Finisterra, LXI, 2026, e42824

ISSN: 0430-5027

doi: 10.18055/Finis42824

Artigo

LOW-COST COASTAL MONITORING USING CITIZEN SCIENTIST DATA:

AN OVERVIEW OF THE COASTSNAP PROGRAM IN BRAZIL ꢀ

1

VITÓRIA GONÇALVES SOUZA

MIGUEL DA GUIA ALBUQUERQUE

DAVIS PEREIRA DE PAULA

MELVIN MOURA LEISNER

MATHEUS CORDEIRO FAÇANHA

ANTONIO RAYLTON RODRIGUES BENDÔ

2

3

4

3

SAMYRA COSTA DE FREITAS

ABSTRACT – Understanding the dynamics of coastal environments is challenging, as it requires reliable, high-

frequency data that reflect environmental reality. In situ data collection demands high financial resources and

specialized teams, while remote sensing may be limited by spatial and temporal resolution. Low-cost monitoring with

citizen participation has therefore become essential for qualitative management strategies in coastal municipalities.

This study provides an overview of the CoastSnap project worldwide, highlighting its implementation and

dissemination in Brazil, and presenting two applications distinct from traditional shoreline analysis. The methodology

employed images from the CoastSnap NE and RS networks to monitor cliff mass movements at Pacheco Beach and user

density at Cal Beach. Cliff monitoring identified and mapped 91 mass movement events between April 2021 and June

2024, mostly during the rainy season, emphasizing the hazards and geomorphological evolution of cliffs, and reinforcing

the importance of continuous monitoring that integrates citizen participation. User density analysis showed an area of

approximately 10.0 hectares, peak occupancy reached 77.9% and 40.9% at 12 a.m. and 3 p.m., while the lowest occurred

at 9 a.m. and 6 p.m., with 4.3% and 31.7%. These case studies demonstrate CoastSnap’s potential to support coastal

management at low cost, effectively involving citizens in environmental monitoring.

ꢀ

Keywords: Coastal management; citizen science; mass moviment; beach user density.

RESUMO – MONITORIZAÇÃO DE BAIXO CUSTO COM RECURSO A DADOS DE CIENTISTAS CIDADÃOS: UMA

VISÃO DO PROGRAMA COASTSNAP NO BRASIL. Compreender a dinâmica dos ambientes costeiros constitui uma tarefa

complexa, uma vez que exige dados fiáveis e de elevada frequência que representem, com precisão, a realidade

ambiental. A recolha de dados in situ implica elevados custos financeiros e equipas especializadas, enquanto o recurso

ao sensoriamento remoto pode apresentar limitações quanto à sua resolução espacial e temporal. Neste contexto, a

monitorização de baixo custo, associada à participação cidadã, tem-se tornado essencial para a formulação de

estratégias de gestão qualitativa nos municípios costeiros. O presente estudo apresenta uma visão geral do projeto

CoastSnap em escala global, destacando a sua implementação e disseminação no Brasil, e descreve duas aplicações

distintas relativamente ao uso tradicional de imagens para a análise da linha de costa. A metodologia baseou-se em

fotografias das redes CoastSnap NE e RS para monitorizar movimentos de massa em arribas da Praia do Pacheco e a

densidade de utilizadores na Praia da Cal. A monitorização das arribas permitiu identificar e cartografar 91 eventos de

movimentos de massa entre abril de 2021 e junho de 2024, maioritariamente durante a estação chuvosa, evidenciando

os riscos associados e a evolução geomorfológica destas formas. A análise da densidade revelou que, numa área de

aproximadamente 10 hectares, a ocupação máxima atingiu 77,9% e 40,9% às 12h e 15h, respetivamente, enquanto os

valores mínimos ocorreram às 9h e às 18h, com 4,3% e 31,7%. Os dois estudos de caso demonstram o potencial do

CoastSnap para apoiar a gestão costeira a baixo custo, com efetiva participação cidadã.

Palavras-chave: Gestão costeira; ciência cidadã; movimentos de massa; densidade de utilizadores de praias.

Recebido: 22/08/2025. Aceite: 10/01/2026. Publicado: 01/02/2026.

¹Institute of Human and Information Sciences, Federal University of Rio Grande, Av. Italia, km 8, 60.714-913, Rio Grande- RS, Brazil.

E-mail: vitoriagoncalvessouza@gmail.com

2

Federal Institute of Science and Technology of Rio Grande do Sul – IFRS, Campus Rio Grande, Rio Grande- RS, Brazil. E-mail:

migueldaguia@gmail.com

3

State

University of

Ceará, Fortaleza-CE, Brazil. E-mail: davispp@gmail.com, melvin.Leisner@aluno.uece.br,

matheuscordeiro@edu.unifor.br , samyra.freitas@aluno.uece.br

⁴Delft University of Technology, Delft, Netherlands. E-mail: rayltonbendo@gmail.com

Published under the terms and conditions of an Attribution-NonCommercial-NoDerivatives 4.0 International license.

Souza, V. G., Albuquerque, M. G., Pereira de Paula, D., Leisner, M. M., Façanha, M. C., Bendô, A., Freitas, S. Finisterra, LXI, 2026, e42824

HIGHLIGHTS

•

Low-cost monitoring integrates citizen science in coastal environments

CoastSnap applied in Brazil for cliffs and beach user density monitoring

At Cal Beach, density data supported management of tourist carrying capacity

At Pacheco Beach, CoastSnap images identified risk areas and natural hazards

Results highlight CoastSnap’s potential for innovative coastal management

1.

