# Monitoring the dynamics of coastal wetlands ecosystems in Brittany (France) using LANDSAT time series and machine learning

Adrien Le Guillou, Simona Niculescu

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Adrien Le Guillou, Simona Niculescu. Monitoring the dynamics of coastal wetlands ecosystems in Brittany (France) using LANDSAT time series and machine learning. Ecological Informatics, 2025, 90, pp.103303. 10.1016/j.ecoinf.2025.103303. hal-05146694

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# Monitoring the dynamics of coastal wetlands ecosystems in Brittany (France) using LANDSAT time series and machine learning

Adrien Le Guillou<sup>a,†</sup>,*, Simona Niculescu<sup>a,b,‡</sup>

## ARTICLE INFO

Dataset link: https://zenodo.org/records/15721404 (Le Guillou and Niculescu, 2025a)

**Keywords:**

Coastal wetlands

Dynamics of ecosystems

Urbanisation

Machine learning

Interlocking spatial scales

Spatial statistics

Coastal erosion

## ABSTRACT

Coastal wetlands protect against erosion, reduce flood risks, and maintain watercourses during periods of drought, which can mitigate global warming and its effects on humans. This study aims to analyse the spatio-temporal dynamics of coastal wetland ecosystems in relation to the main drivers of change urbanisation and coastal erosion in a specific region of Brittany (France) between 1990 and 2020. The study exploits the potential of satellite image time series (SITS), machine learning (ML), and Random Forest (RF) algorithms.

These algorithms enable the software to learn autonomously from multiple datasets, including Landsat 4/5 and 8 SITS archive images. The different elements or ecosystems within a dataset are classified into categories using the Corine Biotopes classification system and multi-scalar analyses. The study proposes two scales of analysis: at the scale of coastal wetlands throughout Brittany and at the local scale of two RAMSAR coastal wetlands (Mont-Saint-Michel Bay and Audierne Bay). The results revealed contrasting spatial patterns. The Audierne Bay has experienced significant urban expansion, with a 24% increase upstream, as well as coastal erosion reaching 1.63 m/year locally, with a retreat of approximately 50 m in the most affected areas during the period 1990–2020. The wetlands in this region are receding in parallel with the coastline and have slightly decreased in area over the last 30 years, with a reduction of 8%. In contrast, the Bay of Mont-Saint-Michel has seen an expansion of salt marshes. Using Landsat archives and an automated coastline detection method, we found that the area of salt marshes has increased by 36% over the last 30 years. In both nested spatial scale approaches, the proposed spatial methodology generates spatial statistics on the dynamics of key ecosystems at the designated time scale. The results provide useful information for stakeholders. These results highlight the diversity of coastal dynamics in Brittany.

## 1. Introduction

Coastal wetlands are among the most ecologically and functionally significant ecosystems on Earth. They play a pivotal role in maintaining biodiversity (Sutton-Grier and Sandifer, 2019) by acting as critical habitats for numerous species and regulating key biogeochemical cycles such as carbon sequestration and water purification (Morris et al., 2012; Hammer and Bastian, 2020). These areas also act as natural buffers (Shisler et al., 1987), protecting coastal zones from erosion and mitigating the impacts of extreme weather events (Barbier, 2013). In the Brittany region of France, the coastal wetlands encompass a rich diversity of ecosystems (Mitsch et al., 2009); they include peat bogs, salt marshes, and intertidal zones, which are integral to both local ecological stability and socio-economic activities (Bioet and Kerbiriou, 1994; Merot et al., 2003). These ecosystems are increasingly under threat, and there is an urgent need to understand their dynamics and

the drivers of change (Xu et al., 2019). Despite their importance, coastal wetlands face degradation that is driven by human induced pressures (Newton et al., 2020), such as urbanisation, agricultural expansion, and climate change, including rising sea levels (Walker et al., 1987). Previous research has documented some of these changes. However, the understanding of relevant long term spatial and temporal dynamics remains limited (Klemas, 2013). Traditional field based approaches to monitoring wetland changes are often constrained by high costs, labour intensity, and limitations in the spatial scale, leading to substantial knowledge gaps. These challenges emphasise the need for innovative methods to map, monitor, and understand the evolution of such ecosystems over time.

