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Special Issue "Citizen Science and Earth Observation"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (1 August 2016)

Special Issue Editors

Guest Editor
Dr. Steffen Fritz

Group Leader—Earth Observation Systems ESM - Ecosystem Services and Management IIASA - International Institute for Applied Systems Analysis A-2361 Laxenburg, Austria
Website | E-Mail
Phone: +43-2236-807-353
Fax: +43-2236-807-599
Guest Editor
Dr. Cidália Maria Parreira da Costa Fonte

Faculty of Sciences and Technology, Department of Mathematics, University of Coimbra, Coimbra, Portugal; Institute for Systems Engineering and Computers at Coimbra (INESCC), Coimbra, Portugal
Website | E-Mail
Interests: geo-spatial data; volunteered geographic information; geo-spatial data quality; land use/land cover mapping; map validation

Special Issue Information

Dear Colleagues,

The term citizen science is used when scientific work is performed partially or completely by volunteers, which are usually non-experts. During the last decade, citizen science and projects which are based on user-generated content have dramatically increased. Citizen science in the field of Earth observation has started more recently and a number of projects have evolved which involve citizens in monitoring the environment. Furthermore, citizen-based observations can support earth observation in a number of different fields such as climate change, sustainable development, drought monitoring, land cover or land-use change. Moreover, there is the potential to use citizen-based observations in combination with other currently increasing earth observation data from new sensors such as the Sentinel family of satellites and Landsat. In particular, in situ data provided by citizens can be used for calibration and validation activities, as well as the conflation or combined use of satellite and citizen observations.
The issue which arises from data provided by citizens is the accuracy, and often a comparison with the “gold reference data” collected by experts is needed.

The proposed special issue welcomes contributions in the field of Earth observation and its applications with respect to:

  • Methods for citizen-based data collection
  • Innovative use of citizen observations
  • Mobilization of citizens
  • Combined use of satellite and citizen-based observations
  • Contributions of citizen observations to support authoritative data
  • Quality of citizen-based observations
  • Data conflation and data mining

Dr. Steffen Fritz
Dr. Cidália Costa Fonte
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • citizen science;
  • applications of earth observations;
  • crowdsourcing;
  • map validation;
  • data quality;
  • user generated content;
  • incentives to mobilize the crowd

Published Papers (12 papers)

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Editorial

Jump to: Research, Review

Open AccessEditorial The Role of Citizen Science in Earth Observation
Remote Sens. 2017, 9(4), 357; doi:10.3390/rs9040357
Received: 29 March 2017 / Revised: 29 March 2017 / Accepted: 29 March 2017 / Published: 11 April 2017
PDF Full-text (487 KB) | HTML Full-text | XML Full-text
Abstract
Citizen Science (CS) and crowdsourcing are two potentially valuable sources of data for Earth Observation (EO), which have yet to be fully exploited. Research in this area has increased rapidly during the last two decades, and there are now many examples of CS
[...] Read more.
Citizen Science (CS) and crowdsourcing are two potentially valuable sources of data for Earth Observation (EO), which have yet to be fully exploited. Research in this area has increased rapidly during the last two decades, and there are now many examples of CS projects that could provide valuable calibration and validation data for EO, yet are not integrated into operational monitoring systems. A special issue on the role of CS in EO has revealed continued trends in applications, covering a diverse set of fields from disaster response to environmental monitoring (land cover, forests, biodiversity and phenology). These papers touch upon many key challenges of CS including data quality and citizen engagement as well as the added value of CS including lower costs, higher temporal frequency and use of the data for calibration and validation of remotely-sensed imagery. Although still in the early stages of development, CS for EO clearly has a promising role to play in the future. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Figure 1

