Blue Water Visitor Monitoring Potential: A Literature Review and Alternative Proposal

This review presents a summary of existing visitor monitoring methods and relevant studies in land and marine-based areas, with a focus on the application to unique aquatic settings. Various opportunities and challenges exist with respect to the use of each method in different marine settings. These methods differ in terms of the complexity, costs, level of accuracy, and detailed information they provide. Furthermore, the feasibility of applying these methods also depends on the site attributes of a marine area. Since each marine area varies in geographical scale and environmental and social conditions, some methods will be more appropriate or perform more successfully than others in a particular location. Therefore, the consideration of these methods should be part of a proposed alternative process, focused on adaptive monitoring that scales to address visitor ebbs and flows in these aquatic areas. The proposed alternative seeks to develop consensus around quantitative goals for visitor monitoring and estimating techniques in marine settings, using a customizable mix of methods and techniques. This alternative effort progresses to subsequent tasks and discussions, and recommendations are made considering the feasibility and confidence of using these methods in particular marine settings and future pilot sites.

[1]  Stanley J. Zarnoch,et al.  The effectiveness of visitation proxy variables in improving recreation use estimates for the USDA Forest Service , 2003 .

[2]  S. Wich,et al.  Dawn of Drone Ecology: Low-Cost Autonomous Aerial Vehicles for Conservation , 2012 .

[3]  A. Gül,et al.  An Approach for Recreation Suitability Analysis to Recreation Planning in Gölcük Nature Park , 2006, Environmental management.

[4]  Laurie J. Bauer,et al.  Recreation Use and Spatial Distribution of Use by Washington Households on the Outer Coast of Washington , 2018 .

[5]  Simon Jennings,et al.  Developing reliable, repeatable, and accessible methods to provide high-resolution estimates of fishing-effort distributions from vessel monitoring system (VMS) data , 2010 .

[6]  Ross G. Andrew,et al.  Springtime Exploitation of Brook Trout by Anglers in Remote Headwater Streams of Central Appalachia , 2019, North American Journal of Fisheries Management.

[7]  Lian Pin Koh,et al.  Drones count wildlife more accurately and precisely than humans , 2017, bioRxiv.

[8]  A. Graefe,et al.  Understanding Hunting Constraints and Negotiation Strategies: A Typology of Female Hunters , 2015 .

[9]  A. Arnberger,et al.  Social Carrying Capacity Challenges in Parks, Forests, and Protected Areas , 2010 .

[10]  J. Hubbart,et al.  Accuracy and Optimal Altitude for Physical Habitat Assessment (PHA) of Stream Environments Using Unmanned Aerial Vehicles (UAV) , 2018, Drones.

[11]  K. Lohmann,et al.  Quantifying Nearshore Sea Turtle Densities: Applications of Unmanned Aerial Systems for Population Assessments , 2017, Scientific Reports.

[12]  R. Burns,et al.  Predicting Deer Hunting Intentions Using the Theory of Planned Behavior: A Survey of Oregon Big Game Hunters , 2012 .

[13]  A. Graefe,et al.  Understanding non-traditional forest recreation: The role of constraints and negotiation strategies among racial and ethnic minorities , 2013 .

[14]  Gyan P. Nyaupane,et al.  Understanding Equity in the Recreation User Fee Context , 2007 .

[15]  W. Haider,et al.  Evaluating Visitor-Monitoring Techniques: A Comparison of Counting and Video Observation Data , 2005, Environmental management.

[16]  Richard C. Stedman,et al.  Distribution of Recreational Boating across Lakes: Do Landscape Variables Affect Recreational Use? , 2000 .

[17]  R. Burns,et al.  A profile of visitors to Brazil Amazon Protected Areas: , 2019, Marketing & Tourism Review.

[18]  Ashok Gopalarathnam,et al.  Testing and Characterization of a Fixed Wing Cross-Domain Unmanned Vehicle Operating in Aerial and Underwater Environments , 2018, IEEE Journal of Oceanic Engineering.

[19]  Greg Brown,et al.  Public Participation GIS: A new method for national park planning , 2011 .

