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On How Crowdsourced Data and Landscape Organisation Metrics Can Facilitate the Mapping of Cultural Ecosystem Services: An Estonian Case Study

Author

Listed:
  • Oleksandr Karasov

    (Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, 51006 Tartu, Estonia)

  • Stien Heremans

    (Research Institute for Nature and Forest (INBO), 1000 Brussels, Belgium)

  • Mart Külvik

    (Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, 51006 Tartu, Estonia)

  • Artem Domnich

    (Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia)

  • Igor Chervanyov

    (Physical Geography and Cartography Department, School of Geology, Geography, Recreation and Tourism, V. N. Karazin Kharkiv National University, 61022 Kharkiv, Ukraine)

Abstract

Social media continues to grow, permanently capturing our digital footprint in the form of texts, photographs, and videos, thereby reflecting our daily lives. Therefore, recent studies are increasingly recognising passively crowdsourced geotagged photographs retrieved from location-based social media as suitable data for quantitative mapping and assessment of cultural ecosystem service (CES) flow. In this study, we attempt to improve CES mapping from geotagged photographs by combining natural language processing, i.e., topic modelling and automated machine learning classification. Our study focuses on three main groups of CESs that are abundant in outdoor social media data: landscape watching, active outdoor recreation, and wildlife watching. Moreover, by means of a comparative viewshed analysis, we compare the geographic information system- and remote sensing-based landscape organisation metrics related to landscape coherence and colour harmony. We observed the spatial distribution of CESs in Estonia and confirmed that colour harmony indices are more strongly associated with landscape watching and outdoor recreation, while landscape coherence is more associated with wildlife watching. Both CES use and values of landscape organisation indices are land cover-specific. The suggested methodology can significantly improve the state-of-the-art with regard to CES mapping from geotagged photographs, and it is therefore particularly relevant for monitoring landscape sustainability.

Suggested Citation

  • Oleksandr Karasov & Stien Heremans & Mart Külvik & Artem Domnich & Igor Chervanyov, 2020. "On How Crowdsourced Data and Landscape Organisation Metrics Can Facilitate the Mapping of Cultural Ecosystem Services: An Estonian Case Study," Land, MDPI, vol. 9(5), pages 1-17, May.
    • Handle: RePEc:gam:jlands:v:9:y:2020:i:5:p:158-:d:359898
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    References listed on IDEAS

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    9. Gosal, Arjan S. & Geijzendorffer, Ilse R. & Václavík, Tomáš & Poulin, Brigitte & Ziv, Guy, 2019. "Using social media, machine learning and natural language processing to map multiple recreational beneficiaries," Ecosystem Services, Elsevier, vol. 38(C), pages 1-1.
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    Cited by:

    1. Nicolas Marine & Cecilia Arnaiz-Schmitz & Luis Santos-Cid & María F. Schmitz, 2022. "Can We Foresee Landscape Interest? Maximum Entropy Applied to Social Media Photographs: A Case Study in Madrid," Land, MDPI, vol. 11(5), pages 1-13, May.
    2. Víctor García-Díez & Marina García-Llorente & José A. González, 2020. "Participatory Mapping of Cultural Ecosystem Services in Madrid: Insights for Landscape Planning," Land, MDPI, vol. 9(8), pages 1-15, July.
    3. Xiao Zhang & Jun Wang & Mingyue Zhao & Yan Gao & Yanxu Liu, 2023. "Variations of Ecosystem Services Supply and Demand on the Southeast Hilly Area of China: Implications for Ecosystem Protection and Restoration Management," Land, MDPI, vol. 12(4), pages 1-25, March.
    4. Lingua, Federico & Coops, Nicholas C. & Griess, Verena C., 2022. "Valuing cultural ecosystem services combining deep learning and benefit transfer approach," Ecosystem Services, Elsevier, vol. 58(C).
    5. Depietri, Yaella & Ghermandi, Andrea & Campisi-Pinto, Salvatore & Orenstein, Daniel E., 2021. "Public participation GIS versus geolocated social media data to assess urban cultural ecosystem services: Instances of complementarity," Ecosystem Services, Elsevier, vol. 50(C).
    6. Edgars Jūrmalis & Arta Bārdule & Jānis Donis & Linda Gerra-Inohosa & Zane Lībiete, 2023. "Forest Inventory Data Provide Useful Information for Mapping Ecosystem Services Potential," Land, MDPI, vol. 12(10), pages 1-19, September.
    7. Karasov, Oleksandr & Heremans, Stien & Külvik, Mart & Domnich, Artem & Burdun, Iuliia & Kull, Ain & Helm, Aveliina & Uuemaa, Evelyn, 2022. "Beyond land cover: How integrated remote sensing and social media data analysis facilitates assessment of cultural ecosystem services," Ecosystem Services, Elsevier, vol. 53(C).
    8. Szymon Chmielewski, 2020. "Chaos in Motion: Measuring Visual Pollution with Tangential View Landscape Metrics," Land, MDPI, vol. 9(12), pages 1-21, December.

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