Research

Jump to: Editorial, Review

Open AccessArticle Citizen Science and Crowdsourcing for Earth Observations: An Analysis of Stakeholder Opinions on the Present and Future
Remote Sens. 2017, 9(1), 87; doi:10.3390/rs9010087
Received: 12 August 2016 / Revised: 25 November 2016 / Accepted: 11 January 2017 / Published: 19 January 2017
Cited by 1 | PDF Full-text (3055 KB) | HTML Full-text | XML Full-text
Abstract
The impact of Crowdsourcing and citizen science activities on academia, businesses, governance and society has been enormous. This is more prevalent today with citizens and communities collaborating with organizations, businesses and authorities to contribute in a variety of manners, starting from mere data
[...] Read more.
The impact of Crowdsourcing and citizen science activities on academia, businesses, governance and society has been enormous. This is more prevalent today with citizens and communities collaborating with organizations, businesses and authorities to contribute in a variety of manners, starting from mere data providers to being key stakeholders in various decision-making processes. The “Crowdsourcing for observations from Satellites” project is a recently concluded study supported by demonstration projects funded by European Space Agency (ESA). The objective of the project was to investigate the different facets of how crowdsourcing and citizen science impact upon the validation, use and enhancement of Observations from Satellites (OS) products and services. This paper presents our findings in a stakeholder analysis activity involving participants who are experts in crowdsourcing, citizen science for Earth Observations. The activity identified three critical areas that needs attention by the community as well as provides suggestions to potentially help in addressing some of the challenges identified. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Open AccessArticle Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology
Remote Sens. 2016, 8(11), 905; doi:10.3390/rs8110905
Received: 12 August 2016 / Revised: 14 October 2016 / Accepted: 26 October 2016 / Published: 1 November 2016
Cited by 1 | PDF Full-text (13157 KB) | HTML Full-text | XML Full-text
Abstract
Citizens are increasingly becoming involved in data collection, whether for scientific purposes, to carry out micro-tasks, or as part of a gamified, competitive application. In some cases, volunteered data collection overlaps with that of mapping agencies, e.g., the citizen-based mapping of features in
[...] Read more.
Citizens are increasingly becoming involved in data collection, whether for scientific purposes, to carry out micro-tasks, or as part of a gamified, competitive application. In some cases, volunteered data collection overlaps with that of mapping agencies, e.g., the citizen-based mapping of features in OpenStreetMap. LUCAS (Land Use Cover Area frame Sample) is one source of authoritative in-situ data that are collected every three years across EU member countries by trained personnel at a considerable cost to taxpayers. This paper presents a mobile application called FotoQuest Austria, which involves citizens in the crowdsourcing of in-situ land cover and land use data, including at locations of LUCAS sample points in Austria. The results from a campaign run during the summer of 2015 suggest that land cover and land use can be crowdsourced using a simple protocol based on LUCAS. This has implications for remote sensing as this data stream represents a new source of potentially valuable information for the training and validation of land cover maps as well as for area estimation purposes. Although the most detailed and challenging classes were more difficult for untrained citizens to recognize, the agreement between the crowdsourced data and the LUCAS data for basic high level land cover and land use classes in homogeneous areas (ca. 80%) shows clear potential. Recommendations for how to further improve the quality of the crowdsourced data in the context of LUCAS are provided so that this source of data might one day be accurate enough for land cover mapping purposes. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Open AccessFeature PaperArticle Citizen Bio-Optical Observations from Coast- and Ocean and Their Compatibility with Ocean Colour Satellite Measurements
Remote Sens. 2016, 8(11), 879; doi:10.3390/rs8110879
Received: 1 August 2016 / Revised: 30 September 2016 / Accepted: 15 October 2016 / Published: 25 October 2016
Cited by 3 | PDF Full-text (2019 KB) | HTML Full-text | XML Full-text
Abstract
Marine processes are observed with sensors from both the ground and space over large spatio-temporal scales. Citizen-based contributions can fill observational gaps and increase environmental stewardship amongst the public. For this purpose, tools and methods for citizen science need to (1) complement existing
[...] Read more.
Marine processes are observed with sensors from both the ground and space over large spatio-temporal scales. Citizen-based contributions can fill observational gaps and increase environmental stewardship amongst the public. For this purpose, tools and methods for citizen science need to (1) complement existing datasets; and (2) be affordable, while appealing to different user and developer groups. In this article, tools and methods developed in the 7th Framework Programme of European Union (EU FP 7) funded project Citclops (citizens’ observatories for coast and ocean optical monitoring) are reviewed. Tools range from a stand-alone smartphone app to devices with Arduino and 3-D printing, and hence are attractive to a diversity of users; from the general public to more specified maker- and open labware movements. Standardization to common water quality parameters and methods allows long-term storage in regular marine data repositories, such as SeaDataNet and EMODnet, thereby providing open data access. Due to the given intercomparability to existing remote sensing datasets, these tools are ready to complement the marine datapool. In the future, such combined satellite and citizen observations may set measurements by the engaged public in a larger context and hence increase their individual meaning. In a wider sense, a synoptic use can support research, management authorities, and societies at large. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Open AccessArticle Relasphone—Mobile and Participative In Situ Forest Biomass Measurements Supporting Satellite Image Mapping
