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Títol: Characterization of nearshore hydrodynamics with UAV video data

Estudiants que han llegit aquest projecte:

Director/a: RIBAS PRATS, FRANCESCA

Departament: FIS

Títol: Characterization of nearshore hydrodynamics with UAV video data

Data inici oferta: 01-05-2022     Data finalització oferta: 30-10-2022


Estudis d'assignació del projecte:
    GR ENG SIST AEROESP
Tipus: Individual
 
Lloc de realització: EETAC
 
Segon director/a (UPC): CALVETE MANRIQUE, DANIEL
Segon director/a extern: SIMARRO GRANDE, Gonzalo
 
Paraules clau:
UAV videos, Remote sensing, Image analysis, Signal processing, Beach dynamics, Shoreline motion
 
Descripció del contingut i pla d'activitats:
Measuring the fast intrawave motion in the swash zone is
important because there are very few data sets available due to
the intrinsic surveying difficulties with traditional in situ
methods. Remote sensing techniques based on video-camera systems
are interesting complements to survey beach dynamics because they
provide continuous monitoring at high temporal resolution. In
particular, the advent of UAVs in the last decade has led to a
variety of interesting cheap applications to monitor beach
dynamics. This includes the shoreline but most existing studies
only intend to obtain time-averaged data with only a few
exceptions exploiting the capacity of UAV videos to resolve the
intrawave swash motion. Therefore, the aim of this study is to
explore the potential of UAV videos to characterize the swash
dynamics and its coupling with incident waves.

Methods:
Available data include UAV videos made in Playa de la Victoria
(Cádiz, Spain) that show the propagation of incident waves and
the swash dynamics in the presence of beach cusps. The first step
will consist on calibrating and georeferencing all the video
frames with available user-friendly Python codes (Simarro et al.,
2020; Simarro et al., 2021b). Then, the shoreline position will
be extracted from all the video frames using smoothing and
gradient detection techniques. Once all the shorelines are
available, a mode decomposition analysis (e.g., EOF or DMD) will
be applied to characterize the inherent spatial and temporal
scales. In order to understand the potential coupling with the
incident surface waves, a second mode analysis of the wave
propagation in the nearshore will be performed using available
Python codes (Simarro et al., 2019). Finally, all the obtained
results will be analysed in detail in order to characterize the
swash dynamics and the potential coupling with incident waves. It
is particularly interesting to find out if there is any sign of
infragravity waves, given the presence of beach cusps in the
studied beach.

Schedule:
Week 1 (2-8/5): Literature reading
Week 2-3 (9-22/5): Video calibration and generation of planviews
(with user-friendly codes)
Weeks 4-8 (23/5-26/6): Shoreline extraction with gradient
detection (by building codes)
Weeks 9-11 (27/6-17/7): Shoreline analysis with mode
descomposition (by building codes)
Weeks 12-13 (18-31/7): Wave analysis with mode descomposition
(with available codes)
Weeks 14-17 (29/8-25/9): Characterization of swash dynamics, its
potential coupling with waves and the presence of infragravity
waves
Weeks 18-22 (26/9-30/10): TFG writing and defense preparation
(TFG writing could be partially done during August)
 
Overview (resum en anglès):

This work aims to explore the potential of UAV-captured videos for the characterization of shoreline dynamics at time scales of seconds to minutes. A 2 min video recorded by a UAV at ¿Playa de la Victoria¿ in Cádiz, on a day when there were beach cusps, was used.

To begin, the images corresponding to the frames of the video to be analyzed were extracted and calibrated. For this, two software from GitHub were used. The first of them is called UClick and it allows to manually calibrate a subset of frames. The second is called Udrone and allows you to calibrate all the other frames from the previous subset and make planviews (images with a zenith view) of the different areas to be studied: an area around the coastline and an area at open sea.

The second step was to develop a Python code for the automatic detection of the coastline from the planviews of this area. Currently, there is no standard methodology to automatically detect the coastline. In this work, image processing tools have been used, such as filters, and two different methodologies have been applied to be able to compare the results. The first is based on the calculation of the gradient of different channels that characterize the colours of the image and that change rapidly around the coastline. The second is based on classifying the pixels between sea and land according to a combination of two colour channels. The best method for coastline detection has turned out to be the gradient of the Red/Green channel, a channel widely used for these applications. The RMSE (Root Mean Square Error) obtained in four frames, which have been taken as an example, have been from 0.4 to 9.7 meters.

Next, the dynamics of the line have been characterized using an EOF (Empirical Orthogonal Functions) decomposition through an already existing code. In both shoreline extraction methodologies, the first EOF has been seen to describe a significant percentage of variance and can therefore be considered to represent a significant portion of the dynamics. In fact, the spatial distribution of the first EOF captures the cusps that were present at "Playa de la Victoria". Doing the Fourier transform of the temporal distribution of the first EOF, it was obtained that the dominant periods were 13.5, 10.1 and 8.7 seconds. Finally, the open sea swell periods have been characterized to be able to compare them with the coastline oscillation periods. To do this, the DMD of the open sea planviews have been calculated with part of the UBathy software, also obtained from GitHub. The dominant periods extracted from the DMD were found to be in 13.1, 6.4 and 4.4 seconds. The software has also been applied to planviews of the area around the coastline to compare with the result of the EOFs of the extracted coastlines. From this area it has been obtained that the dominant periods extracted from the DMD are in the intervals from 5.6 to 6 and from 13.6 to 14.2 seconds.

This work demonstrates the feasibility of using UAV videos to detect the periods of swash zone oscillation due to incident swell.

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