Supporting Earth-Observation Calibration and Validation: A new generation of tools for crowdsourcing and citizen science

See, Linda; Dias, Eduardo; Hendriks, E.; Mijling, Bas; Snik, Frans; Stammes, P.; Vescovi, F.; Zeug, Gunter; Mathieu, Pierre-Philippe; Desnos, Yves-Louis; Rast, M.

doi:10.1109/mgrs.2015.2498840

Cited by 19 publications

(11 citation statements)

References 41 publications

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“…We also examined the overlap between contributions from citizen science and EO to the SDG indicators, which was the case for 24 indicators, to identify the potential for these 2 data sources to complement each other. For example, citizen science can support EO in two different ways: satellite (and other remotely sensed) image interpretation and data collection, which could help in the calibration and validation of products generated by remote sensing (See et al 2016). The GEO Global Agricultural Monitoring (GEOGLAM) is an initiative that aims to strengthen global agricultural monitoring by improving the use of remote sensing tools for crop production projections and weather forecasting.…”

Section: Discussionmentioning

confidence: 99%

Mapping citizen science contributions to the UN sustainable development goals

et al. 2020

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The UN Sustainable Development Goals (SDGs) are a vision for achieving a sustainable future. Reliable, timely, comprehensive, and consistent data are critical for measuring progress towards, and ultimately achieving, the SDGs. Data from citizen science represent one new source of data that could be used for SDG reporting and monitoring. However, information is still lacking regarding the current and potential contributions of citizen science to the SDG indicator framework. Through a systematic review of the metadata and work plans of the 244 SDG indicators, as well as the identification of past and ongoing citizen science initiatives that could directly or indirectly provide data for these indicators, this paper presents an overview of where citizen science is already contributing and could contribute data to the SDG indicator framework. The results demonstrate that citizen science is “already contributing” to the monitoring of 5 SDG indicators, and that citizen science “could contribute” to 76 indicators, which, together, equates to around 33%. Our analysis also shows that the greatest inputs from citizen science to the SDG framework relate to SDG 15 Life on Land, SDG 11 Sustainable Cities and Communities, SDG 3 Good Health and Wellbeing, and SDG 6 Clean Water and Sanitation. Realizing the full potential of citizen science requires demonstrating its value in the global data ecosystem, building partnerships around citizen science data to accelerate SDG progress, and leveraging investments to enhance its use and impact.

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Section: Discussionmentioning

confidence: 99%

Mapping citizen science contributions to the UN sustainable development goals

et al. 2020

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“…High-school students contributed to the detection of illegal deforestation in Borneo via the Earthwatchers project (See et al, 2016). As far as the authors know, the TAILS database used in this study could allow for the first opportunity to use an online crowdsourcing project to monitor compliance of endangered species regulations.…”

Section: Resultsmentioning

confidence: 99%

Free Aerial Imagery as a Resource to Monitor Compliance with the Endangered Species Act

Malcom¹,

Kim

Li³

2017

Preprint

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Compliance monitoring is an integral part of law and policy implementation. A lack of compliance monitoring for the Endangered Species Act (ESA), driven in part by resource limitations, may be undercutting efforts to recover threatened and endangered species. Here we evaluate the utility of freely available satellite and aerial imagery as a cost-efficient component of ESA compliance monitoring. Using data on actions authorized by the U.S. Fish and Wildlife Service (FWS) under section 7 of the ESA, we show that approximately 40% of actions can be found in remotely sensed imagery. Some types of actions, such as residential and commercial development, roadwork, and forestry, show substantially higher observability. Based on our results and the requirements of compliance monitoring, we recommend FWS standardize data collection requirements for consultations; record and publish terms and conditions of consultations; and encourage their staff to use technology such as remotely sensed data as a central part of their workflow for implementing the ESA.

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“…LUCAS is currently the only official validation dataset for products such as CORINE land cover and the very-high-resolution (VHR) layers produced as part of the Copernicus land monitoring service (Büttner and Eiselt, 2013;Gallego, 2011). Thus, any additional in-situ data have great value for calibration and validation of products from Earth Observation, especially in terms of density and frequency of updating (See et al, 2016a). Initial results from a comparison of land cover and land use data collected from the app with the authoritative LUCAS data indicate that volunteers are able to identify basic land cover and land use types on the ground but that more detailed land cover types will require some training (Laso Bayas et al, 2016).…”

Section: Active Framework Datamentioning

confidence: 99%

Sources of VGI for Mapping

See

Estima

Pődör

et al. 2017

Mapping and the Citizen Sensor

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The concept of Volunteered Geographic Information (VGI) is often exemplified by the mapping of features in OpenStreetMap (OSM), yet there are many other sources of VGI available. Some VGI is very focused on the creation of map-based products, while in other applications location is simply one attribute that is routinely collected, due to the proliferation of Global Positioning System (GPS) enabled devices, e.g. mobile phones and tablets. This chapter aims to provide an overview of the variety of sources of VGI currently available, categorised according to whether they can contribute to framework data (i.e. the type of data that are commonly part of the spatial data infrastructure of national mapping agencies and governments) or not and whether the data have been actively or passively collected. A range of examples are presented to illustrate the different types of VGI in each of these main categories. Finally, the chapter discusses some of the main issues surrounding the use of VGI and points to chapters in the book where these issues are described in more detail.

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Supporting Earth-Observation Calibration and Validation: A new generation of tools for crowdsourcing and citizen science

Cited by 19 publications

References 41 publications

Mapping citizen science contributions to the UN sustainable development goals

Mapping citizen science contributions to the UN sustainable development goals

Free Aerial Imagery as a Resource to Monitor Compliance with the Endangered Species Act

Sources of VGI for Mapping

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