Remote Sens. 2016, 8(10), 869; doi:10.3390/rs8100869
Received: 1 September 2016 / Revised: 28 September 2016 / Accepted: 11 October 2016 / Published: 21 October 2016
Cited by 2 | PDF Full-text (30369 KB) | HTML Full-text | XML Full-text
Abstract
Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones,
[...] Read more.
Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones, citizen science is a promising approach to increase spatial and temporal coverages of in situ forest observations in a cost-effective way. Digital cameras can be used as a relascope device to measure basal area, a forest density variable that is closely related to biomass. In this paper, we present the Relasphone mobile application with extensive accuracy assessment in two mixed forest sites from different biomes. Basal area measurements in Finland (boreal zone) were in good agreement with reference forest inventory plot data on pine ( R 2 = 0 . 75 , R M S E = 5 . 33 m 2 /ha), spruce ( R 2 = 0 . 75 , R M S E = 6 . 73 m 2 /ha) and birch ( R 2 = 0 . 71 , R M S E = 4 . 98 m 2 /ha), with total relative R M S E ( % ) = 29 . 66 % . In Durango, Mexico (temperate zone), Relasphone stem volume measurements were best for pine ( R 2 = 0 . 88 , R M S E = 32 . 46 m 3 /ha) and total stem volume ( R 2 = 0 . 87 , R M S E = 35 . 21 m 3 /ha). Relasphone data were then successfully utilized as the only reference data in combination with optical satellite images to produce biomass maps. The Relasphone concept has been validated for future use by citizens in other locations. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Open AccessArticle The Tasks of the Crowd: A Typology of Tasks in Geographic Information Crowdsourcing and a Case Study in Humanitarian Mapping
Remote Sens. 2016, 8(10), 859; doi:10.3390/rs8100859
Received: 2 August 2016 / Revised: 8 September 2016 / Accepted: 11 October 2016 / Published: 18 October 2016
Cited by 2 | PDF Full-text (10535 KB) | HTML Full-text | XML Full-text
Abstract
In the past few years, volunteers have produced geographic information of different kinds, using a variety of different crowdsourcing platforms, within a broad range of contexts. However, there is still a lack of clarity about the specific types of tasks that volunteers can
[...] Read more.
In the past few years, volunteers have produced geographic information of different kinds, using a variety of different crowdsourcing platforms, within a broad range of contexts. However, there is still a lack of clarity about the specific types of tasks that volunteers can perform for deriving geographic information from remotely sensed imagery, and how the quality of the produced information can be assessed for particular task types. To fill this gap, we analyse the existing literature and propose a typology of tasks in geographic information crowdsourcing, which distinguishes between classification, digitisation and conflation tasks. We then present a case study related to the “Missing Maps” project aimed at crowdsourced classification to support humanitarian aid. We use our typology to distinguish between the different types of crowdsourced tasks in the project and choose classification tasks related to identifying roads and settlements for an evaluation of the crowdsourced classification. This evaluation shows that the volunteers achieved a satisfactory overall performance (accuracy: 89%; sensitivity: 73%; and precision: 89%). We also analyse different factors that could influence the performance, concluding that volunteers were more likely to incorrectly classify tasks with small objects. Furthermore, agreement among volunteers was shown to be a very good predictor of the reliability of crowdsourced classification: tasks with the highest agreement level were 41 times more probable to be correctly classified by volunteers. The results thus show that the crowdsourced classification of remotely sensed imagery is able to generate geographic information about human settlements with a high level of quality. This study also makes clear the different sophistication levels of tasks that can be performed by volunteers and reveals some factors that may have an impact on their performance. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Open AccessArticle Comparing Road-Kill Datasets from Hunters and Citizen Scientists in a Landscape Context
Remote Sens. 2016, 8(10), 832; doi:10.3390/rs8100832
Received: 30 July 2016 / Revised: 23 September 2016 / Accepted: 28 September 2016 / Published: 10 October 2016
Cited by 1 | PDF Full-text (1002 KB) | HTML Full-text | XML Full-text
Abstract
Road traffic has severe effects on animals, especially when road-kills are involved. In many countries, official road-kill data are provided by hunters or police; there are also road-kill observations reported by citizen scientists. The aim of the current study was to test whether
[...] Read more.
