Visitor Counting and Monitoring in Forests Using Camera Traps: A Case Study from Bavaria (Southern Germany)

Abstract

A variety of counting methods exist to analyze visitor numbers of outdoor settings such as national parks, recreation areas and urban green spaces, with sensor-based approaches being the most frequently applied. In this paper, we describe the application and practicality of camera traps originally designed for wildlife monitoring for visitor management purposes. The focus of the work is on the practicality of trigger camera traps and data collection for visitor monitoring from a more practice- and management-oriented perspective. Camera traps can provide interesting in-depth and detailed information about recreationists and are flexible and suitable for various uses; however, assessing the visual data manually requires significant staff and working time. To deal with the large amounts of data gathered for numbers of passersby and recreation activities, correlation factors between passersby and pictures were determined, so that the number of passersby related to the number of pictures taken per day or per other time unit could be established. In focusing on using the camera traps and assessing the generated data, it became clear that more studies have to be conducted to compare different methods of visitor monitoring and their accuracy in different outdoor environments.

Automated visitor and wildlife monitoring with camera traps and machine learning

“…For example, if hiking and biking are the most common activities, a differentiation into hiker and biker is possible as detected vehicles and persons on the same image should refer to biking while images with exclusively persons being detected should represent hikers. This is a strong advantage of using camera traps and automated object detection for investigating recreational activities in natural systems (Lupp et al., 2021), since other methods used for generating large spatiotemporal data of human activities such as pressure sensors or infrared sensors are limited in differentiating between activities (Cessford & Muhar, 2003; Staab et al., 2021). However, depending on the common human activity and the proximity of cameras to human settlements, images with detections of a person plus vehicle may need to be further investigated, since also objects such as buggies and wheelchairs were classified as person plus vehicle.…”

“…Particularly studies in recreation ecology seeking to understand complex interactions of species and humans in space and time, would benefit from such an approach as large spatiotemporal data from wildlife and human activities can be analysed simultaneously. So far, assessments of human–wildlife interactions using camera traps were limited by data protection regulations, that is, personal rights, and costly manual classification processes (Lupp et al., 2021; Miller et al., 2017; Reilly et al., 2017). Using automated object detection would significantly reduce temporal and financial efforts in this field and additionally facilitate the compliance with data protection regulations.…”

Nature-Based Tourism in National and Natural Parks in Europe: A Systematic Review

“…Carvache-Franco et al [35] and Carrascosa-López et al [38] analysed the correlation between the perceived value of a touristic destination and the loyalty of tourists, or their desire to return. Monitoring visitors is also a subject of potential interest to park managers, and it is addressed by analysing various methods: camera traps [39], applications [64], and GPS tracking [73]. Kristensen et al [42] analysed tourists' motivations based on their level of experience, information that can be used for potential campsite development.…”

Visitor Use and Activities Detected Using Trail Cameras at Forest Restoration Sites

“…New innovations and technologies, from photographic methods to social media posts, continue to be developed for monitoring visitor use of forests and recreational areas. (O'Connor et al 2017, Wood et al 2020, Lupp et al 2021.…”

“…This assumption was based on a pilot study that found that the combinations of camera and camera settings we used successfully detected greater than 95% of vehicles entering or exiting a parking lot with known traffic levels (see Supplemental Materials). Lastly, the camera positioned at the one hiker-only entrance to Bluffton (i.e., Bluffton Bridge) was assumed to have an accuracy consistent with previous camera trap accuracy with monitoring visitors or large mammals (Carvalho et al 2016, Harmsen et al 2020, Lupp et al 2021, Staab et al 2021.…”