Satellite imagery, particularly from long-term Earth observation programmes such as Landsat (Phiri and Morgenroth, 2017), enables the consistent and systematic monitoring of landscape changes at large scales. The integration of machine learning (ML) algorithms such as

<footnote><sup>a</sup> Laboratory LETG-Brest, Université de Brest, Brest, 29200, France</footnote>

<footnote><sup>b</sup> Institut Universitaire de France (IUF), Paris, 75000, France</footnote>

<footnote>* Corresponding author.
E-mail addresses: adrien.leguillou@univ-brest.fr (A.L. Guillou), Simona.niculescu@univ-brest.fr (S. Niculescu).</footnote>
RF further enhances the capacity to classify complex and heterogeneous ecosystems (Martínez Prentice et al., 2021). Such methods offer accuracy and adaptability. Moreover, spectral indices, derived from satellite imagery, allow researchers to capture specific wetland characteristics such as vegetation health, soil moisture (Ahmad et al., 2010), and hydrological conditions. These tools collectively offer new opportunities to analyse temporal changes in coastal wetlands with precision and efficiency. In 2007, Baker et al. (2007) conducted a study using Stochastic Gradient Boosting (SGB) and Change Vector Analysis (CVA) to classify and detect changes in wetlands based on Landsat images from 1998 to 2001. In 2023, Demarquet et al. (2023) published a meta analysis that revealed that among the many studies focusing on monitoring wetlands using Landsat imagery, the scope of research has varied. Their findings indicated that inland wetlands are studied more often than coastal wetlands, with human made wetlands receiving the least attention. The analysis also highlighted that 77% of change detection methods rely on multitemporal data, while 18% utilise temporal series data. Additionally, the time scale most often used spans between 20 and 30 years, accounting for 49% of the cases studied. Studies on the evolution and degradation of coastal wetlands reveal several other key trends. Unlike the tropical coastal wetlands occupied by mangrove ecosystems, the ecosystems of temperate coastal wetlands in Europe have received relatively little attention. This is partly due to their often smaller sizes and long history of anthropogenic transformation, which has altered much of these environments (Adeli et al., 2020; Davidson et al., 2018; Mitsch and Gosselink, 2015). A substantial portion of research has focused on the Mediterranean wetlands. In 2021, Wang et al. (2021) analysed ten coastal wetland sites in North eastern Italy, including Ramsar sites along the Adriatic coast between 1984 and 2016. The study reveals that, thanks to conservation policies, land use in coastal wetlands has generally remained stable, despite anthropogenic pressures. However, these areas, particularly low lying insular wetlands, were vulnerable to flooding caused by sea-level rise. In tropical environments, research since 2020 has highlighted the continued degradation of coastal wetlands in terms of their surface areas in the Mekong Delta, based on remote sensing and supervised methods. For instance, Dang et al. (2021) reported a 129% loss of coastline and a 32% decline in mangroves that was partly due to the expansion of aquaculture ponds between 1995 and 2020, as evident from Landsat imagery. Several recent studies illustrate the growing use of remote sensing and ecological indices to monitor wetland dynamics globally. For instance, the study by Philip et al. (2024) on the Akkumal-Veli Lake Basin in India applied the Remote Sensing based Ecological Index (RSEI) to assess ecological quality changes over time using Landsat imagery and highlighted the importance of integrating vegetation, humidity, temperature, and soil dryness indicators. Other works have focused on the impact of land use changes: a meta analysis of Chinese wetlands demonstrated that reclamation activities significantly affect soil nutrient levels, altering wetland functions. Furthermore, studies such as the one carried out in the Anzali Wetland in Iran underscore the risks linked to heavy metal accumulation, underlining the need for multi parameter assessments in wetland management. These diverse approaches reflect the increasing sophistication in wetland monitoring strategies and underscore the importance of adapting them to local contexts such as the Brittany coast, where similar pressures and dynamics are at play. Zhang et al. (2023) and Amini et al. (2024) It is worth noting a slight increase in mangroves between 2013 and 2020. Over a similar but slightly longer time frame, namely 1980–2020, and using similar sensors (i.e., Landsat series), Cui et al. (2022) documented a degradation of 3020 km² of coastal wetlands in the Jiangsu Province of China; this figure represents a 42% loss of coastal wetland area in the region. Such trends have been consistently observed in this part of the world for many years (Kim, 2010; Mayer et al., 2004; Liu et al., 2018; Zhang et al., 2009; Deng et al., 2024; Zhang et al., 2020). Research on coastal wetlands has been unevenly distributed globally, with a significant concentration of studies in Asia and the

United States, where issues related to the rapid degradation of these ecosystems are particularly pressing. Our research is more closely aligned with the framework of Wang et al. (2021). In regions such as Vietnam and China, the dynamics of land impermeabilisation and the conversion of coastal wetlands for agricultural use are extreme because of rapid economic and agricultural expansion. By contrast, Brittany has already experienced extensive anthropogenic modification. Similar to the Italian coastline, the Brittany coast encompasses RAMSAR sites and is subject to coastal protection policies. The key question, therefore, is whether such measures have effectively contributed to the preservation of coastal wetlands in the Brittany region.

