Suchergebnisse

The SDGs are a universal agenda to address the world's most pressing societal, environmental and economic challenges. The supply of timely, relevant and reliable data is essential in guiding policies and decisions for successful implementation of the SDGs. Yet official statistics cannot provide all of the data needed to populate the SDG indicator framework. Citizen science offers a novel solution and an untapped opportunity to complement traditional sources of data, such as household surveys, for monitoring progress towards the SDGs, while at the same time mobilizing action and raising awareness for their achievement. This paper presents the potential offered by one specific citizen science tool, Picture Pile, to complement and enhance official statistics to monitor several SDGs and targets. Designed to be a generic and flexible tool, Picture Pile is a web-based and mobile application for ingesting imagery from satellites, orthophotos, unmanned aerial vehicles or geotagged photographs that can then be rapidly classified by volunteers. The results show that Picture Pile could contribute to the monitoring of fifteen SDG indicators under goals 1, 2, 11, 13, 14 and 15 based on the Picture Pile campaigns undertaken to date. Picture Pile could also be modified to support other SDGs and indicators in the areas of ecosystem health, eutrophication and built-up areas, among others. In order to leverage this particular tool for SDG monitoring, its potential must be showcased through the development of use cases in collaboration with governments, NSOs and relevant custodian agencies. Additionally, mutual trust needs to be built among key stakeholders to agree on common goals that would facilitate the use of Picture Pile or other citizen science tools and data for SDG monitoring and impact.

The continued increase of anthropogenic pressure on the Earth's ecosystems is degrading the natural environment and then decreasing the services it provides to humans. The type, quantity, and quality of many of those services are directly connected to land cover, yet competing demands for land continue to drive rapid land cover change, affecting ecosystem services. Accurate and updated land cover information is thus more important than ever, however, despite its importance, the needs of many users remain only partially attended. A key underlying reason for this is that user needs vary widely, since most current products – and there are many available – are produced for a specific type of end user, for example the climate modelling community. With this in mind we focus on the need for flexible, automated processing approaches that support on-demand, customized land cover products at various scales. Although land cover processing systems are gradually evolving in this direction there is much more to do and several important challenges must be addressed, including high quality reference data for training and validation and even better access to satellite data. Here, we 1) present a generic system architecture that we suggest land cover production systems evolve towards, 2) discuss the challenges involved, and 3) propose a step forward. Flexible systems that can generate on-demand products that match users' specific needs would fundamentally change the relationship between users and land cover products – requiring more government support to make these systems a reality.

The continued increase of anthropogenic pressure on the Earth's ecosystems is degrading the natural environment and then decreasing the services it provides to humans. The type, quantity, and quality of many of those services are directly connected to land cover, yet competing demands for land continue to drive rapid land cover change, affecting ecosystem services. Accurate and updated land cover information is thus more important than ever, however, despite its importance, the needs of many users remain only partially attended. A key underlying reason for this is that user needs vary widely, since most current products – and there are many available – are produced for a specific type of end user, for example the climate modelling community. With this in mind we focus on the need for flexible, automated processing approaches that support on-demand, customized land cover products at various scales. Although land cover processing systems are gradually evolving in this direction there is much more to do and several important challenges must be addressed, including high quality reference data for training and validation and even better access to satellite data. Here, we 1) present a generic system architecture that we suggest land cover production systems evolve towards, 2) discuss the challenges involved, and 3) propose a step forward. Flexible systems that can generate on-demand products that match users' specific needs would fundamentally change the relationship between users and land cover products – requiring more government support to make these systems a reality.

Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation.

Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation.

Critical knowledge gaps seriously hinder efforts for building disaster resilience at all levels, especially in disaster-prone least developed countries. Information deficiency is most serious at local levels, especially in terms of spatial information on risk, resources, and capacities of communities. To tackle this challenge, we develop a general methodological approach that integrates community-based participatory mapping processes, one that has been widely used by governments and non-government organizations in the fields of natural resources management, disaster risk reduction and rural development, with emerging collaborative digital mapping techniques. We demonstrate the value and potential of this integrated participatory and collaborative mapping approach by conducting a pilot study in the flood-prone lower Karnali river basin in Western Nepal. The process engaged a wide range of stakeholders and non-stakeholder citizens to co-produce locally relevant geographic information on resources, capacities, and flood risks of selected communities. The new digital community maps are richer in content, more accurate, and easier to update and share than those produced by conventional Vulnerability and Capacity Assessments (VCAs), a variant of Participatory Rural Appraisal (PRA), that is widely used by various government and non-government organizations. We discuss how this integrated mapping approach may provide an effective link between coordinating and implementing local disaster risk reduction and resilience building interventions to designing and informing regional development plans, as well as its limitations in terms of technological barrier, map ownership, and empowerment potential.

