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Land
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Citizen Science and Crowdsourcing for Land Use, Land Cover and...
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Citizen Science and Crowdsourcing for Land Use, Land Cover and Change Detection

A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: closed (31 May 2019) | Viewed by 30928

Special Issue Editors

Dr. Linda See

E-Mail Website
Guest Editor
International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, A-2361 Laxenburg, Austria
Interests: citizen science, crowdsourcing and volunteered geographic information (data collection, quality assessment, creating added value products with VGI, motivation and engagement, etc.); land cover/land use validation; creation of hybrid land cover products; serious gaming; sustainable development goals (SDGs)
Special Issues, Collections and Topics in MDPI journals
Dr. Cidalia C Fonte

E-Mail Website
Guest Editor
Institute for Systems and Computers Engineering at Coimbra, Department of Mathematics, University of Coimbra, 3001-501 Coimbra, Portugal
Interests: spatial data validation and quality assessment; land use land cover mapping; volunteered geographic information; spatial data integration; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

There are an increasing number of citizen science projects and crowdsourcing applications emerging in the field of land use, land cover, and change detection, e.g., Geo-Wiki, LACO-Wiki and citizen observatories (e.g., LandSense, groundtruth2.0, scent and GROW). Visual interpretation of very high resolution satellite imagery from Google Earth and Bing provide a valuable source of training data for classification algorithms, as well as reference datasets for validation of land use, land cover, and change over time. OpenStreetMap (OSM) is a successful example of citizen-based mapping of the world, which is very rich in detail and contains land-use information that is difficult to map using remote sensing alone. OSM can also be converted to land use and land cover maps, with new applications emerging, e.g., the OSM Land Use and Land Cover application developed at the University of Heidelberg and new tools for conversion of OSM to land use and land cover developed at the University of Coimbra. Additionally, due to the continuous updates to OSM, this product can be used for change detection, particularly in urban areas. Disaster response is greatly aided by volunteers through mapping affected areas, recognition of damaged areas from satellite or drone imagery, and manual filtering of tweets. Citizens are becoming an integral part of land-monitoring systems via citizen science and crowdsourcing activities. This Special Issue aims to bring together state-of-the-art research in this field.

We invite papers on any aspect of citizen science and crowdsourcing related to the development and validation of land use and land cover maps, or for change detection of land use and land cover. We will also consider disaster-related topics if they are related to the mapping of land cover or land use. Papers on data quality arising from citizen-contributed data in this field are also welcome, as are papers that consider project design, data standards, interoperability, data privacy, and motivational aspects of participation, among other topics relevant to the overarching theme of this Special Issue.

Dr. Linda See
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 submissions that pass pre-check are 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. Land 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 2600 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.

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Published Papers (4 papers)