Road traffic has severe effects on animals, especially when road-kills are involved. In many countries, official road-kill data are provided by hunters or police; there are also road-kill observations reported by citizen scientists. The aim of the current study was to test whether road-kill reports by hunters stem from similar landscapes than those reported by citizen scientists. We analysed the surrounding landscapes of 712 road-kill reportings of European hares in the province of Lower Austria. Our data showed that road-killed hares reported both by hunters and citizens are predominantly surrounded by arable land. No difference of hedges and solitary trees could be found between the two datasets. However, significant differences in landcover classes and surrounding road networks indicate that hunters’ and citizen scientists’ data are different. Hunters reported hares from landscapes with significantly higher percentages of arable land, and greater lengths of secondary roads. In contrast, citizens reported hares from landscapes with significantly higher percentages of urban or industrial areas and greater lengths of motorways, primary roads, and residential roads. From this we argue that hunters tend to report data mainly from their hunting areas, whereas citizens report data during their daily routine on the way to/from work. We conclude that a citizen science approach is an important source for road-kill data when used in addition to official data with the aim of obtaining an overview of road-kill events on a landscape scale. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Open AccessArticle Local Knowledge and Professional Background Have a Minimal Impact on Volunteer Citizen Science Performance in a Land-Cover Classification Task
Remote Sens. 2016, 8(9), 774; doi:10.3390/rs8090774
Received: 29 July 2016 / Revised: 2 September 2016 / Accepted: 12 September 2016 / Published: 20 September 2016
Cited by 2 | PDF Full-text (845 KB) | HTML Full-text | XML Full-text
Abstract
The idea that closer things are more related than distant things, known as ‘Tobler’s first law of geography’, is fundamental to understanding many spatial processes. If this concept applies to volunteered geographic information (VGI), it could help to efficiently allocate tasks in citizen
[...] Read more.
The idea that closer things are more related than distant things, known as ‘Tobler’s first law of geography’, is fundamental to understanding many spatial processes. If this concept applies to volunteered geographic information (VGI), it could help to efficiently allocate tasks in citizen science campaigns and help to improve the overall quality of collected data. In this paper, we use classifications of satellite imagery by volunteers from around the world to test whether local familiarity with landscapes helps their performance. Our results show that volunteers identify cropland slightly better within their home country, and do slightly worse as a function of linear distance between their home and the location represented in an image. Volunteers with a professional background in remote sensing or land cover did no better than the general population at this task, but they did not show the decline with distance that was seen among other participants. Even in a landscape where pasture is easily confused for cropland, regional residents demonstrated no advantage. Where we did find evidence for local knowledge aiding classification performance, the realized impact of this effect was tiny. Rather, the inherent difficulty of a task is a much more important predictor of volunteer performance. These findings suggest that, at least for simple tasks, the geographical origin of VGI volunteers has little impact on their ability to complete image classifications. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Open AccessArticle Season Spotter: Using Citizen Science to Validate and Scale Plant Phenology from Near-Surface Remote Sensing
Remote Sens. 2016, 8(9), 726; doi:10.3390/rs8090726
Received: 26 July 2016 / Revised: 18 August 2016 / Accepted: 23 August 2016 / Published: 1 September 2016
Cited by 2 | PDF Full-text (9309 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The impact of a rapidly changing climate on the biosphere is an urgent area of research for mitigation policy and management. Plant phenology is a sensitive indicator of climate change and regulates the seasonality of carbon, water, and energy fluxes between the land
[...] Read more.
The impact of a rapidly changing climate on the biosphere is an urgent area of research for mitigation policy and management. Plant phenology is a sensitive indicator of climate change and regulates the seasonality of carbon, water, and energy fluxes between the land surface and the climate system, making it an important tool for studying biosphere–atmosphere interactions. To monitor plant phenology at regional and continental scales, automated near-surface cameras are being increasingly used to supplement phenology data derived from satellite imagery and data from ground-based human observers. We used imagery from a network of phenology cameras in a citizen science project called Season Spotter to investigate whether information could be derived from these images beyond standard, color-based vegetation indices. We found that engaging citizen science volunteers resulted in useful science knowledge in three ways: first, volunteers were able to detect some, but not all, reproductive phenology events, connecting landscape-level measures with field-based measures. Second, volunteers successfully demarcated individual trees in landscape imagery, facilitating scaling of vegetation indices from organism to ecosystem. And third, volunteers’ data were used to validate phenology transition dates calculated from vegetation indices and to identify potential improvements to existing algorithms to enable better biological interpretation. As a result, the use of citizen science in combination with near-surface remote sensing of phenology can be used to link ground-based phenology observations to satellite sensor data for scaling and validation. Well-designed citizen science projects targeting improved data processing and validation of remote sensing imagery hold promise for providing the data needed to address grand challenges in environmental science and Earth observation. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Open AccessArticle Mapping Presence and Predicting Phenological Status of Invasive Buffelgrass in Southern Arizona Using MODIS, Climate and Citizen Science Observation Data
