Volunteered data sources are readily available due to advances in electronic communications technology.For example,smartphones provide tools to collect ground-based observations over broad areas from a diverse set of ...Volunteered data sources are readily available due to advances in electronic communications technology.For example,smartphones provide tools to collect ground-based observations over broad areas from a diverse set of data collectors,including people with,and without,extensive training.In this study,volunteers used a smartphone application to collect ground-based observations.Forest structural components were then estimated over a broader area using high spatial resolution RapidEye remote sensing imagery(5 spectral bands 440–850 nm,5 m spatial resolution)and a digital elevation model following a three nearest neighbor approach(K-NN).Participants with professional forestry experience on average chose highpriority fuel load locations near buildings,while nonprofessional participants chose a broader range of conditions over a larger extent.When used together,the professional and nonprofessional observations provided a more complete assessment of forest conditions.A generalized framework is presented that utilizes K-NN imputation tools for estimating the distribution of forest fuels using remote sensing and topography variables,ensuring spatial representation,checking attribute accuracy,and evaluating predictor variables.Frameworks to integrate volunteered data from smartphone platforms with remote sensing may contribute toward more complete Earth observation for Digital Earth.展开更多
Citizen Science(CS)is a prominent field of application for Open Science(OS),and the two have strong synergies,such as:advocating for the data and metadata generated through science to be made publicly available[1];sup...Citizen Science(CS)is a prominent field of application for Open Science(OS),and the two have strong synergies,such as:advocating for the data and metadata generated through science to be made publicly available[1];supporting more equitable collaboration between different types of scientists and citizens;and facilitating knowledge transfer to a wider range of audiences[2].While primarily targeted at CS,the EU-Citizen.Science platform can also support OS.One of its key functions is to act as a knowledge hub to aggregate,disseminate and promote experience and know-how;for example,by profiling CS projects and collecting tools,resources and training materials relevant to both fields.To do this,the platform has developed an information architecture that incorporates the public participation in scientific research(PPSR)-Common Conceptual Model.This model consists of the Project Metadata Model,the Dataset Metadata Model and the Observation Data Model,which were specifically developed for CS initiatives.By implementing these,the platform will strengthen the interoperating arrangements that exist between other,similar platforms(e.g.,BioCollect and SciStarter)to ensure that CS and OS continue to grow globally in terms of participants,impact and fields of application.展开更多
基金National Science and Engineering Research Council(NSERC)Discovery grant to Coops and a NSERC Engage to Ferster,Coops,and Valhallaunder University of British Columbia ethics application H12-00257.
文摘Volunteered data sources are readily available due to advances in electronic communications technology.For example,smartphones provide tools to collect ground-based observations over broad areas from a diverse set of data collectors,including people with,and without,extensive training.In this study,volunteers used a smartphone application to collect ground-based observations.Forest structural components were then estimated over a broader area using high spatial resolution RapidEye remote sensing imagery(5 spectral bands 440–850 nm,5 m spatial resolution)and a digital elevation model following a three nearest neighbor approach(K-NN).Participants with professional forestry experience on average chose highpriority fuel load locations near buildings,while nonprofessional participants chose a broader range of conditions over a larger extent.When used together,the professional and nonprofessional observations provided a more complete assessment of forest conditions.A generalized framework is presented that utilizes K-NN imputation tools for estimating the distribution of forest fuels using remote sensing and topography variables,ensuring spatial representation,checking attribute accuracy,and evaluating predictor variables.Frameworks to integrate volunteered data from smartphone platforms with remote sensing may contribute toward more complete Earth observation for Digital Earth.
基金The EU-Citizen.Science project received funding from the EU’s Horizon 2020 Framework Program for Research and Innovation under grant agreement No.824580The research described in this paper is partly supported by the project“Citizen Science to promote creativity,scientific literacy,and innovation throughout Europe”(COST Action),which received funding from the EU’s Horizon 2020 Framework Program for Research and Innovation under grant agreement No.15212The opinions expressed are those of the authors and not necessarily those of the COST Action or the European Commission.
文摘Citizen Science(CS)is a prominent field of application for Open Science(OS),and the two have strong synergies,such as:advocating for the data and metadata generated through science to be made publicly available[1];supporting more equitable collaboration between different types of scientists and citizens;and facilitating knowledge transfer to a wider range of audiences[2].While primarily targeted at CS,the EU-Citizen.Science platform can also support OS.One of its key functions is to act as a knowledge hub to aggregate,disseminate and promote experience and know-how;for example,by profiling CS projects and collecting tools,resources and training materials relevant to both fields.To do this,the platform has developed an information architecture that incorporates the public participation in scientific research(PPSR)-Common Conceptual Model.This model consists of the Project Metadata Model,the Dataset Metadata Model and the Observation Data Model,which were specifically developed for CS initiatives.By implementing these,the platform will strengthen the interoperating arrangements that exist between other,similar platforms(e.g.,BioCollect and SciStarter)to ensure that CS and OS continue to grow globally in terms of participants,impact and fields of application.