INTRODUCTION

Sandy beaches are dynamic and complex environments continuously modified by both natural

and anthropogenic processes. These areas cover over one-third of the world's shoreline, providing

various ecosystem services to society (Luijendijk et al., 2018; Vousdoukas et al., 2020). Over the past

few decades, the intensification of urban development, population growth, and climate change

scenarios has placed significant environmental pressure on coastal regions (Turner et al., 2001;

Zacarias, 2013; Lithgow et al., 2014; Botero et al., 2015; IPCC, 2021).

To understand the complex morphodynamic variations of sandy beaches in response to

different climatic conditions, it is necessary to conduct frequent and continuous monitoring of various

aspects, including different scales and parameters (e.g., waves, sediments, coastal topography,

bathymetry, sand transportation, and forms of use and occupation). The high cost associated with

medium and long-term monitoring is the major limitation to the high-frequency acquisition of spatial

data. This, in turn, generates uncertainty in coastal planning and management and may lead to

erroneous decision-making by coastal managers due to insufficient or incomplete coastal data.

Numerous measurement and observation methods have been employed to monitor coastal

environments. Among the main techniques the following stand out: aerial photogrammetric surveys

(Paola et al., 2022), remote sensing techniques using images obtained from orbital sensors (Touré et

al., 2019; McAllister et al., 2022), LiDAR surveys (Bossard & Lerma, 2020), in situ surveys with GPS-

RTK receivers (Splinter et al., 2018) and UAVs (Unmanned Aerial Vehicles), and video monitoring

systems (Holman & Stanley, 2007). Traditional remote sensing methods can be costly when high

spatial resolution is required, and they may also exhibit temporal gaps caused by cloud cover or by the

revisit time of orbital sensors. Field surveys, in turn, also tend to be expensive, as they require

specialized equipment and trained personnel, which ultimately results in limited temporal coverage

due to the associated logistical constraints.

Although each method has its advantages and uncertainties, they all share spatial, temporal,

logistical, and/or financial limitations. In light of this, recent technological advancements have enabled

the collection and storage of large volumes of data through smartphones and easy access to the

internet, facilitating the generation and sharing of information between citizens and scientists (Hart &

Martinez, 2006; Zerger et al., 2010; Poelen et al., 2014; González-Villanueva et al., 2023). Faced with

the challenges posed by the high costs of acquiring in situ data and the low participation of civil society,

alternative data acquisition methodologies based on low-cost technologies and citizen engagement

have emerged, enabling continuous data collection across broad spatial scales while fostering

community involvement and the reciprocal exchange of knowledge.

To enable greater citizen participation, CoastSnap was created in 2017. Developed by

researchers from the Water Research Laboratory at the University of New South Wales, Sydney,

Australia, CoastSnap is a global citizen science project based on low-cost participatory monitoring

(Harley & Kinsela, 2022). Citizen science involves community contributions to the development of

scientific research (Bonney et al., 2009). In CoastSnap, citizens participate by sharing photographs of

the landscape taken with their smartphones. The images are stored in a centralized database, enabling

different categories of analysis such as coastline movement and user density, among other

applications.

One of the major challenges today is encouraging civil society's interest in participating in

scientific research to build knowledge in different areas (Martins & Cabral, 2021). In this context, this

study aims to provide a global overview of CoastSnap, describe its current status in Brazil, and present

two case studies that showcase different applications of citizen-generated imagery. The first evaluates

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Souza, V. G., Albuquerque, M. G., Pereira de Paula, D., Leisner, M. M., Façanha, M. C., Bendô, A., Freitas, S. Finisterra, LXI, 2026, e42824

user-density patterns at Cal Beach (RS) using CoastSnap RS data, while the second demonstrates the

potential of CoastSnap NE data from Pacheco Beach (CE) for monitoring cliff mass-movement. These

examples highlight the contribution of citizen scientists to coastal monitoring.

2.

CoastSnap Project: a brief overview and its implementation along the Brazilian coast

Community monitoring proposals associated with advances in low-cost technologies, remote

access to mobile devices, and information and communication technologies aim to enhance citizen

science as a tool for including the general public in research development focused on sustainable

coastal zone management. CoastSnap is a global citizen science project, regarded by Harley and Kinsela

(2022) as the largest land-based coastal monitoring program (i.e., excluding the use of remote sensing

data). Using images captured through the smartphones of coastal community participants, the data

generated by CoastSnap allow, for example, coastal processes to be observed at a local scale.

The success of the pilot project by Harley et al. (2019) in Australia ensured that the low-cost

coastal monitoring methodology was rapidly adopted by various institutions worldwide. The rapid

growth facilitated an expanded range of tool applications, the development of open-source algorithms,

and training courses to assist partners in managing their regional CoastSnap networks (Harley &

Kinsela, 2022). Currently, CoastSnap initiatives are distributed along the coastlines of several

countries, as shown in Table I.

Table I – Worldwide overview of CoastSnap projects (Period: 2018 to June 2025).

Tabela I – Panorama global dos projetos CoastSnap (Período: 2018 a junho de 2025).

Continent

Oceania

Country

Australia

New Zealand

France

Holland

Spain

Portugal

Sweden

Germany

United Kingdom

United States

Canada

Brazil

Chile

Uruguay

Mozambique

Source: https://www.coastsnap.com/

Projects Number

08

02

04

01

03

05

01

11

01

Europe

North America

South America

Africa

In addition to promoting greater participation from coastal communities, CoastSnap also aligns

with Sustainable Development Goals (SDG 13 - Climate Action) by facilitating interaction between the

academic community, public managers, and civil society. This involvement has enabled the

construction of participatory knowledge, which aids in understanding the environmental dynamics of

monitored coastal segments and implementing appropriate shoreline management strategies.