Previous studies of Brittany's coastal wetlands have predominantly examined either macro-scale processes such as watershed hydrology and urban expansion—or micro-scale phenomena like species-level habitat surveys. Very few have connected these scales to capture the complex interactions unique to the region's littoral interface. Rapinel et al. (2019) highlighted the necessity of multi-scalar approaches for wetland mapping at fine spatial resolutions. At this time, no research has systematically mapped Brittany coastal wetlands from 1990 to 2020 using Landsat time series analysis. This work bridges the methodological gap between coarse regional inventories and detailed habitat focused surveys, offering a replicable framework tailored to dynamic coastal environments (Rapinel et al., 2015, 2018).

This study analyses the spatiotemporal dynamics of ecosystems in coastal wetlands in Brittany between 1990 and 2020. The aim is to examine the relationship between urbanisation and coastal erosion, leveraging the potential of remote sensing (RS) and ML. Using Landsat -4/5 for the 1990 period and Landsat -8 for 2020, we apply a classification approach to detect the major trends in ecosystem dynamics. Secondly, we examine the dynamics at the local level in two RAMSAR coastal wetlands in Brittany. The study employs a simplified typology derived from the Corine Biotope classifications, focusing on key wetland categories, such as peat bogs, coastal marshes, salt marshes, intertidal zones, wet meadows, and wet forests. By analysing changes in the classes of coastal wetland areas from 1990 to 2020, the research explores the factors that drive both their decline and expansion. Human-induced pressures, such as urban development and agricultural encroachment along with natural factors such as coastal erosion, are examined as key factors that contribute to the degradation and loss of these ecosystems. The main aim is to develop an adapted and optimised method for monitoring the dynamics of ecosystems in coastal wetlands using SITS and a robust ML approach. In the proposed methodological chain, we introduce variables that are essential for understanding this dynamic: topographical variables to better delimit and pre-locate wetlands and various spectral indices are incorporated to enhance classification accuracy (Amani et al., 2018), capturing biophysical properties of wetlands. The final objective of this study is to achieve a better detection understanding, mapping and generation of spatial statistics from satellite images of the surface evolution of the main coastal wetlands ecosystems dynamics in Brittany over time. This comprehensive methodology ensures a detailed and reliable assessment of ecosystems coastal wetland dynamics over the 30-year period of study. The dual focus not only highlights the dynamic and multifaceted nature of wetland evolution while also providing valuable insights to guide conservation efforts and sustainable management initiatives.

## 2. Materials and methods

### 2.1. Study site

Brittany (France) (Fig. 1) is located in the northwest of France and is distinguished by its complex coastal geography, characterised by a rich diversity of littoral ecosystems (Le Lannou, 1950). The region has more than 2700 km of coastline, with a vast diversity of coast types (Stéphan et al., 2019) and an array of coastal wetlands, including salt marshes, mudflats, peat bogs, and estuarine environments (Bioet and Kerbiriou,
<figure><img src="image_3.png" /><figcaption>Fig. 1. Location of the study area - Brittany (France) at the national and regional levels.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 2. Illustration of Wetland Corine Biotopes typology in our study area from Landsat4-5 (USGS/NASA).</figcaption></figure>

1994). The wetlands are ecologically vital and offer critical habitats for many species, including migratory birds and unique plant communities (Le Corre et al., 2009). The diversity of these ecosystems is influenced by a several factors, including tidal dynamics, freshwater input, and the temperate maritime climate of the region (Zang et al., 2020). Coastal wetlands, represent a transition zone between terrestrial and marine environments (Traut, 2005). They provide essential ecosystem services such as water filtration, carbon sequestration, and coastal protection (Barbier, 2013) (see Fig. 1).

The coastal wetlands in Brittany are of considerable ecological importance due to their role in biodiversity conservation and ecosystem functioning (Hallégouët and Poncet, 1980). These areas serve as crucial breeding, feeding, and resting grounds for many bird species (Kantrud, 1986; Kantrud and Stewart, 1984), including those of conservation concern, such as the Eurasian curlew (Numenius arquata) and the

marsh harrier (Circus aeruginosus). Additionally, the coastal marshes and mudflats are home to a range of flora adapted to saline or brackish conditions, including halophytic vegetation such as Spartina species and salt-tolerant grasses. These ecosystems also provide significant hydrological functions, such as flood regulation and nutrient cycling, which are vital in the context of environmental stressors including climate change and coastal erosion (Nearing et al., 2004). The preservation of these wetlands is essential for maintaining the local biodiversity but also for the resilience of coastal landscapes to climatic variability (Barnaud, 1997).

