Long-Term Quantification of Beach Users Using Video Monitoring

Guillén, Jorge; Garciá‐Olivares, Antonio; Ojeda, Elena; Osorio, Andrés F.; Chic, Óscar; Gonzalez, Raul

doi:10.2112/07-0886.1

Cited by 50 publications

(35 citation statements)

References 5 publications

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“…The determination of the temporal and spatial distribution of tourist habits (seen as motor responses to the stimuli of the weather and climate) and possible modifications of this behaviour as a function of the variability in atmospheric conditions should be of use in short-, medium-and long-term coastal management plans. Thus, knowing the number of beach users or the density of a beach's use are important parameters, both for proper beach management and for defining indices of coastal environmental quality (Guillén et al 2008). The appropriate design of beach access facilities, showers, lifeguards, cleaning and other services is determined, to a great extent, by the number of people on the beach throughout the day and the distribution of this use in relation to the day of the week, holiday periods and weather conditions, to mention just a few aspects.…”

Section: Abstract: Sun and Beach Tourism · Atmospheric Conditions · mentioning

confidence: 99%

“…Jiménez et al (2007), drawing on information from various Argus video-cameras, proposed Coastal State Indicators (CSI) for use in different aspects of the recreational management of beaches examined in a number of case studies. Similarly, Guillén et al (2008) monitored 2 city beaches in Barcelona (Spain) and established daily, weekly, seasonal and interannual user distribution patterns based on a consideration of certain external factors, including temperature and wind conditions. Martínez-Ibarra (2010) proposed a series of weather types for evaluating the touristclimate aptitude for sun and beach tourism on the Alicante coast.…”

Section: Behaviourmentioning

confidence: 99%

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Tourism demand and atmospheric parameters: non-intrusive observation techniques

Gómez-Martín¹,

Martínez-Ibarra²

2012

Clim. Res.

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Section: Abstract: Sun and Beach Tourism · Atmospheric Conditions · mentioning

confidence: 99%

Section: Behaviourmentioning

confidence: 99%

Tourism demand and atmospheric parameters: non-intrusive observation techniques

Gómez-Martín¹,

Martínez-Ibarra²

2012

Clim. Res.

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“…In the first step, suitable images were automatically selected for further image process. An automatic approach for a long-term image analysis often meets the image quality problem owing to the hostile and uncontrollable field conditions (Guillen et al, 2008;Liu et al, 2009;Uunk et al, 2010). It occurs on days of low visibility, e.g., rain, fog or typhoon event, as well as owing to the pollution of camera lens cover.…”

Section: Image Analysis Proceduresmentioning

confidence: 99%

Long-Term Nearshore Bathymetry Evolution From Video Imagery: A Case Study in the Miyazaki Coast

Liu

Arii

Sato

et al. 2012

Int. Conf. Coastal. Eng.

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A four-year evaluation of the nearshore morphology based on video imagery was conducted in the Miyazaki coast, Japan. An automatic nearshore bathymetry mapping technique was developed based on the linear wave dispersion relationship, in which the wave phase velocity was estimated from the cross-correlation of neighboring pixels' brightness time series. Bathymetry estimation was carried out at both low and high tide levels of each day and integrated over a 7-day period which includes both the neap and spring tides. Model validation shows the present technique reproduced the ground truth data fairly well with a bias generally less than 0.3 m in the surf zone. In total, 88 bathymetry data were obtained within the investigation period. Subsequently, a two dimensional Empirical Orthogonal Function (EOF) analysis was applied to the time series of the obtained nearshore bathymetry data in order to investigate their long-term morphological behavior. Results from the first three modes of the EOF analysis represent the bathymetry variation from the nearshore response due to the cross-shore processes. The first mode reveals the coastal erosion occurred during the study period with a net loss of sand volume in the target region. Erosion was severe before December 2008, especially in the offshore region, and mitigated afterwards resulting a relatively equilibrium stage for the mean beach profile. The second mode shows the mean beach profile rotated in an anti-clockwise direction around a cross-shore location of 50 m away from the present shoreline, which led to a milder beach slope in the study period. The third mode represents the cross-shore movement of the longshore bar. Higher modes of the EOF analysis, such as fourth, fifth and sixth modes, correspond to the bathymetry variation due to the longshore sediment movement which frequently changed its direction as indicated from a number of nodal points in the relevant temporal eigenfunction distribution. Based on the EOF analysis, it is also confirmed that the nearshore morphological process in the target area was predominant by the cross-shore sand movement.

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“…Holland et al, 2001;Chickadel et al, 2003) and later subtidal bathymetries (e.g Aarninkhof et al, 2005;van Dongeren et al, 2008;Holman et al, 2013). The global presence of coastal camera stations makes it possible to monitor long-term coastal behavior (Smit et al, 2007) and sparked several applications for coastal zone managers, like estimating coastal state indicators , deriving beach usage statistics (Jimenez et al, 2007;Guillén et al, 2008), tracking sandbar positions (Lippmann and Holman, 1989;van Enckevort and Ruessink , 2001;Price and Ruessink , 2011) and rip current detection systems (Bogle et al, 2000;Gallop et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

An automated method for semantic classification of regions in coastal images

Hoonhout

Radermacher

Baart

et al. 2015

Coastal Engineering

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Large, long-term coastal imagery datasets are nowadays a low-cost source of information for various coastal research disciplines. However, the applicability of many existing algorithms for coastal image analysis is limited for these large datasets due to a lack of automation and robustness. Therefore manual quality control and site-and time-dependent calibration is often required. In this paper we present a fully automated algorithm that classifies each pixel in an image given a pre-defined set of classes. The necessary robustness is obtained by distinguishing one class of pixels from another based on more than a thousand pixel features and relations between neighboring pixels rather than a handful of color intensities.Using a manually annotated dataset of 192 coastal images, a Structured Support Vector Machine (SSVM) is trained and tested to distinguish between the classes water, sand, vegetation, sky and object. The resulting model correctly classifies 93.0% of all pixels in a previously unseen image. Two case studies are used to show how the algorithm extracts beach widths and water lines from a coastal camera station.Both the annotated dataset and the software developed to perform the model training and prediction are provided as free and open-source data sources.

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Long-Term Quantification of Beach Users Using Video Monitoring

Cited by 50 publications

References 5 publications

Tourism demand and atmospheric parameters: non-intrusive observation techniques

Tourism demand and atmospheric parameters: non-intrusive observation techniques

Long-Term Nearshore Bathymetry Evolution From Video Imagery: A Case Study in the Miyazaki Coast

An automated method for semantic classification of regions in coastal images

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