Remote Sens. 2016, 8(7), 524; doi:10.3390/rs8070524
Received: 25 February 2016 / Revised: 11 May 2016 / Accepted: 31 May 2016 / Published: 24 June 2016
Cited by 1 | PDF Full-text (6387 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The increasing spread and abundance of an invasive perennial grass, buffelgrass (Pennisetum ciliare), represents a critical threat to the native vegetation communities of the Sonoran desert in southern Arizona, USA, where buffelgrass eradication is a high priority for resource managers. Herbicidal
[...] Read more.
The increasing spread and abundance of an invasive perennial grass, buffelgrass (Pennisetum ciliare), represents a critical threat to the native vegetation communities of the Sonoran desert in southern Arizona, USA, where buffelgrass eradication is a high priority for resource managers. Herbicidal treatment of buffelgrass is most effective when the vegetation is actively growing, but the remoteness of infestations and the erratic timing and length of the species’ growth periods confound effective treatment. The goal of our research is to promote buffelgrass management by using remote sensing data to detect where the invasive plants are located and when they are photosynthetically active. We integrated citizen scientist observations of buffelgrass phenology in the Tucson, Arizona area with PRISM precipitation data, eight-day composites of 250-m Moderate-resolution Imaging Spectroradiometer (MODIS) satellite imagery, and aerially-mapped polygons of buffelgrass presence to understand dynamics and relationships between precipitation and the timing and amount of buffelgrass greenness from 2011 to 2013. Our results show that buffelgrass responds quickly to antecedent rainfall: in pixels containing buffelgrass, higher correlations (R2 > 0.5) typically occur after two cumulative eight-day periods of rain, whereas in pixels dominated by native vegetation, four prior 8-day periods are required to reach that threshold. Using the new suite of phenometrics introduced here—Climate Landscape Response metrics—we accurately predicted the location of 49% to 55% of buffelgrass patches in Saguaro National Park. These metrics and the suggested guidelines for their use can be employed by resource managers to treat buffelgrass during optimal time periods. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Open AccessArticle Synergistic Use of Citizen Science and Remote Sensing for Continental-Scale Measurements of Forest Tree Phenology
Remote Sens. 2016, 8(6), 502; doi:10.3390/rs8060502
Received: 30 December 2015 / Revised: 17 May 2016 / Accepted: 31 May 2016 / Published: 14 June 2016
Cited by 4 | PDF Full-text (1604 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
There is great potential value in linking geographically dispersed multitemporal observations collected by lay volunteers (or “citizen scientists”) with remotely-sensed observations of plant phenology, which are recognized as useful indicators of climate change. However, challenges include a large mismatch in spatial scale and
[...] Read more.
There is great potential value in linking geographically dispersed multitemporal observations collected by lay volunteers (or “citizen scientists”) with remotely-sensed observations of plant phenology, which are recognized as useful indicators of climate change. However, challenges include a large mismatch in spatial scale and diverse sources of uncertainty in the two measurement types. These challenges must be overcome if the data from each source are to be compared and jointly used to understand spatial and temporal variation in phenology, or if remote observations are to be used to predict ground-based observations. We investigated the correlation between land surface phenology derived from Moderate Resolution Imaging Spectrometer (MODIS) data and citizen scientists’ phenology observations from the USA National Phenology Network (NPN). The volunteer observations spanned 2004 to 2013 and represented 25 plant species and nine phenophases. We developed quality control procedures that removed observations outside of an a priori determined acceptable period and observations that were made more than 10 days after a preceding observation. We found that these two quality control steps improved the correlation between ground- and remote-observations, but the largest improvement was achieved when the analysis was restricted to forested MODIS pixels. These results demonstrate a high degree of correlation between the phenology of individual trees (particularly dominant forest trees such as quaking aspen, white oak, and American beech) and the phenology of the surrounding forested landscape. These results provide helpful guidelines for the joint use of citizen scientists’ observations and remote sensing phenology in work aimed at understanding continental scale variation and temporal trends. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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Review