Brittany's coastal wetlands are distinguished by their particular ecological characteristics, which are shaped by the region's distinct climatic and geological conditions. The intertidal zones of Brittany are influenced by a combination of high tidal ranges and strong oceanic winds, creating dynamic environments where saltwater and freshwater
meet (Sabatier et al., 2009). The salt marsh areas, are prime examples of this coastal ecology, characterised by plant species adapted to fluc- tuating salinity levels. They host specialised halophytic vegetation and serve as important breeding sites for shorebirds (Pennings and Bertness, 2001; Pomeroy and Wiegert, 2012; Vernberg, 1993). In addition to these salt marshes, the presence of peat bogs and low marshes adds to the ecological complexity of the region. These areas play an important role in carbon storage, water purification, and maintaining hydrolog- ical stability, particularly in the context of increasing climate change pressures (Barbier, 2013, 2019; Sierszen et al., 2012).

## 2.2. Wetland corine biotopes typology applied in this study

Wetlands are diverse and essential ecosystems, playing critical roles in biodiversity conservation (Gibbs, 2000), water regulation, and car- bon storage. For the classification and study of wetlands using remote sensing, we rely on the Corine Biotopes typology (European Environ- ment Agency, 1991) (Table 1). It provides a structured and detailed hierarchical framework for describing habitats and enables the con- sistent classification of wetland environments into broad functional categories, which are then subdivided into specific habitat types. In this study, wetlands are grouped into six main classes: salt marshes, continental waters, mudflats, wet prairies, humid forests, and peatlands and fens (Fig. 2). The salt marsh class comprises coastal habitats that are influenced by salinity and tidal dynamics. These include salt marshes and saline steppes, with specific subcategories such as Spartina meadows (Code 15.2) and Atlantic salt meadows (Code 15.3). These habitats are crucial for coastal protection and provide a refuge for many specialised plant and animal species. Their vegetation is highly adapted to saline conditions, contributing to sediment stabilisation and nutrient cycling in coastal zones.

The continental water category encompasses non-marine aquatic environments, such as lagoons, stagnant freshwater, and brackish or saline water bodies. Subcategories include lagoons (Code 21), freshwa- ter stagnant waters (Code 22), and stagnant brackish or saline waters (Code 23). These habitats are essential for maintaining the hydrological balance and sustaining aquatic biodiversity. Their open water surfaces and marginal vegetation zones are hotspots for species interactions and nutrient exchanges, which means they are a key target for ecological monitoring.

The mudflats class includes unvegetated sandbanks and tidal flats (Foster et al., 2013), typically located in estuarine or intertidal zones. Although they lack vegetation, these habitats are biologically active and support high densities of invertebrates, which form the base of the food web. Mudflats are also critical feeding grounds for migratory bird species. This connection links them to broader ecological networks and highlights their importance in wetland monitoring.

Wet prairies are herbaceous wetlands that are subject to seasonal flooding. They include eutrophic wet meadows (Code 37.2), olig- otrophic wet meadows (Code 37.3), herbaceous wetland edges (Code 37.4), and improved wet meadows (Code 81.2). These ecosystems provide critical services such as groundwater recharge and serve as habitats for many plant and animal species. Their vegetation structure varies depending on the availability of water and the nutrient levels, reflecting different ecological conditions that can be monitored through RS.

Humid forests, such as swamps and riparian forests, are integral components of wetland systems. They combine terrestrial and aquatic characteristics. This class includes riparian willow formations (Code 44.1), white alder galleries (Code 44.2), marshy woods of alder, willow, and bog myrtle (Code 44.9), and marshy forests of birch and conifers (Code 44.A). These forests serve as buffers and regulate the hydrolog- ical flows; they also provide critical habitats for both terrestrial and aquatic organisms. Their structural complexity and spatial distribution make them particularly interesting for habitat classification.

Finally, peatlands and fens are wetlands characterised by the ac- cumulation of organic soils and specific hydrological conditions. Sub- categories include vegetation along water edges (Code 53) as well as transitional bogs and fens (Code 54). These ecosystems play a major role in carbon sequestration due to their ability to store organic matter in peat layers over long periods. In addition, their hydrological functions are essential for regulating water availability and maintaining biodiversity under changing climatic conditions.