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Research

18 pages, 2673 KiB  
Article
Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App
by Juan Carlos Laso Bayas, Linda See, Hedwig Bartl, Tobias Sturn, Mathias Karner, Dilek Fraisl, Inian Moorthy, Michaela Busch, Marijn van der Velde and Steffen Fritz
Land 2020, 9(11), 446; https://doi.org/10.3390/land9110446 - 15 Nov 2020
Cited by 21 | Viewed by 4881
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Citizen Science and Crowdsourcing for Land Use, Land Cover and Change Detection)
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26 pages, 18579 KiB  
Article
Crowdsourced Street-Level Imagery as a Potential Source of In-Situ Data for Crop Monitoring
by Raphaël D'Andrimont, Momchil Yordanov, Guido Lemoine, Janine Yoong, Kamil Nikel and Marijn Van der Velde
Land 2018, 7(4), 127; https://doi.org/10.3390/land7040127 - 22 Oct 2018
Cited by 24 | Viewed by 6933
Abstract
New approaches to collect in-situ data are needed to complement the high spatial (10 m) and temporal (5 d) resolution of Copernicus Sentinel satellite observations. Making sense of Sentinel observations requires high quality and timely in-situ data for training and validation. Classical ground [...] Read more.
New approaches to collect in-situ data are needed to complement the high spatial (10 m) and temporal (5 d) resolution of Copernicus Sentinel satellite observations. Making sense of Sentinel observations requires high quality and timely in-situ data for training and validation. Classical ground truth collection is expensive, lacks scale, fails to exploit opportunities for automation, and is prone to sampling error. Here we evaluate the potential contribution of opportunistically exploiting crowdsourced street-level imagery to collect massive high-quality in-situ data in the context of crop monitoring. This study assesses this potential by answering two questions: (1) what is the spatial availability of these images across the European Union (EU), and (2) can these images be transformed to useful data? To answer the first question, we evaluated the EU availability of street-level images on Mapillary—the largest open-access platform for such images—against the Land Use and land Cover Area frame Survey (LUCAS) 2018, a systematic surveyed sampling of 337,031 points. For 37.78% of the LUCAS points a crowdsourced image is available within a 2 km buffer, with a mean distance of 816.11 m. We estimate that 9.44% of the EU territory has a crowdsourced image within 300 m from a LUCAS point, illustrating the huge potential of crowdsourcing as a complementary sampling tool. After artificial and built up (63.14%), and inland water (43.67%) land cover classes, arable land has the highest availability at 40.78%. To answer the second question, we focus on identifying crops at parcel level using all 13.6 million Mapillary images collected in the Netherlands. Only 1.9% of the contributors generated 75.15% of the images. A procedure was developed to select and harvest the pictures potentially best suited to identify crops using the geometries of 785,710 Dutch parcels and the pictures’ meta-data such as camera orientation and focal length. Availability of crowdsourced imagery looking at parcels was assessed for eight different crop groups with the 2017 parcel level declarations. Parcel revisits during the growing season allowed to track crop growth. Examples illustrate the capacity to recognize crops and their phenological development on crowdsourced street-level imagery. Consecutive images taken during the same capture track allow selecting the image with the best unobstructed view. In the future, dedicated crop capture tasks can improve image quality and expand coverage in rural areas. Full article
(This article belongs to the Special Issue Citizen Science and Crowdsourcing for Land Use, Land Cover and Change Detection)
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18 pages, 41629 KiB  
Communication
Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data
by Myroslava Lesiv, Linda See, Juan Carlos Laso Bayas, Tobias Sturn, Dmitry Schepaschenko, Mathias Karner, Inian Moorthy, Ian McCallum and Steffen Fritz
Land 2018, 7(4), 118; https://doi.org/10.3390/land7040118 - 11 Oct 2018
Cited by 66 | Viewed by 12011
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Citizen Science and Crowdsourcing for Land Use, Land Cover and Change Detection)
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18 pages, 8086 KiB  
Article
An Experimental Framework for Integrating Citizen and Community Science into Land Cover, Land Use, and Land Change Detection Processes in a National Mapping Agency
by Ana-Maria Olteanu-Raimond, Laurence Jolivet, Marie-Dominque Van Damme, Timothée Royer, Ludovic Fraval, Linda See, Tobias Sturn, Mathias Karner, Inian Moorthy and Steffen Fritz
Land 2018, 7(3), 103; https://doi.org/10.3390/land7030103 - 4 Sep 2018
Cited by 10 | Viewed by 5612
Abstract
Accurate and up-to-date information on land use and land cover (LULC) is needed to develop policies on reducing soil sealing through increased urbanization as well as to meet climate targets. More detailed information about building function is also required but is currently lacking. [...] Read more.
Accurate and up-to-date information on land use and land cover (LULC) is needed to develop policies on reducing soil sealing through increased urbanization as well as to meet climate targets. More detailed information about building function is also required but is currently lacking. To improve these datasets, the national mapping agency of France, Institut de l’Information Géographique et Foréstière (IGN France), has developed a strategy for updating their LULC database on a update cycle every three years and building information on a continuous cycle using web, mobile, and wiki applications. Developed as part of the LandSense project and eventually tapping into the LandSense federated authentication system, this paper outlines the data collection campaigns, the key concepts that have driven the system architecture, and a description of the technologies developed for this solution. The campaigns have only just begun, so there are only preliminary results to date. Thus far, feedback on the web and mobile applications has been positive, but still requires a further demonstration of feasibility. Full article
(This article belongs to the Special Issue Citizen Science and Crowdsourcing for Land Use, Land Cover and Change Detection)
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