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Abstract The use of recreational ecosystem services is highly dependent on the surrounding environmental and climate conditions. Due to this dependency, future recreational opportunities provided by nature are at risk from climate change. To understand how climate change will impact recreation we need to understand current recreational patterns, but traditional data is limited and low resolution. Fortunately, social media data presents an opportunity to overcome those data limitations and machine learning offers a tool to effectively use that big data. We use data from the social media site Flickr as a proxy for recreational visitation and random forest to model the relationships between social, environmental, and climate factors and recreation for the peak season (summer) in California. We then use the model to project how non-urban recreation will change as the climate changes. Our model shows that current patterns are exacerbated in the future under climate change, with currently popular summer recreation areas becoming more suitable and unpopular summer recreation areas becoming less suitable for recreation. Our model results have land management implications as recreation regions that see high visitation consequently experience impacts to surrounding ecosystems, ecosystem services, and infrastructure. This information can be used to include climate change impacts into land management plans to more effectively provide sustainable nature recreation opportunities for current and future generations. Furthermore, our study demonstrates that crowdsourced data and machine learning offer opportunities to better integrate socio-ecological systems into climate impacts research and more holistically understand climate change impacts to human well-being.
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Does the Past Influence the Present or the Future? Deep Time, Land Use, and Remote Sensing in Southern Mexico
This paper reports the initial results of a pilot study investigating the relationships among long-term land use, settlements, historic population, and their potential influence for understanding and evaluating current and future land use. Most of our work to date has been focused on evaluating chang- ing patterns of historic settlement and its relationship to what we know about the historic environment and landscape. Here, we instead rely on remotely-sensed big data as a first step to see how patterns of past land use are correlated with what we know about current land use and land cover. The pilot study initiates a broader research agenda that better incorporates what we know about past landscapes into contemporary land use decisions and to offer critical insights into how the future could be shaped by integrating information about the past. As a first step, the analysis is intentionally broad so that our next steps can provide the fidelity and resolution to offer place based information for design and planning. Nevertheless, it offers a unique window of perception into current land use and a platform for operationalizing evolutionary uses of the past for better managing, designing, and planning complex land systems and moving beyond analogic uses.
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- Award ID(s):
- 1849921
- PAR ID:
- 10510505
- Publisher / Repository:
- Wichmann
- Date Published:
- Journal Name:
- Journal of digital landscape architecture
- Issue:
- 8
- ISSN:
- 2511-624X
- Page Range / eLocation ID:
- 75-83
- Subject(s) / Keyword(s):
- GIS Remote Sensing Archaeology Land Use Landscape
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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