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Snapshots of Nature-Based Recreation Across Rural Landscapes: Insights from Geotagged Photographs in the Northeastern United States

Environmental Management volume 71, pages 234–248 (2023)Cite this article

Abstract

Nature-based recreation is of increasing economic importance to rural communities transitioning away from traditional natural resource extraction. Rural areas are rich in cultural ecosystem services (CES) that function as essential public goods, providing benefits to livelihoods and fostering conservation of large landscapes. Geotagged photographs from social media platforms capture detailed information on public use of natural areas that can be useful for stakeholders interested in promoting resilient social–ecological systems, but applications of such “big data” are often limited in either spatiotemporal or thematic scope. We integrated multiple aspects of crowdsourced image data to better understand human-environment interactions, focusing on the Northern Forest, a culturally and ecologically significant region of the northeastern United States. Images for the region were mined from Flickr, a crowdsourcing image platform, from 2012 to 2017 and assigned themes via a neural network-cluster analysis pipeline while geographic drivers of nature-based visitor engagement were assessed with random forest models to predict use of CES across the Northern Forest. Daily and seasonal patterns in photography were broadly consistent throughout the region, whereas annual photography trends were more variable. Eleven core themes were identified in images, with 70% of photographs depicting activities related to CES. Visitor engagement with nature was greater near roads and shorelines, and at higher elevations, reflecting tensions between accessibility and aesthetics for recreation. Automated image classification tools can rapidly extract relevant information from crowdsourced photography for exploring human-environment interactions, but researchers should consider multiple spatial and temporal scales in CES assessments of large social-ecological systems.

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Acknowledgements

We are grateful to James Gibbs for providing important guidance and feedback throughout this project. We thank Alden Sampson and Marshall Moutenot of Upstream Tech for accessing and preparing the image data used in this study, as well as Heera Lee and Bumsuk Seo, for their valuable input on the analysis in this study. We also thank the many students at SUNY ESF who participated in validation exercises. This project was supported by the Northeastern States Research Cooperative, with funding provided by the USDA Forest Service. The conclusions and opinions in this article are those of the authors and not of the NSRC, the Forest Service, or the USDA.

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Authors and Affiliations

  1. Department of Wildlife, Fisheries, and Conservation Biology, University of Maine, Orono, ME, USA

    Harrison Goldspiel

  2. Department of Environmental Biology, State University of New York College of Environmental Science and Forestry, Syracuse, NY, USA

    Brannon Barr

  3. Department of Forest and Natural Resources Management, State University of New York College of Environmental Science and Forestry, Syracuse, NY, USA

    Joshua Badding & Diane Kuehn

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  1. Harrison Goldspiel
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  2. Brannon Barr
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  3. Joshua Badding
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  4. Diane Kuehn
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Contributions

HG and DK conceived and the designed the study. All authors contributed to data collection, BB and JB performed validation work, and HG and DK led the analysis. The first draft of the manuscript was written by HG and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Harrison Goldspiel.

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Goldspiel, H., Barr, B., Badding, J. et al. Snapshots of Nature-Based Recreation Across Rural Landscapes: Insights from Geotagged Photographs in the Northeastern United States. Environmental Management 71, 234–248 (2023). https://doi.org/10.1007/s00267-022-01728-2

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  • DOI: https://doi.org/10.1007/s00267-022-01728-2

Keywords

  • Content analysis
  • Cultural ecosystem services
  • Flickr
  • Machine learning
  • Social media
  • Image classification