By utilising the Corine Biotopes framework, this study organises these wetland ecosystems into a coherent classification system, to facilitate their analysis and mapping using RS techniques. The selected typology provides a scientific structure for classification and facili- tates the aggregation of diverse wetland types into broader functional classes. This system enables a scalable approach for monitoring wetland ecosystems at both the regional and local levels (Moss and Wyatt, 1994).<fnref n="1" />

## 2.3. Evolution of urbanisation in coastal wetlands of brittany

The evolution of the wetlands in Brittany, including the estuarine systems and coastal salt marshes, has a long historical trajectory. Sub- stantial anthropogenic alterations occurred long before 1990; indeed, extensive drainage efforts for agricultural expansion had begun as early as the medieval period and intensified in later centuries. These prac- tices involved the construction of drainage systems and the systematic reclamation of marshes to convert vast areas of natural wetlands into productive farmland. This process fundamentally disrupted the local hydrological systems and diminished the extent of potential wetland habitats. The concurrent growth of urban centres, ports, and indus- trial infrastructure along the estuaries led to further fragmentation and confinement of these ecosystems (Freeman et al., 2019; Marinoni et al., 2013). Coastal zones, which historically served as hubs for trade, resource extraction, and settlement, experienced heavy pressure from demographic and economic expansion, particularly during the industrial era. By the late 20th century, these cumulative historical processes had already resulted in the loss or severe modification of many wetland areas in France (Derex, 2001, 2006). The relatively limited changes observed after 1990 must therefore be contextualised within this broader historical legacy of transformation. In many cases, the wetlands that still exist today represent highly altered systems or have been areas preserved through targeted conservation efforts. The historical context is thus also crucial for interpreting current trends and assessing the efficacy of any restoration initiatives. Furthermore, it highlights the importance of historical ecology in obtaining baselines for conservation planning. This approach can reveal the extent of past human impacts and help identify areas that have the potential for ecological recovery. Policies such as the designation of Natura 2000 and RAMSAR sites have sought to address this legacy. However, the scope of restoration is constrained by centuries of anthropogenic change, par- ticularly in regions where wetlands have been irreversibly replaced by urban or agricultural landscapes. O'Sullivan (2012) and Van de Noort (2016). Indeed, it seems more accurate to say that coastal wetlands are returning rather than increasing.

## 2.4. Evolution of coastal wetlands in brittany

The Brittany region alone accounts for 33% of the coastline in metropolitan France. Within this coastal area, 57% consists of cliffs and rocky shores, 29% sandy and silty coasts, 10% port areas and polders, and 4% muddy shores (Hallégouët and Van Vliet-Lanoë, 1989). Approximately 15% of Brittany's coastline, representing about 2800 km, is affected by erosion processes, corresponding to between 296 and 398 km of shoreline. Although significant efforts have been made

<footnote n="1">¹ https://inpn.mnhn.fr/habitat/cd Typo/22.</footnote>
<table><caption>**Table 1**

Typology and corresponding corine biotope typology with sub-categories.</caption><thead><tr><th>Typology</th><th>Corine biotope typology</th><th>Sub-category corine biotopes</th></tr></thead><tbody><tr><td>Salt marsh</td><td>15. Salt marshes, Pre-saline (Schorres), saline steppes and gypsum thickets</td><td>15.2 Spartina meadows<br>15.3 Atlantic Pre-salines</td></tr><tr><td>Continental water</td><td>2. Non-marine aquatic environments</td><td>21. Lagoons<br>22. Standing freshwater<br>23. Standing brackish and saline waters</td></tr><tr><td>Mudflats/Slikkes</td><td>Mudflats and sandbanks without vegetation</td><td>(No sub-category added)</td></tr><tr><td>Wet meadows</td><td>37. Wet meadows and megaphorbia</td><td>37.2 Eutrophic wet meadows<br>37.3 Oligotrophic wet meadows<br>37.4 Wet grassland edges<br>81.2 Improved wet meadows</td></tr><tr><td>Wet forests</td><td>44.1 Riparian forests, very humid forests and thickets</td><td>44.1 Willow riparian formations<br>44.2 Alder galleries<br>44.9 Swamp woods of alder, willow, and marsh myrtles<br>44. A Marshy forests of birch and conifers</td></tr><tr><td>Peatlands and wet marshes</td><td>5. Peat bogs and marshes</td><td>53. Water-edge vegetation<br>54. Wet marshes, peat bogs and transitional areas</td></tr></tbody></table>

since 1949 to preserve the coastal areas through protective shoreline management structures, anthropogenic pressures have continued to grow, largely due to growing tourism and real estate development along the coast (Gérard, 1999; Hénaff and Le Berre, 2003). Climate change also plays a critical role in coastal erosion as it disrupts the natural dynamics of shoreline evolution.

