TY - JOUR
T1 - Cost benefit analysis of survey methods for assessing intertidal sediment disturbance
T2 - a bait collection case study
AU - White, Shannon M.
AU - Schaefer, Martin
AU - Barfield, Peter
AU - Cantrell, Ruth
AU - Watson, Gordon J.
N1 - Funding Information:
The authors acknowledge the financial support of Natural England (Contract Reference: ECM_51116). The authors thank staff (N. Fuller, J. Mackellar, M. Martin) of the University of Portsmouth, T. Hopez, and M. Wilson for the field support and C. Thompson for sharing details on CCO aerial photography collection. This project would not have been possible without the help of staff from the local agencies.
Funding Information:
The authors acknowledge the financial support of Natural England (Contract Reference: ECM_51116 ). The authors thank staff (N. Fuller, J. Mackellar, M. Martin) of the University of Portsmouth, T. Hopez, and M. Wilson for the field support and C. Thompson for sharing details on CCO aerial photography collection. This project would not have been possible without the help of staff from the local agencies.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/3/15
Y1 - 2022/3/15
N2 - Coastal management requires cost-effective, yet accurate, assessments of habitat condition, especially in areas protected by statutory conservation measures. Unmanned Aerial Vehicles (UAVs) provide alternatives to manned aircraft and walk-over (WO) surveys. To support coastal managers with method selection, we compare the costs and benefits of the three techniques using the extent of bait collection (sediment scarring from manual digging) on intertidal mudflats from three UK sites. UAV and WO surveys were conducted in parallel and aerial photography was downloaded from the Channel Coastal Observatory (CCO). Digging was digitised from estimations on foot (WO) or by manually labelling imagery with confidence assigned (UAV/CCO). Method efficacy is compared with respect to spatial coverage, control over survey time/location, spatial resolution, positioning accuracy, and area of digging detected. Personnel hours and up-front costs (e.g. training/equipment), costs for personnel time standardised by shore area, personnel risk, and environmental impact are also compared. Regarding efficacy, CCO imagery had extensive shore coverage compared to UAV and WO, however, assessments are restricted to times/locations with available imagery. Each method's resolution was sufficient to detect digging. WO achieved the highest resolution (on foot), but the lowest positioning accuracy, in contrast to accurate feature delineation on aerial imagery. An additive two-way ANOVA revealed a significantly higher percent area of ‘dug’ sediment (all confidence levels) recorded by UAV than WO. CCO was the most cost-effective with no fieldwork/equipment costs. UAV had the highest up-front costs, but WO was more costly for personnel hours/km2 for survey time and digitisation. For all methods, digitisation was the most time-consuming aspect. Compared to WO, UAV achieved rapid shore surveys and the CCO and UAV methods minimise personnel risks. UAV and WO both cause wildlife disturbance, with trampling an additional WO impact. With each method suited to sediment disturbance assessment, selection will depend on resources and objectives and will be aided by this holistic cost-benefit analysis. Cost-effectiveness will improve with evolving regulations that facilitate UAV use and technological developments (e.g. machine learning for disturbance detection) that could significantly expedite imagery analysis and enable broadscale assessments from CCO or satellite imagery.
AB - Coastal management requires cost-effective, yet accurate, assessments of habitat condition, especially in areas protected by statutory conservation measures. Unmanned Aerial Vehicles (UAVs) provide alternatives to manned aircraft and walk-over (WO) surveys. To support coastal managers with method selection, we compare the costs and benefits of the three techniques using the extent of bait collection (sediment scarring from manual digging) on intertidal mudflats from three UK sites. UAV and WO surveys were conducted in parallel and aerial photography was downloaded from the Channel Coastal Observatory (CCO). Digging was digitised from estimations on foot (WO) or by manually labelling imagery with confidence assigned (UAV/CCO). Method efficacy is compared with respect to spatial coverage, control over survey time/location, spatial resolution, positioning accuracy, and area of digging detected. Personnel hours and up-front costs (e.g. training/equipment), costs for personnel time standardised by shore area, personnel risk, and environmental impact are also compared. Regarding efficacy, CCO imagery had extensive shore coverage compared to UAV and WO, however, assessments are restricted to times/locations with available imagery. Each method's resolution was sufficient to detect digging. WO achieved the highest resolution (on foot), but the lowest positioning accuracy, in contrast to accurate feature delineation on aerial imagery. An additive two-way ANOVA revealed a significantly higher percent area of ‘dug’ sediment (all confidence levels) recorded by UAV than WO. CCO was the most cost-effective with no fieldwork/equipment costs. UAV had the highest up-front costs, but WO was more costly for personnel hours/km2 for survey time and digitisation. For all methods, digitisation was the most time-consuming aspect. Compared to WO, UAV achieved rapid shore surveys and the CCO and UAV methods minimise personnel risks. UAV and WO both cause wildlife disturbance, with trampling an additional WO impact. With each method suited to sediment disturbance assessment, selection will depend on resources and objectives and will be aided by this holistic cost-benefit analysis. Cost-effectiveness will improve with evolving regulations that facilitate UAV use and technological developments (e.g. machine learning for disturbance detection) that could significantly expedite imagery analysis and enable broadscale assessments from CCO or satellite imagery.
KW - aerial imagery
KW - benthic
KW - drone
KW - fishery
KW - mud flats
KW - remote sensing
UR - http://www.scopus.com/inward/record.url?scp=85122589814&partnerID=8YFLogxK
U2 - 10.1016/j.jenvman.2021.114386
DO - 10.1016/j.jenvman.2021.114386
M3 - Article
C2 - 35030426
AN - SCOPUS:85122589814
SN - 0301-4797
VL - 306
JO - Journal of Environmental Management
JF - Journal of Environmental Management
M1 - 114386
ER -