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

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Institute of Agricultural and Environmental Sciences, Estonian University of Life Sciences, 51006 Tartu, Estonia
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Research Institute for Nature and Forest (INBO), 1000 Brussels, Belgium
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Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia
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Physical Geography and Cartography Department, School of Geology, Geography, Recreation and Tourism, V. N. Karazin Kharkiv National University, 61022 Kharkiv, Ukraine
*
Author to whom correspondence should be addressed.
Land 2020, 9(5), 158; https://doi.org/10.3390/land9050158
Received: 9 April 2020 / Revised: 4 May 2020 / Accepted: 14 May 2020 / Published: 19 May 2020
(This article belongs to the Section Landscape Ecology)
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. View Full-Text
Keywords: cultural ecosystem services; automated image recognition; natural language processing; topic modelling; landscape coherence; colour harmony cultural ecosystem services; automated image recognition; natural language processing; topic modelling; landscape coherence; colour harmony
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Karasov, O.; Heremans, S.; Külvik, M.; Domnich, A.; Chervanyov, I. On How Crowdsourced Data and Landscape Organisation Metrics Can Facilitate the Mapping of Cultural Ecosystem Services: An Estonian Case Study. Land 2020, 9, 158.

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