-mediated interactions and the use of digital technologies, and we highlight possible future developments.openCappa F.; Franco S.; Rosso F.Cappa, F.; Franco, S.; Rosso, F

A European research roadmap for optimizing societal impact of big data on environment and energy efficiency

We present a roadmap to guide European research efforts towards a socially responsible big data economy that maximizes the positive impact of big data in environment and energy efficiency. The goal of the roadmap is to allow stakeholders and the big data community to identify and meet big data challenges, and to proceed with a shared understanding of the societal impact, positive and negative externalities, and concrete problems worth investigating. It builds upon a case study focused on the impact of big data practices in the context of Earth Observation that reveals both positive and negative effects in the areas of economy, society and ethics, legal frameworks and political issues. The roadmap identifies European technical and non-technical priorities in research and innovation to be addressed in the upcoming five years in order to deliver societal impact, develop skills and contribute to standardization.Comment: 6 pages, 2 figures, 1 tabl

Resilient seed systems for climate change adaptation and sustainable livelihoods in the East Africa sub-region: Report of training workshop, Addis Ababa Ethiopia, 17-21 September 2019

Bioversity International is implementing a Dutch-supported project entitled: Resilient seed systems for climate change adaptation and sustainable livelihoods in the East Africa sub-region. This work aims to boost timely and affordable access to good-quality seed for a portfolio of crops / varieties for millions of women and men farmers’ and their communities across east Africa. A first project training: i) contextualized farmer varietal selection, ii) provided practical demonstrations of tools for climate-change analysis, iii) introduced policy issues associated with managing crop diversity, iv) outlined characterization and evaluation of genetic resources, and v) articulated associated gender issues, and issues related to disseminating elite materials. The training concluded with a contextualizing field trip. In the workshop evaluation, 98% participants declared their overall satisfaction level to be high (74%) or medium (24%), indicating the training furnished them with good ideas for networking and using the tools and methods they learned about

Increasing resilience to natural hazards through crowd-sourcing in St. Vincent and the Grenadines

In this project we aim to demonstrate how volcanic environments exposed to multiple hazards tend to be characterised by a lack of relevant data available both in real time and over the longer term (e.g. months to years). This can be at least partially addressed by actively involving citizens, communities, scientists and other key stakeholders in the collection, analysis and sharing of observations, samples and measurements of changes in the environment. Such community monitoring and co-production of knowledge over time can also build trusting relationships and resilience (Stone et al. 2014). There are more than 100 institutions worldwide that monitor volcanoes and other natural hazards, contribute to early warning systems and are embedded in communities. They have a key role in building resilience alongside civil protection/emergency management agencies. In this report, we propose that such institutions are involved in big data initiatives and related research projects. In particular, we suggest that tools for crowd-sourcing may be of particular value. Citizen science, community monitoring and analysis of social media can build resilience by supporting: a) coordination and collaboration between scientists, authorities and citizens, b) decision-making by institutions and individuals, c) anticipation of natural hazards by monitoring institutions, authorities and citizens, d) capacity building of institutions and communities, and e) knowledge co-production. We propose a mobile phone app with a supporting website as an appropriate crowd-sourcing tool for St Vincent and the Grenadines. The monitoring institution is the key contact for users and leads on the required specifications based on local knowledge and experience. Remote support is provided from the UK on technical issues, research integration, data management, validation and evaluation. It is intended that the app facilitates building of long-term relationships between scientists, communities and authorities. Real-time contributions and analysis of social media support early warning, real-time awareness and real-time feedback enhancing the response of scientists and authorities. The app has potential to facilitate, for example, discussions on new or revised hazards maps, multiple hazard analysis and could contribute to real-time risk monitoring. Such an approach can be scaled up to facilitate regional use – and is transferable to other countries. Challenges of such an approach include data validation and quality assurance, redundancy in the system, motivating volunteers, managing expectations and ensuring safety. A combination of recruiting a core group of known and reliable users, training workshops, a code of conduct for users, identifying information influx thresholds beyond which external support might be needed, and continuing evaluation of both the data and the process will help to address these issues. The app is duplicated on the website in case mobile phone networks are down. Development of such approaches would fit well within research programmes on building resilience. Ideally such research should be interdisciplinary in acknowledgement of the diversity and complexity of topics that this embraces. There may be funding inequality between national monitoring institutions and international research institutions but these and other in-country institutions can help drive innovation and research if they are fully involved in problem-definition and research design. New innovations arising from increasing resolution (temporal and spatial) of EO products should lead to useful near-real time products from research and operational services. The app and website can ensure such diverse products from multiple sources are accessible to communities, scientists and authorities (as appropriate). Other innovations such as machine learning and data mining of time-series data collected by monitoring institutions may lead to new insights into physical processes which can support timely decision-making by scientists in particular (e.g. increasing alert levels)

Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges

Widely available digital technologies are empowering citizens who are increasingly well informed and involved in numerous water, climate, and environmental challenges. Citizen science can serve many different purposes, from the "pleasure of doing science" to complementing observations, increasing scientific literacy, and supporting collaborative behaviour to solve specific water management problems. Still, procedures on how to incorporate citizens' knowledge effectively to inform policy and decision-making are lagging behind. Moreover, general conceptual frameworks are unavailable, preventing the widespread uptake of citizen science approaches for more participatory cross-sectorial water governance. In this work, we identify the shared constituents, interfaces, and interlinkages between hydrological sciences and other academic and non-academic disciplines in addressing water issues. Our goal is to conceptualize a transdisciplinary framework for valuing citizen science and advancing the hydrological sciences. Joint efforts between hydrological, computer, and social sciences are envisaged for integrating human sensing and behavioural mechanisms into the framework. Expanding opportunities of online communities complement the fundamental value of on-site surveying and indigenous knowledge. This work is promoted by the Citizens AND HYdrology (CANDHY) Working Group established by the International Association of Hydrological Sciences (IAHS)