In Brazil, the first participatory coastal monitoring initiative emerged in 2018 in Santa Catarina

(SC) state. Implemented by the Federal University of Santa Catarina (UFSC), CoastSnap SC established

its first station at Santinho Beach. Subsequently, in 2020, CoastSnap Ceará was established, due to the

Covid-19 pandemic, the project only started operating in 2021 at Pacheco Beach (pilot project),

involving researchers from the State University of Ceará (UECE) and the Federal University of Ceará

(UFC). The primary focus was monitoring the shoreline and the mass movements of the cliff adjacent

to the beach. In 2022, with funding from the National Council for Scientific and Technological

Development (CNPq), CoastSnap Ceará expanded to become CoastSnap NE (or CoastSnap Nordeste),

operating in the states of Ceará, Rio Grande do Norte, and Piauí, and involving three additional higher

education institutions (UESPI, UFRN, and UERN).

Also in 2020, in southeastern Brazil, the Mar à Vista Project of the Federal University of Rio de

Janeiro (UFRJ) installed the first CoastSnap station in Rio de Janeiro (RJ) state. The CoastSnap RJ station

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Souza, V. G., Albuquerque, M. G., Pereira de Paula, D., Leisner, M. M., Façanha, M. C., Bendô, A., Freitas, S. Finisterra, LXI, 2026, e42824

is located at Prainha, in the western part of Rio de Janeiro (Lins-de-Barros et al., 2022). The successful

data collection from the stations in Santa Catarina, Ceará, and Rio de Janeiro facilitated the

implementation of additional stations in these states and spurred similar initiatives in other coastal

states of Brazil. Table II presents the distribution and number of CoastSnap stations active in Brazil.

Table II – Overview of CoastSnap stations in Brazil (Period: 2018 to June 2025).

Tabela II – Panorama das estações do CoastSanp no Brasil (Período: 2018 a junho de 2025).

Brazilian Region

North

State

Amapá

Alagoas

Bahia

Responsible Institutions

UFAP

UFAL

UFBA

UECE and UFC

UFMA

UFPE

UESPI

UERN and UFRN

UFSE

Stations per Region

01

05

02

01

03

08

12

03

Ceará

Maranhão

Pernambuco

Piauí

Rio Grande do Norte

Sergipe

Northeast

Southeast

Rio de Janeiro

Santa Catarina

UERJ, UFF and UFRJ

UFSC

IFRS

South

03

Rio Grande do Sul

UFRGS

Source: Authors’ elaboration

In Brazil, the processes of station implementation, image database creation, data processing,

information dissemination on networks, and station maintenance have been the responsibility of

federal and state universities. In most cases, the management of the stations has been carried out

independently, being the responsibility of the institutions that enabled the implementation of the

CoastSnap stations. To a lesser extent, some institutions work together, generally as part of universal

projects and with financial support from federal and/ or state research funding agencies.

To demonstrate the potential of the CoastSnap methodology for generating valuable products

for coastal management, this study presents two regional case studies. In this context, the terms

CoastSnap NE and CoastSnap RS refer to the regional branches of the CoastSnap initiative operating in

the Northeast (‘NE’) and Southern (‘RS’) regions of Brazil, respectively. The first focuses on the analysis

of mass movements in cliffs, utilizing data from the CoastSnap NE image bank at the Pacheco beach

station. The second addresses the monitoring of beach user density, based on images from the

CoastSnap RS station at Cal Beach.

3.

STUDY AREAS

3.1.

Pacheco Beach

In the first case study, CoastSnap data were used to characterize mass-movement processes at

Pacheco Beach, in the municipality of Caucaia, within the Metropolitan Region of Fortaleza,

northeastern, Brazil (Fig. 1). The study area corresponds to a cliffed coastal sector that hosts the first

CoastSnap station installed in the region. The municipality of Caucaia has a coastal extension of

approximately 28 km, distributed among six beaches, of which roughly 3 km correspond to Pacheco

Beach. This shoreline is characterized by an alternation of active sea cliffs and short sandy beach

segments, and in some areas coastal protection structures, primarily groins and rock revetments, have

been installed to mitigate erosion (Leisner et al., 2023).

Pacheco Beach is situated within the Baturité–Jaibaras structural domain, and its cliffs consist

of sediments from the Barreiras Formation, composed of Neogene siliciclastic deposits of a pre-littoral

environment that are widely distributed along the northeastern Brazilian coast (Bezerra et al., 2006).

The origin of these deposits is generally linked to episodes of epigenetic uplift (Bezerra et al., 2001;

Saadi et al., 2005) and marine transgressive phases (Rossetti et al., 2013).

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Figure 1 – Pacheco Beach and the CoastSnap NE monitoring station, municipality of Caucaia, Ceará.

Figura 1– Praia do Pacheco e da estação de monitoramento CoastSnap NE, município de Caucaia, Ceará.

Source: Authors’ elaboration based on SIRGAS 2000, Zone 24S, IBGE (2022)

From a sedimentological perspective, Pacheco Beach is predominantly composed of medium

sand; however, during episodes of intense erosion driven by storm waves, gravel may temporarily

dominate the beach surface (Leisner et al., 2024). According to the study conducted by the authors,

short-term analyses further show that cliff retreat and beach morphology vary on seasonal timescales,

alternating between phases of erosion and deposition.

For the initial development of the CoastSnap NE pilot project, a 700 m-long segment of this

beach–cliff system, recognized as being under continuous erosion, was selected. This stretch was

chosen due to its suitability for smartphone-based image acquisition, offering both adequate elevation

and a lateral viewpoint, as well as the presence of beach users who could contribute photographs.

3.2.

