The paper presents a methodology to quantify the suitability of forest stands for the potential delivery of cultural ecosystem services (CES). The quantification of CES represents a complicated task in the framework of ecosystem service valuation. Compared to traditional investigations, focusing on the study of the aesthetic appreciation of a particular territory, the use of geotagged photographs seems to be a promising alternative to appraise CES. Thus, in order to analyse CES with a particular focus on the aesthetic appreciation of forest stands, this study exploits big data through the analysis of photos shared on the Flickr social network. Crowdsourced datasets are used to depict the geographic location and density of pictures - expressed as the number of photos per unit of surface - as well as their relationship to forest variables and logistic characteristics. The implemented geostatistical model is used to spatialise the results at the regional level (Tuscany forests, Italy). Among the outputs, high values of CES are stressed for high forest and protected areas. From a forest species viewpoint, silver fir, coastal Mediterranean pine, beech and mixed forests seem to be more appreciated compared to other stand typologies such as oaks (e.g., pubescent or Turkey oak) and thermophilic broad-leaved species. Additional quantitative parameters (e.g., elevation, biomass stock and distance to main roads) were significant to the CES assessment. The potential applications of the technique to support forest planning and management are discussed.
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Citation
Bernetti I, Chirici G, Sacchelli S (2019). Big data and evaluation of cultural ecosystem services: an analysis based on geotagged photographs from social media in Tuscan forest (Italy). iForest 12: 98-105. - doi: 10.3832/ifor2821-011
Academic Editor
Susanna Nocentini
Paper history
Received: Apr 19, 2018
Accepted: Nov 22, 2018
First online: Feb 11, 2019
Publication Date: Feb 28, 2019
Publication Time: 2.70 months
© SISEF - The Italian Society of Silviculture and Forest Ecology 2019
Open Access
This article is distributed under the terms of the Creative Commons Attribution-Non Commercial 4.0 International (https://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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