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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.

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