Cal Beach

CoastSnap data was used to characterize user density on Cal Beach in Torres, southern Brazil

(Fig. 2), during the 2024 summer season. These analyses relied on images contributed by citizen

scientists, that is, photos captured and submitted by tourists and visitors who used the CoastSnap RS

station installed at Cal Beach. The choice of this location for implementing a CoastSnap station was due

to the region having the highest degree of urbanization of the RS coast, with Torres being one of the

largest coastal cities in the state with a permanent population, and 50% of the coastline urbanized

(Esteves et al., 2003; IBGE, 2023), the exceptions are in the Conservation Unit areas (Itapeva State Park

and Guarita Park) and a small part of the southern municipality (Rockett et al., 2018). The fact that the

local economy is primarily based on tourism and leisure activities (Lopes et al., 2018) also contributed

to the choice of the location.

Geologically, the study area is characterized by a narrower coastal plain with an internal

boundary marked by the escarpments of the Serra Geral and the eastern edge of the Paraná Basin,

which reach the current shoreline, forming the only rocky promontory with rock formations composed

of sandstones, basalts, and volcano-clastic sequences (Pereira et al., 2010). In this setting, Cal Beach

corresponds to an asymmetrical embayed (pocket) beach bounded by two rocky headlands — Morro

do Farol to the north and Morro da Guarita to the south and exhibits typical intermediate behavior

with high mobility due to significant vertical variations (Calliari & Toldo Jr., 2016). The backshore is

characterized by embryonic dunes and restinga vegetation, indicative of early-stage foredune

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development; however, parts of these features have been altered or constrained due to the proximity

of urbanized areas and coastal infrastructure (Cristiano et al., 2016).

The tidal regime is semidiurnal with an average height of 0.30 m; however, meteorological tides

can reach up to 1.20 m (Calliari et al., 1996; Gonzaga et al., 2020), intensifying the erosive capacity of

the waves and potentially causing severe damage to the coast (Calliari & Silva, 1998). These

phenomena are associated with the passage of cold fronts that occur more frequently in April and May

(Albuquerque et al., 2018). Additionally, the most frequent winds are from the northeast (NE) followed

by the south (S) (Leal-Alves et al., 2020).

Figure 2 – Cal Beach and the CoastSnap RS monitoring station, situated in the municipality of Torres, Rio Grande do

Sul.

Figura 2 – Praia da Cal e da estação de monitoramento CoastSnap RS, situada no município de Torres, Rio Grande do Sul.

Source: Authors’ elaboration based on SIRGAS 2000, Zone 22S, IBGE (2022)

The geomorphological characteristics, with rocky outcrops forming cliffs and coves, constitute

a unique landscape diversity present on the coast of the state of Rio Grande do Sul, offering high

tourism potential (Cristiano et al., 2016). During the summer season, Cal Beach, whose width ranges

from approximately 100 m to about 40 m, depending on prevailing meteorological and oceanographic

conditions, receives a high concentration of bathers. This intense use generates environmental

impacts, including the accumulation of solid waste and the discharge of effluents, which can

contaminate beach sediments and coastal waters.

4.

MATERIALS AND METHODS

The CoastSnap methodology proposed by Harley et al. (2019) relies on community participation

in coastal monitoring through smartphone-captured photos shared on social networks (Facebook,

Instagram, WhatsApp, and X) by residents and tourists. For this purpose, simple and low-cost

structures, known as stations or supports for smartphones, made of stainless steel, are installed on

monitored beaches to control the position and angle of the photograph to be sent. The stations also

feature plaques with instructions on properly positioning the smartphone for image capture (figs. 3a

and 3b) and on what data should be provided when submitting the photo to the CoastSnap database.

Figure 4 presents the methodological flowchart with the main steps applied for the two case

studies presented. The photographs sent by citizen scientists are stored in a database with information

on the date and time of capture to enable corrections for the influence of astronomical tides on the

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water level, given that the images are collected at random stages of the tidal cycle. These images are

processed using the Bird Eye View method, with routines applied in MATLAB software. This method

involves digital image processing that results in the geometric modification of the image, transforming

the view from a real perspective to an overhead/plan view (Venkatesh & Vijayakumar, 2012). This

transformation is divided into three main steps: i) shifting the image into a new coordinate system; ii)

rotating the image; and iii) projecting the image onto a two-dimensional plane. It is important to note

that the first step is carried out using fixed ground control points (GCPs) in the study area (e.g.,

buildings, containment structures) previously defined by the researchers.

After the station's establishment, fixed and easily identifiable GCPs in the photos are collected

using a GPS-RTK for georeferencing and image correction. The GCPs are manually identified in a single

control image to aid in orthorectification, associating pairs of coordinates to each pixel, and each

subsequent image is then registered to the control image using the automatic alignment function in

Adobe Photoshop software.

Figure 3 – CoastSnap RS stations located in: a) Morro do Farol with a view of Cal Beach, Torres, Rio Grande do Sul

state; b) Pacheco beach, Caucaia, Ceará state.

Figura 3 – Estações CoastSnap RS localizadas em: a) Morro do Farol com vista para a Praia da Cal, Torres, Rio Grande do

Sul; b) Praia do Pacheco, Caucaia, Ceará.

Source: Authors’ elaboration

Figure 4 – Methodological flowchart for cliff mass-movement detection and user-density analysis.

Figura 4 – Fluxograma metodológico para detecção de movimentos de massa em falésias e análise de densidade de

usuários.

Source: Authors’ elaboration

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The images are processed using algorithms developed by Harley et al. (2019) in the Matlab

programming language, which extract information on shoreline position as well as several other

parameters relevant to coastal management. These include the characterization of beach activities and

uses, the estimation of user density and distribution, the assessment of risks such as cliff mass

movements, and the identification of potential pollution sources, including wastewater discharge,

solid waste, and oil spills. Together, these products provide essential support for coastal monitoring,

planning, and environmental quality conservation.