[20]  Mari-Liis Lamp,et al.  LBS in marketing and tourism management: measuring destination loyalty with mobile positioning data , 2010, J. Locat. Based Serv..

[21]  Thomas R. Carruthers,et al.  Imputing recreational angling effort from time-lapse cameras using an hierarchical Bayesian model , 2015 .

[22]  Rein Ahas,et al.  Evaluating passive mobile positioning data for tourism surveys: An Estonian case study , 2008 .

[23]  Ishwar Dhami,et al.  Identifying and mapping forest-based ecotourism areas in West Virginia – Incorporating visitors' preferences , 2014 .

[24]  M. Sköld,et al.  Using Vessel Monitoring System Data to Improve Systematic Conservation Planning of a Multiple-Use Marine Protected Area, the Kosterhavet National Park (Sweden) , 2014, AMBIO.

[25]  A. Hodgson,et al.  Unmanned Aerial Vehicles (UAVs) for Surveying Marine Fauna: A Dugong Case Study , 2013, PloS one.

[26]  Jarrod C Hodgson,et al.  Precision wildlife monitoring using unmanned aerial vehicles , 2016, Scientific Reports.

[27]  G. Payne,et al.  Bridging the temporal gap: Continuous and cost-effective monitoring of dynamic recreational fisheries by web cameras and creel surveys , 2016 .

[28]  Spencer A Wood,et al.  Measuring recreational visitation at U.S. National Parks with crowd-sourced photographs. , 2016, Journal of environmental management.

[29]  Matthew T. J. Brownlee,et al.  GPS Visitor Tracking and Recreation Suitability Mapping: tools for understanding and managing visitor use. , 2014 .

[30]  A. Haynie,et al.  Using Vessel Monitoring System Data to Identify and Characterize Trips Made by Fishing Vessels in the United States North Pacific , 2016, PloS one.

[31]  Tarmo Virtanen,et al.  Smartphone GPS tracking—Inexpensive and efficient data collection on recreational movement , 2017 .

[32]  Andreas Muhar,et al.  Monitoring options for visitor numbers in national parks and natural areas , 2003 .

[33]  R. Manning,et al.  Analysis of the Social Carrying Capacity of a National Park Scenic Road , 2010 .

[34]  L. Alessa,et al.  Social–ecological hotspots mapping : A spatial approach for identifying coupled social–ecological space , 2008 .

[35]  Sara M. Maxwell,et al.  Pragmatic approaches for effective management of pelagic marine protected areas , 2014 .

[36]  C. Silva,et al.  Estimating forest uniformity in Eucalyptus spp. and Pinus taeda L. stands using field measurements and structure from motion point clouds generated from unmanned aerial vehicle (UAV) data collection , 2018, Forest Systems.

[37]  B. Thapa,et al.  Seasonal Spatial Activity Patterns of Visitors with a Mobile Exercise Application at Seoraksan National Park, South Korea , 2018, Sustainability.

[38]  Saul Greenberg,et al.  A Tool Supporting the Extraction of Angling Effort Data from Remote Camera Images , 2015 .

[39]  P. Newman,et al.  A visitor use monitoring approach on the Half Dome cables to reduce crowding and inform park planning decisions in Yosemite National Park , 2013 .

[40]  Linda J. Bilmes,et al.  Total Economic Valuation of the National Park Service Lands and Programs: Results of a Survey of the American Public , 2016 .

[41]  Gyan P. Nyaupane,et al.  The role of equity, trust and information on user fee acceptance in protected areas and other public lands: a structural model , 2009 .

[42]  Catherine Marina Pickering,et al.  Developing ecological indicators of visitor use of protected areas: a new integrated framework from Australia , 2009 .

[43]  M. Strager,et al.  Cicada (Magicicada) Tree Damage Detection Based on UAV Spectral and 3D Data , 2018 .

[44]  Roger Clarke,et al.  Understanding the drone epidemic , 2014, Comput. Law Secur. Rev..

[45]  Kevin Fall,et al.  Enhancing AIS to improve whale-ship collision avoidance and maritime security , 2009, OCEANS 2009.