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Open AccessReview Citizen Observatories and the New Earth Observation Science
Remote Sens. 2017, 9(2), 153; doi:10.3390/rs9020153
Received: 2 August 2016 / Revised: 29 January 2017 / Accepted: 30 January 2017 / Published: 15 February 2017
Cited by 1 | PDF Full-text (1362 KB) | HTML Full-text | XML Full-text
Abstract
Earth observation is diversifying, and now includes new types of systems, such as citizen observatories, unmanned aerial vehicles and wireless sensor networks. However, the Copernicus Programme vision of a seamless chain from satellite data to usable information in the hands of decision makers
[...] Read more.
Earth observation is diversifying, and now includes new types of systems, such as citizen observatories, unmanned aerial vehicles and wireless sensor networks. However, the Copernicus Programme vision of a seamless chain from satellite data to usable information in the hands of decision makers is still largely unrealized, and remote sensing science lacks a conceptual framework to explain why. This paper reviews the literatures on citizen science, citizen observatories and conceptualization of remote sensing systems. It then proposes a Conceptual Framework for Earth Observation which can be used in a new Earth observation science to explain blockages in the chain from collecting data to disseminating information in any Earth observation system, including remote sensing systems. The framework differs from its predecessors by including social variables as well as technological and natural ones. It is used here, with evidence from successful citizen science projects, to compare the factors that are likely to influence the effectiveness of satellite remote sensing systems and citizen observatories. The paper finds that constraints on achieving the seamless “Copernicus Chain” are not solely technical, as assumed in the new Space Strategy for Europe, but include social constraints too. Achieving the Copernicus Chain will depend on the balance between: (a) the ‘forward’ momentum generated by the repetitive functioning of each component in the system, as a result of automatic operation or human institutions, and by the efficiency of interfaces between components; and (b) the ‘backward’ flow of information on the information needs of end users. Citizen observatories will face challenges in components which for satellite remote sensing systems are: (a) automatic or straightforward, e.g., sensor design and launch, data collection, and data products; and (b) also challenging, e.g., data processing. Since citizen observatories will rely even more on human institutions than remote sensing systems to achieve repetitive functioning, one of their greatest strengths—using a “crowd” of hand-held sensors to cover large areas—could also be one of their greatest weaknesses. Full article
(This article belongs to the Special Issue Citizen Science and Earth Observation)
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