Critical knowledge gaps seriously hinder efforts for building disaster resilience at all levels, especially in disaster-prone least developed countries. Information deficiency is most serious at local levels, especially in terms of spatial information on risk, resources, and capacities of communities. To tackle this challenge, we develop a general methodological approach that integrates community-based participatory mapping processes, one that has been widely used by governments and non-government organizations in the fields of natural resources management, disaster risk reduction and rural development, with emerging collaborative digital mapping techniques. We demonstrate the value and potential of this integrated participatory and collaborative mapping approach by conducting a pilot study in the flood-prone lower Karnali river basin in Western Nepal. The process engaged a wide range of stakeholders and non-stakeholder citizens to co-produce locally relevant geographic information on resources, capacities, and flood risks of selected communities. The new digital community maps are richer in content, more accurate, and easier to update and share than those produced by conventional Vulnerability and Capacity Assessments (VCAs), a variant of Participatory Rural Appraisal (PRA), that is widely used by various government and non-government organizations. We discuss how this integrated mapping approach may provide an effective link between coordinating and implementing local disaster risk reduction and resilience building interventions to designing and informing regional development plans, as well as its limitations in terms of technological barrier, map ownership, and empowerment potential.

There are many new land use and land cover (LULC) products emerging yet there is still a lack of in situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens. Moreover, the photographs submitted through the app were not always of sufficient quality. For these reasons, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree. Furthermore, a near real-time quality assurance system was implemented to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to determine whether citizens can provide high quality in situ data on LULC through crowdsourcing that can complement LUCAS. We compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. As shown by the cost comparisons with LUCAS, FotoQuest can complement LUCAS surveys by enabling continuous collection of large amounts of high quality, spatially explicit field data at a low cost.

There are many new land use and land cover (LULC) products emerging yet there is still a lack of in situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens. Moreover, the photographs submitted through the app were not always of sufficient quality. For these reasons, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree. Furthermore, a near real-time quality assurance system was implemented to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to determine whether citizens can provide high quality in situ data on LULC through crowdsourcing that can complement LUCAS. We compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. As shown by the cost comparisons with LUCAS, FotoQuest can complement LUCAS surveys by enabling continuous collection of large amounts of high quality, spatially explicit field data at a low cost.

The agriculture, forestry and other land use (AFOLU) sectors contribute substantially to the net global anthropogenic greenhouse gas (GHG) emissions. To reduce these emissions under the Paris Agreement, effective mitigation actions are needed that require engagement of multiple stakeholders. Emission reduction also requires that accurate, consistent and comparable datasets are available for transparent reference and progress monitoring. Availability of free and open datasets and portals (referred to as independent data) increases, offering opportunities for improving and reconciling estimates of GHG emissions and mitigation options. Through an online survey, we investigated stakeholders' data needs for estimating forest area and change, forest biomass and emission factors, and AFOLU GHG emissions. The survey was completed by 359 respondents from governmental, intergovernmental and non-governmental organizations, research institutes and universities, and public and private companies. These can be grouped into data users and data providers. Our results show that current open and freely available datasets and portals are only able to fulfil stakeholder needs to a certain degree. Users require a) detailed documentation regarding the scope and usability of the data, b) comparability between alternative data sources, c) uncertainty estimates for evaluating mitigation options, d) more region-specific and detailed data with higher accuracy for sub-national application, e) regular updates and continuity for establishing consistent time series. These requirements are found to be key elements for increasing overall transparency of data sources, definitions, methodologies and assumptions, which is required under the Paris Agreement. Raising awareness and improving data availability through centralized platforms are important for increasing engagement of data users. In countries with low capacities, independent data can support countries' mitigation planning and implementation, and related GHG reporting. However, there is a strong need for further guidance and capacity development (i.e. 'readiness support') on how to make proper use of independent datasets. Continued investments will be needed to sustain programmes and keep improving datasets to serve the objectives of the many stakeholders involved in climate change mitigation and should focus on increased accessibility and transparency of data to encourage stakeholder involvement.

The agriculture, forestry and other land use (AFOLU) sectors contribute substantially to the net global anthropogenic greenhouse gas (GHG) emissions. To reduce these emissions under the Paris Agreement, effective mitigation actions are needed that require engagement of multiple stakeholders. Emission reduction also requires that accurate, consistent and comparable datasets are available for transparent reference and progress monitoring. Availability of free and open datasets and portals (referred to as independent data) increases, offering opportunities for improving and reconciling estimates of GHG emissions and mitigation options. Through an online survey, we investigated stakeholders' data needs for estimating forest area and change, forest biomass and emission factors, and AFOLU GHG emissions. The survey was completed by 359 respondents from governmental, intergovernmental and non-governmental organizations, research institutes and universities, and public and private companies. These can be grouped into data users and data providers. Our results show that current open and freely available datasets and portals are only able to fulfil stakeholder needs to a certain degree. Users require a) detailed documentation regarding the scope and usability of the data, b) comparability between alternative data sources, c) uncertainty estimates for evaluating mitigation options, d) more region-specific and detailed data with higher accuracy for sub-national application, e) regular updates and continuity for establishing consistent time series. These requirements are found to be key elements for increasing overall transparency of data sources, definitions, methodologies and assumptions, which is required under the Paris Agreement. Raising awareness and improving data availability through centralized platforms are important for increasing engagement of data users. In countries with low capacities, independent data can support countries' mitigation planning and implementation, and related GHG reporting. However, there is a strong need for further guidance and capacity development (i.e. 'readiness support') on how to make proper use of independent datasets. Continued investments will be needed to sustain programmes and keep improving datasets to serve the objectives of the many stakeholders involved in climate change mitigation and should focus on increased accessibility and transparency of data to encourage stakeholder involvement.