Through a locally adaptive thresholding algorithm, it is possible to determine the difference

between the waterline and the sand, which serves as an indicator of the shoreline (Boak & Turner,

2005), allowing for automatic vectorization of this feature. The date and time information of the

captured images is used to correct the shoreline position due to the influence of meteorological tides,

thereby enabling the identification of beach changes and the analysis of shoreline behavior. This

standard procedure applied at any CoastSnap stations allows for the monitoring, and measurement of

the constant changes occurring along the shoreline over time, with precision similar to other

techniques used for beach monitoring (Harley et al., 2019; Harley & Kinsela, 2022).

To demonstrate the potential of CoastSnap-generated products, this study presents two new

applications focusing on the analysis of cliff mass movements and the monitoring of beach user

density. In the first case study, mass movements were identified in the CoastSnap Pacheco

photographs and recorded in a database with information about their positioning. Two monthly

images were selected, covering the period between April 2021 and July 2024, totaling 78 images

analyzed. The mapping of mass movements was conducted by integrating two methods: the

orthorectified CoastSnap image was processed using Matlab 2018A, and the location of the

occurrences was recorded using the GIS software QGIS 3.28. In this way, it was possible to identify the

most active sections of the cliff over time, allowing the creation of a hazard map for mass movements.

This is only possible due to the georeferencing of the photographs during image processing, which

simplifies the identification of mass movements through geoprocessing. From the interpolation of the

points where mass movement occurred, heat maps (Kernel algorithm) were generated to determine

the areas with the highest concentration of cliff collapse occurrences.

Mass-movement events were identified through systematic visual analysis of the multitemporal

images, based on diagnostic indicators of cliff instability such as newly formed scarps, freshly exposed

surfaces, detachment zones, fallen or accumulated blocks at the cliff base, and abrupt changes in the

geometry of the cliff top or face. Only features that appeared or expanded between consecutive survey

intervals were classified as distinct events.

To determine user density in the second case study, we applied superpixel segmentation to the

CoastSnap images. Superpixels are clusters of neighboring pixels with similar spectral or textural

properties, allowing the image to be partitioned into meaningful homogeneous regions. This

procedure enabled the generation of binary masks that separated free and occupied beach areas. The

centroids of the pixels classified as “occupied beach” were subsequently extracted and converted into

georeferenced point vector files for further spatial analysis.

For user counting, this technique involves partitioning the image into several clusters of pixels.

The images were segmented into two classes: free and occupied beach areas. For the second case study,

images from January 15, 2024, captured at 9:00 AM, 12:00 PM, 3:00 PM, and 6:00 PM were used.

Superpixel segmentation generated binary images (masks) for both classes. Next, user density was

calculated using QGIS 3.28, employing a spatial density (kernel) algorithm, which assigned an

occupancy distribution to the sand strip at each time.

5.

5.1. Pacheco Beach

Quantitative analysis indicated a high incidence of mass movements along the cliff section of

RESULTS

Pacheco, totaling 91 events over a stretch of 700 meters between April 2021 and June 2024. The

distribution of these mass movements was predominantly concentrated in the central-eastern portion

of the study area, with higher occurrence during the first semester of each monitored year (fig. 5).

These periods are associated with episodes of increased precipitation in the state of Ceará.

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The months of January 2022, with 16 events, April 2024, with 10 events, and March 2024, with

8 events, recorded the highest frequencies of slope movements. In contrast, the months corresponding

to the dry season showed a significant reduction in activity, with a maximum of one event, as observed

in July, August, and November 2023. These data indicate the seasonal influence of climatic processes,

demonstrating that the rainy season, which occurs predominantly between January and June in the

region, exerts a significant control over slope instability processes affecting the cliffs.

Figure 5 – Spatial distribution of mass movements in Pacheco beach. a) Mass movement concentrations map, using

kernel interpolation; b) Monthly distribution of mass movements between January April 2021 and June 2024.

Figura 5 – Distribuição espacial dos movimentos de massa ocorridos na praia de Pacheco. a) Mapa de concentrações de

movimentos de massa, utilizando interpolação kernel; b) Distribuição mensal dos movimentos de massa entre janeiro a

abril de 2021 e junho de 2024.

Source: Authors’ elaboration

With respect to community participation, the Pacheco Beach station received a total of 401

photographs between April 2021 and June 2025 (fig. 6). Figure 6 shows the dynamics of monthly

submissions since the station’s implementation, highlighting peaks of participation in July 2021 (25

photos), January 2024 (21 photos), March 2023 (19 photos), January 2025 (17 photos), and February

2025 (15 photos).

30

25

21

19

20

17

15

10

5

0

Months

2021

2022

2023

2024

2025

Figure 6 – Number of photos received from April 2021 to June 2025 for Pacheco Beach.

Figura 6 – Número de fotos recebidas de abril de 2021 a junho de 2025 para a Praia do Pacheco.

Source: Authors’ elaboration

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It is observed that in almost all years, the highest number of contributions coincide with the

summer period (January, February, and March), which may be associated with the tourist high season

and school holidays, underscoring the influence of seasonality on user engagement. However, when

comparing across different years, distinct patterns emerge: in 2022, participation was concentrated

exclusively in January (4 photos) and February (5 photos), reflecting a punctual and low contribution;

in 2023, there was a marked regularity in submissions throughout nearly all months, except for

January, suggesting greater continuous community engagement; whereas in 2025, from January to

June, a stable pattern with low variability was recorded, with monthly submissions ranging between

11 and 17 photos, indicating a more consistent, albeit still moderate, level of contribution.

