Summary
This research project aimed to explore remote monitoring approaches to better understand visitor pressure and recreational value of woodlands and provide additional evidence for the threats, social capital and landscape data being gathered through the National Forest Inventory (NFI). The project trialled different approaches for monitoring woodland recreation, including remote passive sensors, social media and mobile phone data to determine the most useful and cost-effective approach to gather data on social recreation visits to forests in England. The sampling framework was aligned with other NCEA biodiversity monitoring projects (bats, soils, eDNA) to allow further exploration of recreational impacts on biodiversity.
Research Objectives
There is a clear policy need to better understand how woodlands are used and how recreational activities interact with ecosystem health. Key evidence gaps include the cumulative ecological effects of increasing visitor numbers, how visitor use varies across space and time, and how effective different management approaches are at reducing negative impacts.
Evidence of the impacts of recreation on woodlands is limited and mixed. Much existing research focusses forest loss and degradation linked to development, rather pressures arising specifically from recreation (Clements & Ambrose-Oji, 2023). In particular, we lack detailed data linking different visitor types and activities to specific environmental outcomes.
This project aimed to explore different approaches and data sources to monitor visitor pressure and recreational value in our woodlands. Our objective was to generate robust, quantitative evidence on woodland recreation that adds value to existing woodland surveys conducted as part of the National Forest Inventory (NFI). The project also aimed to identify a recommended approach for large scale woodland recreation monitoring, alongside its policy and management implications.
We drew on a range of data sources, including:
- People counters
- Mobile phone detectors
- Strava data
- Social media data
This data also supported work within the NCEA Bats project, delivered in collaboration the Bat Conservation Trust (BCT). AudioMoth acoustic recorders were used to develop acoustic indices to monitor different elements of the soundscape, including human-made noise (anthropophony) and wildlife sounds (biophony). These data will enable us to explore how recreational activity influences woodland soundscapes and biodiversity.
Findings and Recommendations
Key findings
- Recreation is driven by site location and access, not woodland characteristics
- Distance to the nearest built‑up area was the only significant predictor of visitor numbers. Visits fell by ~29% per kilometre away from populated areas.
- Woodland size and type (broadleaved, conifer, mixed) showed no significant effect on visit rates.
- Despite the importance of proximity to built-up areas, visitor numbers varied considerably between sites, indicating that a substantial proportion of variation in recreational use is influenced by factors not captured in the current model.
- Visitor patterns show clear seasonal, weekly, and daily rhythms
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- Spring and summer were the busiest seasons; winter was quietest.
- Weekends, especially Friday–Monday, saw the highest use.
- Peak activity occurred 11:00–14:00, with extended use on lighter summer evenings.
- Visitor numbers vary widely between sites
- Annual estimated visits ranged from ~35 to >84,000 per site.
- Differences between sites could not be fully explained by woodland characteristics alone, and are likely influenced by local context, such as accessibility, parking, paths, transport links, and surrounding populations.
- Monitoring technologies provide valuable insights but require maintenance. A combined approach of methodologies provides the most robust and cost-effective data.
- Infra‑red counters provided robust, continuous data but suffered losses from theft, vandalism and weather damage.
- Additional user-generated data (e.g., from Strava Metro) can provide behavioural detail but these are biased towards active users.
Policy and management implications
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- Woodlands near urban areas have the highest visitor pressure. These sites should be prioritised for infrastructure provision, maintenance, signage, and regular monitoring.
- Predictable temporal patterns can be used to plan management operations. For example, allocating resources to spring/summer and weekends, or scheduling conservation work during quiet mid‑week and off‑season periods.
- Robust monitoring requires a combined approach with continued investment in maintaining a sensor network. Counters provide valuable baseline data that can be complemented with additional surveys or data from Strava Metro, but adequate resources to ensure continuous and representative coverage is essential.
Further research can strengthen and extend these findings:
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- Enhance modelling by incorporating additional variables such as weather, local population characteristics, transport links, access points, and path density to improve prediction of visitor pressure. Although distance to built-up areas was a significant predictor of visitor numbers, the current model left substantial variation unexplained. Including these additional drivers of recreation is likely to improve model precision and strengthen predictions of visitor pressure.
- Link recreational pressure data with NCEA biodiversity monitoring (bats, soils, eDNA) to assess ecological impacts and inform evidence‑based zoning and protection measures.
Latest Update
Funding & Partners
- Funded by the UK Government through Defra’s Natural Capital and Ecosystem Assessment programme (NCEA)
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