Furthermore, sand has become the second most exploited natural resource worldwide after water (Luijendijk et al., 2018), making it a highly sought-after material. In Brittany, the sea levels have already risen by 35 cm over the past 300 years (BRGM, 2022), and this trend is likely to continue and will compound the coastal vulnerability to erosion. These combined anthropogenic and climatic factors render coastal erosion a major threat to littoral ecosystems, which are directly impacted by both natural and human-induced processes (Blaise et al., 2015; Hénaff et al., 2018). This element represents an additional factor influencing the dynamics of coastal wetlands. Current estimates indicate that Brittany has lost 1.63 km<fnref n="2" /> in coastal areas since the 1960s.

## 2.5. Data

As mentioned, we used Landsat SITS data to conduct a temporal analysis of the evolution of coastal wetlands in Brittany over a 30-year. Specifically, we analysed data from Landsat 4/5 for the year 1990 and from Landsat 8 for the year 2020. Landsat provides a consistent and reliable source of multispectral imagery, making it particularly suitable for long-term environmental monitoring. Our choice of Landsat data was also related to historical continuity and comparability; it has employed a consistent spatial resolution of 30 m across several sensor generations. Although this resolution is not optimal for mapping fine-scale features, it remains one of the best options for regional scale analyses, such as the mapping of coastal wetlands. It also enables the identification of broader landscape patterns and land-use changes by ensuring compatibility across the different temporal datasets. The analysis uses Landsat 4/5 TM and Landsat 8 OLI/TIRS surface reflectance products from the USGS Collection 2 Level-2 (C2L2). These pre-processed datasets inherently incorporate essential atmospheric corrections (aerosol adjustment and Rayleigh scattering) and terrain illumination corrections (using a Digital Elevation Model), applied by the USGS Landsat Product Generation System (LPGS). To ensure optimal data quality, only scenes meeting stringent criteria were selected: a cloud cover threshold of <10%, coupled with visual inspection confirming the complete absence of cloud obstruction over the whole Area of Interest (AOI), which covers the full Brittany coast (Crawford et al., 2023).

Landsat 4/5, equipped with the Thematic Mapper (TM) sensor, and Landsat 8, featuring the Operational Land Imager (OLI) sensor, both provide multispectral data for distinguishing land cover types.

<table><caption>**Table 2**

Summary of Landsat SITS data for 1990 and 2020.</caption><thead><tr><th>Scene location</th><th>1990 (Landsat 4/5)</th><th>2020 (Landsat 8)</th></tr></thead><tbody><tr><td>Northwest (BD)</td><td>1987-04-28, 1989-06-20,<br>1989-08-23, 1989-11-27</td><td>2019-02-15, 2019-04-20,<br>2019-07-09, 2020-05-24,<br>2020-09-13</td></tr><tr><td>Northeast (HG)</td><td>1988-02-17, 1988-10-30,<br>1988-11-15, 1989-07-29,<br>1989-08-30</td><td>2020-02-27, 2020-05-15,<br>2019-09-18, 2019-03-26,<br>2019-07-16, 2019-05-13</td></tr><tr><td>Southwest (BG)</td><td>1988-02-17, 1988-06-24,<br>1988-11-15, 1989-07-29,<br>1989-08-30</td><td>2020-02-27, 2020-05-15,<br>2019-09-18, 2019-03-26</td></tr><tr><td>Southeast (HD)</td><td>1987-04-28, 1989-06-20,<br>1989-08-23, 1989-11-27</td><td>2019-02-15, 2019-04-20,<br>2019-07-09, 2020-09-13</td></tr></tbody></table>

These sensors capture data across multiple spectral bands, including visible, near-infrared (NIR), and shortwave infrared (SWIR) (Amani et al., 2018). This capacity makes them particularly effective for delineating wetlands and assessing any changes in vegetation and soil moisture. The temporal consistency and multispectral capabilities of Landsat data thus provide an opportunity to analyse the evolution of coastal wetlands (Demarquet et al., 2023), despite limitations in the spatial resolution (Arvor et al., 2013). These limitations are acknowledged, particularly regarding the detection of small or narrow features; however, the capability to conduct a regional-scale assessment over three decades offers invaluable insights into land-cover dynamics and the effects of natural and anthropogenic processes on coastal ecosystems (Yuan et al., 2020). We selected Landsat 5 and Landsat 8 due to their unparallelled combination of temporal depth, spatial resolution, and accessibility. These sensors offer a consistent 30 m multispectral resolution over a 30-year interval, enabling robust temporal comparisons for coastal wetland dynamics. Importantly, both datasets are freely accessible. Consequently, Landsat 5 and 8 constitute the most suitable and scientifically valid sensors for our multi-decadal classification and shoreline analysis of Brittany coastal wetlands. By leveraging the archive of Landsat imagery (Table 2), this study aims to create a comprehensive comparison of land cover in coastal wetlands in 1990 and 2020 (Table 2). The results provide important insights about the area's transformation over time and help to support the broader efforts towards wetland conservation and sustainable management.<fnref n="2" />