Instagram is the project's main vehicle for communication with the community, being used to

publicize activities, results, educational materials, ongoing studies, and participation in events,

connecting both the academic community and the general public. In terms of performance and

engagement on social media, the CoastSnap NE Instagram profile, which currently has 703 followers,

reached 3860 accounts, of which 90.7% do not follow the profile and 9.3% are followers, totaling

13573 impressions. As for the audience profile, 51% of followers are women and 48.9% are men. The

predominant age group is between 25 and 44 years old, representing 67.1% of total followers.

5.2. Cal Beach

Originally, CoastSnap was developed to monitor shoreline variations, identifying erosion or

progradation processes along coastal segments. However, for the second case study, high-frequency

database images of CoastSnap RS provided by citizen scientists were utilized for the user density

analysis during a summer day (January 15, 2024) at Cal beach.

This analysis revealed that the total area of the sand beach available to bathers at Cal Beach was

approximately 10.0 hectares, with an average distance of 43 meters between the base of dunes and

the shoreline. At 9:00 AM and 6:00 PM, the lowest percentage of occupied area was recorded, with

4.3% and 31.7%, respectively (table III). The highest concentration of users was observed at 12:00 PM,

when solar incidence is most intense, with 77.97% of the area occupied. By 3:00 PM, the percentage of

beach users decreased to 40.9%.

Table III - Characterization of the free and occupied total areas, and occupation percentages of Cal beach during the

survey.

Tabela III - Caracterização das áreas totais livres e ocupadas e percentuais de ocupação da praia da Cal durante o

levantamento.

Distance between

shoreline and dune

(m)

% Occupied

Free Area

(m²)

Occupied Area

(m²)

Total Area

(m²)

Hour

9h

94969

21863

58620

67786

4281

77387

40630

31464

4.3

77.97

40.9

31.7

99250

43

12h

15h

18h

Source: Authors’ elaboration

Spatially, users, regardless of the monitored time of day, were predominantly concentrated in a

specific area located in the southwestern sector of the beach (figure 7). The highest occupation

densities are represented by warm colors, while the lowest appear in cooler tones.

This distribution pattern can be attributed to the presence of infrastructures and supporting

services, such as parking, a lifeguard station, kiosks/bars, toilet, and spaces dedicated to sports

activities, including volleyball and beach tennis courts. This set of attributes, typically valued by

tourists seeking comfort and convenience, contributes to the greater attractiveness of this region of

the beach and, consequently, leads to higher levels of occupation.

Regarding community contributions through photo submissions to the CoastSnap RS initiative,

over one year and eight months of monitoring (September 2023 to June 2025), the Cal Beach station

received a total of 1986 images.

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Souza, V. G., Albuquerque, M. G., Pereira de Paula, D., Leisner, M. M., Façanha, M. C., Bendô, A., Freitas, S. Finisterra, LXI, 2026, e42824

Figure 7 – Spatial distribution of users at Cal Beach on January 15, 2024, at 9:00 AM (a), 12:00 PM (b), 3:00 PM (c),

and 6:00 PM (d).

Figura 7 – Distribuição espacial dos usuários na Praia da Cal em 15 de janeiro de 2024, às 9h (a), 12h (b), 15h (c) e 18h

(d).

Source: Authors’ elaboration

Figure 8 illustrates the monthly frequency of photos submitted during the analyzed period. In

2023, 500 images were recorded in only four months of operation, with December standing out by

concentrating 220 submissions. During the first full year of monitoring in 2024, a total of 1161 photos

were received, with a notable peak in January (314 submissions).

In 2025, during the first six months, the total contributions amounted to 325 images, with

January once again representing the month of highest participation (79 photos). This outcome

highlights a significant reduction in engagement compared to the same period of the previous year,

when 805 images had been submitted.

350

314

300

250

220

200

150

79

100

50

0

Months

2023

2024

2025

Figure 8 – Number of photos received from September 2023 to June 2025 for Cal Beach.

Figura 8 – Número de fotos recebidas de setembro de 2023 a junho de 2025 para praia da Cal.

Source: Authors’ elaboration

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Figure 8a also shows that most contributions occur during the summer months (December to

March), a period characterized by the high tourist season and increased beach attendance. Although

the peak of submissions in both 2024 and 2025 occurred in January, the number of photos in 2025

was substantially lower compared to 2024. In April and May, participation levels were similar across

both years. Additionally, a marked difference is observed between December 2023 and December

2024, with submissions being significantly higher in the first year, highlighting a notable contrast for

the same period.

In terms of performance and audience engagement, the CoastSnap RS Instagram account

currently has 450 followers, having reached 2971 accounts and generated a total of 6168 impressions.

Among its followers, the gender distribution is nearly balanced (50.3% male and 49.6% female), and

the most representative age group ranges from 25 to 44 years. Although the follower base is relatively

modest, these metrics demonstrate that the project’s digital presence effectively enhances public

visibility and promotes meaningful community engagement, reinforcing the role of social media as an

important tool for awareness-raising and participatory coastal monitoring.

6.

DISCUSSION

The natural evolution of cliffs poses significant risks to residents and bathers in areas where

such formations occur, although the intensity and nature of these risks depend on the lithology of the

cliffs, which influences their stability and erosion processes (Rio & Gracia, 2009; Teixeira et al., 2014).

Understanding the dynamics of these landforms requires continuous monitoring, often involving high

costs due to the use of advanced technologies (e.g., UAVs and laser scanners) and the need for

specialized personnel to operate this equipment.

