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Fritz S, See L, Perger C, et al. A global dataset of crowdsourced land cover and land use reference data. Sci Data. 2017;4:170075doi: 10.1038/sdata.2017.75.
Fritz, S., See, L., Perger, C., McCallum, I., Schill, C., Schepaschenko, D., Duerauer, M., Karner, M., Dresel, C., Laso-Bayas, J. C., Lesiv, M., Moorthy, I., Salk, C. F., Danylo, O., Sturn, T., Albrecht, F., You, L., Kraxner, F., & Obersteiner, M. (2017). A global dataset of crowdsourced land cover and land use reference data. Scientific data, 4170075. https://doi.org/10.1038/sdata.2017.75
Fritz, Steffen, et al. "A global dataset of crowdsourced land cover and land use reference data." Scientific data vol. 4 (2017): 170075. doi: https://doi.org/10.1038/sdata.2017.75
Fritz S, See L, Perger C, McCallum I, Schill C, Schepaschenko D, Duerauer M, Karner M, Dresel C, Laso-Bayas JC, Lesiv M, Moorthy I, Salk CF, Danylo O, Sturn T, Albrecht F, You L, Kraxner F, Obersteiner M. A global dataset of crowdsourced land cover and land use reference data. Sci Data. 2017 Jun 13;4:170075. doi: 10.1038/sdata.2017.75. PMID: 28608851; PMCID: PMC5469313.
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Sci Data. 2017 Jun 13;4:170075. doi: 10.1038/sdata.2017.75.

A global dataset of crowdsourced land cover and land use reference data.

Scientific data

Steffen Fritz, Linda See, Christoph Perger, Ian McCallum, Christian Schill, Dmitry Schepaschenko, Martina Duerauer, Mathias Karner, Christopher Dresel, Juan-Carlos Laso-Bayas, Myroslava Lesiv, Inian Moorthy, Carl F Salk, Olha Danylo, Tobias Sturn, Franziska Albrecht, Liangzhi You, Florian Kraxner, Michael Obersteiner

Affiliations

  1. Ecosystems Services and Management Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg A-2361, Austria.
  2. FeLis, Albert Ludwigs University of Freiburg, Freiburg D-79106, Germany.
  3. Southern Swedish Forest Research Center, Swedish University of Agricultural Sciences, Alnarp SE-230 53, Sweden.
  4. GeoVille Information Systems GmbH, Innsbruck A-6020, Austria.
  5. Environment and Production Technology, International Food Policy Research Institute (IFPRI), Washington, District Of Columbia 20005, USA.
  6. Key Laboratory of Agri-informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China.

PMID: 28608851 PMCID: PMC5469313 DOI: 10.1038/sdata.2017.75

Abstract

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general.

References

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