[46]  Arne Arnberger,et al.  Visitor monitoring methods for managing public use pressures in the Danube Floodplains National Park, Austria , 2003 .

[47]  A. Aasa,et al.  Mapping changes of residence with passive mobile positioning data: the case of Estonia , 2017, Int. J. Geogr. Inf. Sci..

[48]  L. Beckley,et al.  Assessing patterns of recreational use in large marine parks: A case study from Ningaloo Marine Park, Australia , 2011 .

[49]  D. Johnston,et al.  Comparing occupied and unoccupied aircraft surveys of wildlife populations: assessing the gray seal (Halichoerus grypus) breeding colony on Muskeget Island, USA , 2017 .

[50]  Hilde M. Toonen,et al.  The digital frontiers of fisheries governance: fish attraction devices, drones and satellites , 2020 .

[51]  Giovanna Jona Lasinio,et al.  A low-cost drone based application for identifying and mapping of coastal fish nursery grounds , 2016 .

[52]  Poh-Chin Lai,et al.  An Assessment of GPS and GIS in Recreational Tracking , 2007 .

[53]  Chelsey Walden-Schreiner,et al.  Spatially Characterizing Visitor Use and Its Association with Informal Trails in Yosemite Valley Meadows , 2013, Environmental Management.

[54]  Laurent Moalic,et al.  Clustering Weekly Patterns of Human Mobility Through Mobile Phone Data , 2018, IEEE Transactions on Mobile Computing.

[55]  Charles C. Harris,et al.  Cooperative research for monitoring recreation use of the Lower Salmon River. , 1989 .

[56]  Ross G. Andrew,et al.  Conceptualizing the National marine sanctuary visitor counting process for marine protected areas , 2020 .

[57]  Jason A. Hubbart,et al.  Challenges in Aquatic Physical Habitat Assessment: Improving Conservation and Restoration Decisions for Contemporary Watersheds , 2017 .

[58]  Daniela Golinelli,et al.  System for Observing Play and Recreation in Communities (SOPARC): Reliability and Feasibility Measures. , 2006, Journal of physical activity & health.

[59]  Ian D. Bishop,et al.  Management of Recreational Areas: GIS, Autonomous Agents, and Virtual Reality , 2000 .

[60]  A. Guerry,et al.  Using social media to quantify nature-based tourism and recreation , 2013, Scientific Reports.

[61]  R. Burns,et al.  Integrating Constraints to the Theory of Planned Behavior in Predicting Deer Hunting Participation , 2016 .

[62]  Jeffrey J. Murphy,et al.  Monitoring boat-based recreational fishing effort at a nearshore artificial reef with a shore-based camera , 2016 .

[63]  Roland Bouffanais,et al.  Distributed system of autonomous buoys for scalable deployment and monitoring of large waterbodies , 2018, Auton. Robots.

[64]  Peter Newman,et al.  Estimating visitor use at attraction sites and trailheads in Yosemite National Park using automated visitor counters , 2010 .

[65]  R. Johnston,et al.  Estimating regional visitor numbers. , 2002 .

[66]  Johann Mourier,et al.  Using unmanned aerial vehicles (UAVs) to investigate shark and ray densities in a shallow coral lagoon , 2016 .

[67]  Gary L. Tyre,et al.  Instant‐count sampling: A technique for estimating recreation use in municipal settings , 1979 .

[68]  A. Arnberger,et al.  Whitewater recreationists’ preferences for social, resource and managerial attributes in the Alpine Nature and Geopark Styrian Eisenwurzen , 2017 .

[69]  Francesco Giordano,et al.  Integrating Sensors into a Marine Drone for Bathymetric 3D Surveys in Shallow Waters , 2015, Sensors.

[70]  C. Monz,et al.  GPS-based measurements of backcountry visitors in parks and protected areas : Examples of methods and applications from three , 2016 .

[71]  N. Cosco,et al.  Behavior mapping: a method for linking preschool physical activity and outdoor design. , 2010, Medicine and science in sports and exercise.