<footnote n="2">² Archives have been downloaded from the USGS website: https://www.usgs.gov/landsat-missions/landsat-data-access.</footnote>
<table><caption>**Table 3**
Spectral and topographic indices used in the analysis.</caption><thead><tr><th>Index name (Acronym)</th><th>Formula or description</th><th>Reference</th></tr></thead><tbody><tr><td>Normalized Difference Vegetation Index (NDVI)</td><td>(NIR - Red)/(NIR + Red)</td><td>Rouse et al. (1974)</td></tr><tr><td>Green NDVI (GNDVI)</td><td>(NIR - Green)/(NIR + Green)</td><td>Gitelson et al. (2002)</td></tr><tr><td>Enhanced Vegetation Index (EVI)</td><td>2.5 · <sup>NIR-Red</sup>⁄<sub>NIR+6-Red-7.5-Blue+1</sub></td><td>Huete et al. (2002)</td></tr><tr><td>Soil Adjusted Vegetation Index (SAVI)</td><td>(L+L<sub>1</sub>/(NIR-Red)) · (NIR+Red+L), L = 0.5</td><td>Huete (1988)</td></tr><tr><td>Normalized Difference Moisture Index (NDMI)</td><td>(NIR - SWIR)/(NIR + SWIR)</td><td>Gao (1996)</td></tr><tr><td>Normalized Difference Water Index (NDWI)</td><td>(Green - NIR)/(Green + NIR)</td><td>Gao (1996)</td></tr><tr><td>Bare Soil Index (BSI)</td><td>(SWIR+Red)-(NIR+Blue) ⁄ (SWIR+Red)+(NIR+Blue)</td><td>Rikimaru et al. (2002)</td></tr><tr><td>Topographic Wetness Index (TWI)</td><td>ln(a / tan β)</td><td>Beven and Kirkby (1979)</td></tr><tr><td>Topographic Position Index (TPI)</td><td>A · tan(β) ⁄ z</td><td>Guisan et al. (1999)</td></tr><tr><td>Topographic Ruggedness Index (TRI)</td><td>1/n · ∑<sub>i=1</sub><sup>n</sup> |z<sub>i</sub> - z<sub>i-1</sub>|</td><td>Riley et al. (1999)</td></tr><tr><td>Valley Depth</td><td>z<sub>max</sub> - z<sub>min</sub></td><td>GIS derived</td></tr><tr><td>Multi-Resolution Valley Bottom Flatness (MRVBF)</td><td>H<sub>flat</sub> ⁄ H<sub>total</sub></td><td>Gallant and Dowling (2003)</td></tr></tbody></table>

## 2.6. Methodology

To analyse the dynamics of coastal wetland ecosystems in the Brittany region, with a focus on urbanisation and coastal erosion, we employed a multisource and multitemporal methodology. Our dataset combined SITS (Landsat), spectral indices (such as NDVI and GNDVI) and topographic data (BD Alti 25 m) (Fig. 3). The primary dataset consisted of a Landsat SITS, specifically Landsat 4/5 from 1990 and Landsat 8 from 2020. The study area was subdivided into four scenes to ensure complete coverage of the region. For each scene and time point, we calculated a range of spectral indices widely used in vegetation and water-related studies. There were the Advanced Vegetation Index (AVI), the Bare Soil Index (BSI) (Rikimaru et al., 2002), the Enhanced Vegetation Index (EVI) (Huete et al., 2002), the Green Normalized Difference Vegetation Index (GNDVI) (Gitelson et al., 2002), the Normalized Difference Moisture Index (NDMI) (Gao, 1996), the Normalized Difference Vegetation Index (NDVI) (Rouse et al., 1974), the Normalized Difference Water Index (NDWI) (Gao, 1996), and the Soil-Adjusted Vegetation Index (SAVI) (Huete, 1988). These indices provided information on vegetation health, soil moisture, and water presence (Table 3).