Leisner et al. (2023) identified that the sea cliffs at Pacheco Beach exhibit markedly high average

retreat rates, exceeding 2 m/year. The authors note that this erosional behavior is highly variable and

difficult to predict, as it is modulated by the seasonal climatic forcing of waves, tides, and precipitation,

as well as by the friable sandy–clayey lithology of the Quaternary deposits of the Barreiras Formation,

which enhances susceptibility to mass-movement processes. Furthermore, the authors emphasize the

importance of long-term monitoring of this coastal segment in northeastern Brazil to improve the

understanding of the mechanisms driving its local morphodynamic evolution.

Contrary to the traditional use of the CoastSnap project for coastal data extraction, the case

study developed at Pacheco Beach demonstrated additional applications beyond those previously

recognized. Over two years (2021–2024), photographs submitted by beachgoers played a key role in

understanding cliff evolution, analyzing the significant influence of rainfall on slope mass movement,

and identifying areas of higher instability that may present potential risks.

Furthermore, the study conducted by Freitas et al. (2024), which analyzed images from the

CoastSnap NE station at Pacheco Beach, identified that the walls of the houses located on top of the

Pacheco Beach cliffs are progressively approaching the cliff edge. This evidence, in the medium term,

confirms the erosive processes previously described by Leisner et al. (2023) and reinforces the

potential of CoastSnap as a low-cost, effective tool for the identification and continuous monitoring of

risk areas.

These findings open new discussions on the applications of citizen science through image

collection within the CoastSnap Project. Previous studies, such as those by Lusty (2019), Zabota and

Kobal (2020), Tavani et al. (2020), and Burningham et al. (2024), explored the feasibility of low-cost

methodologies and community engagement for monitoring cliff dynamics using smartphone cameras,

representing a strategy similar to that employed by CoastSnap NE at Pacheco Beach.

Regarding the understanding of daily beach user dynamics, although this parameter is not yet a

standard environmental indicator widely adopted by coastal managers in Brazil, it represents a

valuable tool for promoting sustainable tourism development by generating relevant information for

the proper planning and management of coastal areas, particularly those with high recreational and

tourist potential. Medeiros et al. (2016) highlight that the uneven distribution of beach users reveals

the need for spatial management to reduce congestion levels and enhance the quality of recreational

experience.

According to Silva et al. (2020), the high concentration of beach users has become a significant

issue. In the absence of effective coastal management strategies, combined with the intensification of

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Souza, V. G., Albuquerque, M. G., Pereira de Paula, D., Leisner, M. M., Façanha, M. C., Bendô, A., Freitas, S. Finisterra, LXI, 2026, e42824

mass tourism, the ecological integrity and natural resources that attract visitors can be substantially

impacted, creating a “tourism paradox” (Defeo & Elliott, 2020; Fanini et al., 2020).

In this context, data extracted from photographs submitted to the CoastSnap RS network are

promising for supporting strategies to mitigate pressures on coastal environments and optimize the

provision of services during periods of peak tourist activity. The ability to identify the daily number of

beach users, as well as their spatial distribution along the shoreline, provides valuable insights for

coastal management.

This information can guide the appropriate sizing of support infrastructure and services, such

as public toilets, waste collection containers, parking areas, and other elements essential to ensuring

a positive user experience. Additionally, such planning contributes to ecological preservation, as the

lack of adequate infrastructure can generate negative impacts; for example, the absence of public

toilets has been identified as a factor compromising bathing water quality (Araújo & Costa, 2016).

Traditionally, studies on beach user numbers require field monitoring teams, specialized

equipment, and laboratory data processing. However, the high costs involved in conventional image

collection and analysis techniques often make them economically unfeasible for many coastal

municipalities (Proença, 2024).

Given this limitation, the past decade has seen a significant expansion of low-cost coastal

monitoring alternatives. In the context of monitoring beach user dynamics, Albuquerque et al. (2024)

demonstrated the potential of CoastSnap RS images to identify beachgoers at Guarita Beach, Torres

(RS), comparing the accuracy of visitor-submitted photographs with high-resolution images

simultaneously captured by a UAV. Complementarily, Leal-Alves et al. (2022) showed the feasibility of

monitoring high-frequency variations throughout the day in beach environments using electronic

measurements and computational algorithms applied to accessible devices.

Regarding coastal management, high-frequency information is essential for understanding

relationships between socioeconomic and environmental parameters, identifying areas affected by

erosive processes, delineating risk zones, and assessing the resilience of beaches to the effects of

climate change, such as sea-level rise and increased storm activity. These data make it possible to

determine whether a beach is recovering after extreme events, which is particularly relevant in

urbanized coastal settings where infrastructure limits the natural adjustment of the shoreline, thereby

supporting the implementation of appropriate coastal protection measures when necessary. In this

sense, the results obtained with CoastSnap highlight the relevance of local community involvement in

scientific knowledge production, directly contributing to expanding available datasets and providing

valuable information to support management strategies and decision-making for the conservation of

coastal environments.

Differences in engagement observed between the Pacheco Beach station (CoastSnap NE) and

the Cal Beach station (CoastSnap RS) underscore the influence of station location on data collection

frequency. The Cal Beach station received a considerably higher number of photo submissions, largely

due to its strategic position at Morro do Farol, a viewpoint with constant visitation regardless of direct

beach use. In contrast, the Pacheco Beach station, situated in an area with lower spontaneous visitor

flow, recorded more limited participation.

Social media metrics from CoastSnap RS and CoastSnap NE further reveal differences and

similarities in community engagement, indicating that even with relatively small follower bases, both

initiatives are able to reach wider audiences, enhancing project visibility and stimulating social

participation. Moreover, as highlighted by Lins-de-Barros et al. (2022), CoastSnap methodology

strengthens Ocean Literacy by connecting the public to critical ocean-related issues and promoting

greater awareness of ocean preservation and sustainable use. However, the authors also note that

irregular public participation in data generation can hinder the consistency of contributions, creating

challenges for subsequent data analysis.