Citizens are providing vast amounts of georeferenced data in the form of in situ data collections as well as interpretations and digitization of Earth-observation (EO) data sets. These new data streams have considerable potential for supporting the calibration and validation of current and future products derived from EO. We provide a general introduction to this growing area of interest and review existing crowdsourcing and citizen science (CS) initiatives of relevance to EO. We then draw upon our own experiences to provide case studies that highlight different types of data collection and citizen engagement and discuss the various barriers to adoption. Finally, we highlight opportunities for how citizens can become part of an integrated EO monitoring system in the framework of the European Union (EU) space program, including Copernicus and other monitoring initiatives.

Citizens are providing vast amounts of georeferenced data in the form of in situ data collections as well as interpretations and digitization of Earth-observation (EO) data sets. These new data streams have considerable potential for supporting the calibration and validation of current and future products derived from EO. We provide a general introduction to this growing area of interest and review existing crowdsourcing and citizen science (CS) initiatives of relevance to EO. We then draw upon our own experiences to provide case studies that highlight different types of data collection and citizen engagement and discuss the various barriers to adoption. Finally, we highlight opportunities for how citizens can become part of an integrated EO monitoring system in the framework of the European Union (EU) space program, including Copernicus and other monitoring initiatives.

The contribution of citizen science to addressing societal challenges has long been recognized. The United Nations (UN) Sustainable Development Goals (SDGs), as an overarching policy framework and a roadmap to guide global development efforts until 2030 for achieving a better future for all, could benefit from the potential that citizen science offers. However, there is a lack of knowledge on the value of citizen science, particularly in addressing the data needs for SDG monitoring, among the UN agencies, national statistical offices, policy makers and the citizen science community itself. To address this challenge, we launched a Community of Practice on Citizen Science and the SDGs (SDGs CoP) in November 2018 as part of the EU Horizon 2020 funded WeObserve project. The SDGs CoP brings together citizen science researchers, practitioners, UN custodian agencies, broader data communities and other key actors to develop an understanding on how to demonstrate the value of citizen science for SDG achievement. The initial focus and the main objective of the SDGs CoP has been to conduct a research study to understand the contribution of citizen science to SDG monitoring and implementation. In this talk, we will present the work of the SDGs CoP. We will first discuss existing data gaps and needs for measuring progress on the SDGs, and then provide an overview on the results of a systematic review that we undertook within the CoP, showing where citizen science is already contributing and could contribute data to the SDG framework. We will provide concrete examples of our findings to demonstrate how citizen science data could inform the SDGs. We will also touch on the challenges for and barriers to the uptake of citizen science data for the SDG monitoring processes, and how to bring this source of data into the scope of official statistics.

The contribution of citizen science to addressing societal challenges has long been recognized. The United Nations (UN) Sustainable Development Goals (SDGs), as an overarching policy framework and a roadmap to guide global development efforts until 2030 for achieving a better future for all, could benefit from the potential that citizen science offers. However, there is a lack of knowledge on the value of citizen science, particularly in addressing the data needs for SDG monitoring, among the UN agencies, national statistical offices, policy makers and the citizen science community itself. To address this challenge, we launched a Community of Practice on Citizen Science and the SDGs (SDGs CoP) in November 2018 as part of the EU Horizon 2020 funded WeObserve project. The SDGs CoP brings together citizen science researchers, practitioners, UN custodian agencies, broader data communities and other key actors to develop an understanding on how to demonstrate the value of citizen science for SDG achievement. The initial focus and the main objective of the SDGs CoP has been to conduct a research study to understand the contribution of citizen science to SDG monitoring and implementation. In this talk, we will present the work of the SDGs CoP. We will first discuss existing data gaps and needs for measuring progress on the SDGs, and then provide an overview on the results of a systematic review that we undertook within the CoP, showing where citizen science is already contributing and could contribute data to the SDG framework. We will provide concrete examples of our findings to demonstrate how citizen science data could inform the SDGs. We will also touch on the challenges for and barriers to the uptake of citizen science data for the SDG monitoring processes, and how to bring this source of data into the scope of official statistics.