Given these dynamics, it becomes essential that the locations selected for CoastSnap station

implementation present substantial visitor flow. This condition is crucial to ensure the creation of

robust, high-frequency image databases, one of the core strengths of the CoastSnap approach.

Regardless of the specific application, CoastSnap stands out as a low-cost tool for beach monitoring

and public engagement. In a context where coastal managers require agile and accessible solutions to

address daily challenges across multiple dimensions of the coastal zone (Silva et al., 2020), the

initiative demonstrates considerable potential to deliver high-frequency information on diverse

coastal processes.

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The primary limitation identified in this study relates to the low and irregular participation of

the community in image submissions, which directly affects the temporal resolution and consistency

of the datasets generated. This challenge reinforces the need for strategic station placement,

continuous outreach, and improved mechanisms to motivate public engagement. As future

perspectives, we intend to validate the data collected from the CoastSnap stations, both for user

detection and for identifying mass-movement events on cliffs, using traditional methods with UAVs.

This comparison will allow us to assess the accuracy, reliability, and operational potential of citizen-

generated imagery as a complementary tool for coastal monitoring and risk analysis.

7.

CONCLUSIONS

Coastal monitoring, in its conventional form, employs methodologies that require extensive

logistics and continuous financial investment. However, this type of initiative often fails to engage with

society and has largely disregarded civic knowledge. In recent years, citizen science has been on the

rise in Brazil, enabling greater citizen participation in coastal data collection, which has led to a better

understanding of the dynamics and complexity of these environments.

On the northeastern coast, the submission of photographs and the mapping of mass movement

occurrences at Pacheco beach underscore the importance of studies that involve community

participation in data collection while simultaneously raising awareness of the risks associated with

cliffed beaches. The data generated from CoastSnap NE images have allowed for an accurate overview

of areas at risk of mass movements, in addition to complementing more complex studies on the

morphodynamic evolution of the cliffs.

In the southern littoral, CoastSnap photographs acquired and shared by citizen scientists proved

effective in monitoring the user density at Cal beach. In the short term, the data obtained has shown

great potential to provide almost real-time information on beach occupancy. In the long term, this

information can help coastal managers plan tourism strategies, especially during summer seasons, and

help to manage carrying capacity on the beaches.

CoastSnap, in its initial conception, was structured to characterize shoreline movement over

time. The two case studies presented open a discussion on the potential for various applications of

photographs submitted by beachgoers and coastal communities, contributing to the implementation

of new applications and sustainable coastal management strategies. Given Brazil’s extensive coastal

zone, establishing regional projects focused on citizen science contributes to the development of new

shoreline monitoring tools and supports conservation and protection efforts along the coast. Finally,

the involvement of citizen scientists has improved continuous data collection, fostering social

engagement in beach monitoring initiatives and contributing to the preservation of these

environments, while raising awareness within the participating communities.

In national and Latin American contexts, regions often characterized by limited investment in

science and environmental management, coastal monitoring based on citizen science becomes even

more relevant. In Brazil, where more than half of the population lives in coastal zones and coastal

tourism represents an important economic pillar, the availability of reliable, frequent, and low-cost

data is essential not only to support decision-making in the face of natural hazards and disasters but

also to strengthen the sustainability of tourism, ensuring the protection of the natural resources that

underpin local economies. Similarly, in several Latin American countries that face comparable

socioeconomic constraints, the adoption of accessible and replicable methodologies such as CoastSnap

can empower coastal communities, enhance environmental governance, and expand the capacity of

local managers to address the challenges associated with effective and resilient coastal management.

ACKNOWLEDGEMENTS

The authors thank the Federal Institute of Education, Science and Technology of Rio Grande do Sul

(IFRS) - Campus Rio Grande, State University of Ceará (UECE), and Federal University of Rio Grande (FURG)

for their support. This study was supported by the National Council for Scientific and Technological

Development – CNPq (Project N°. 406334/2023-4, 420516/2022-0, and Productivity PQ 309102/2022-7).

14

Souza, V. G., Albuquerque, M. G., Pereira de Paula, D., Leisner, M. M., Façanha, M. C., Bendô, A., Freitas, S. Finisterra, LXI, 2026, e42824

ORCID ID

Vitória Gonçalves Souza

Miguel da Guia Albuquerque

https://orcid.org/0000-0001-6462-6350

https://orcid.org/0000-0002-2063-492X

Davis Pereira de Paula

Melvin Moura Leisner

https://orcid.org/0000-0002-8298-7720

https://orcid.org/0000-0003-3473-6924

Matheus Cordeiro Façanha

Antonio Raylton Rodrigues Bendô

Samyra Costa de Freitas https://orcid.org/0000-0003-9747-0657

https://orcid.org/0000-0002-3161-0325

https://orcid.org/0000-0002-9742-2466

AUTHOR`S CONTRIBUTIONS

Vitória Gonçalves Souza: Conceptualization, Methodology, Formal analysis, Investigation, Resources,

Data curation, Writing – original draft preparation, Writing – review and editing and Visualization. Miguel

da Guia Albuquerque: Conceptualization, Methodology, Writing – review and editing, Visualization,

Supervision, Project administration, Funding acquisition. Davis Pereira de Paula: Conceptualization,

Methodology, Writing – review and editing, Visualization, Supervision, Project administration, Funding

acquisition. Melvin Moura Leisner: Methodology, Investigation, Data curation, Writing – original draft

preparation, Writing – review and editing. Matheus Cordeiro Façanha: Data curation, Visualization.

Antonio Raylton Rodrigues Bendô: Methodology, Software. Samyra Costa de Freitas: Data curation.

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