We also employed a 25-m resolution Digital Elevation Model (DEM) from the Bd Alti<fnref n="3" /> dataset to derive topographic indices that are essential for identifying potential wetland areas based on their hydrological and morphological characteristics. These indices included the Topographic Wetness Index (TWI) (Beven and Kirkby, 1979), Topographic Position

Index (TPI) (Guisan et al., 1999), the Multi-Resolution Valley Bottom Flatness Index (MRVBF) (Gallant and Dowling, 2003), the Topographic Ruggedness Index (TRI) (Riley et al., 1999), and the Valley Depth. Once these topographical indices were calculated, we normalised each measurement as a value between 0 and 1, representing a soil moisture susceptibility gradient for each index. For example, 0 was assigned for 'wet' in the case of a valley floor for the Valley Depth index, as water will tend to run off there. Once each index had been standardised, a composite score was produced between these indices, enabling us to map the susceptibility to soil moisture. By thresholding this data, we could pre-locate any wetlands, which we then integrated into our stack using our spectral indices and satellite images. This pre-location step reduced potential confusion between wet and non-wet ecosystems, which have a high spectral similarity (e.g. wet and non-wet forests) (see Fig. 3). This methodology is based on Le Guillou and Niculescu (2025b,a).

After preprocessing the Landsat imagery, including radiometric corrections, we integrated the wetland mask with the calculated spectral indices and used training datasets that were focused on coastal areas. The training data were carefully constructed to represent the coastal wetland classes of interest, ensuring an emphasis on littoral ecosystems. The same training dataset, comprising an identical number of georeferenced samples at the same spatial locations, was used for both years to minimise temporal sampling bias. Because high-resolution 1990 land-cover archives were scarce (the only available source being Corine Land Cover in 1990, which lacked the necessary detail for accurate mapping of littoral wetlands), extensive expert-driven photo-interpretation of historical Landsat imagery was conducted. To comprehensively map the ecosystems of coastal wetlands, we adopted an estuarine approach. This phase systematically extended the analysis into estuaries to capture all potential coastal wetland areas, particularly salt marshes, and minimise the risk of omission. An RF classifier was trained and applied separately to each of the four scenes. The individual scene classifications were then mosaicked to produce a seamless and comprehensive map of the wetlands throughout the region. The RF model was configured with the following parameters:

* **200 trees**: ensures sufficient ensemble diversity to stabilise probability estimates and counter the high spectral variability of mixed pixels typical of coastal wetlands.

* **Minimum of 20 samples per node**: prevents the creation of overly specific decision rules, reducing the risk of overfitting noise in spectrally similar habitats (e.g., salt marsh vs. wet meadow).

* **Maximum depth of 15**: limits tree growth to avoid capturing spurious patterns while still allowing fine discrimination of subtle spectral differences in littoral vegetation classes.

Finally, the results were refined through postprocessing. This step included sieve filtering to remove small, isolated misclassifications as well as photo-interpretation to eliminate outlier errors, ensuring the highest possible accuracy and ecological relevance (see Fig. 4).

To control the quality of the classifications for subsequent analysis, we included a large number of randomly sampled points, independent of the training dataset, across the entire study area. We employed a stratified random sampling approach with 1000 validation points per land cover or ecosystem class, totalling 12,000 points. This method ensures a balanced representation of all classes and reduces any bias in accuracy assessment. Validation was performed using a hold-out approach with a stratified and independent validation set. Cross-validation or bootstrapping was not applied due to the large-scale nature of land use/land cover (LULC) classification and the high computational demands involved. Validation points were compared against the reference data derived from high-resolution imagery or ground truth to compute precision, recall, F₁-score, and overall accuracy (OA). Precision reflects the proportion of correctly classified samples relative to all predictions for a class. Recall, or producer's accuracy, measures the proportion of correctly classified samples relative

<footnote n="3">³ Available here: https://geoservices.ign.fr/bdalti.</footnote>
<figure><img src="image_5.png" /><figcaption>Fig. 3. Methodological framework for mapping of coastal wetlands mask using topographic index.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Methodological framework for mapping of ecosystems coastal wetlands using SITS, RF and Corine Biotope typology.</figcaption></figure>

to the total reference samples for the class. The F₁-score combines these metrics to provide a balanced measure of classification quality. The OA metric represents the overall proportion of correctly classified points. Once both classifications had been produced, change detection was performed. A pixel-by-pixel approach to compare the 1990 and 2020 classifications was used to study the major trends in changes across various ecosystems. The purpose was to gain an accurate understanding of the role played by humans in these changes, particularly through urbanisation.

The metrics are formally defined as follows:

$$Precision = \frac{TP}{TP + FP} \qquad (1)$$

$$Recall = \frac{TP}{TP + FN} \qquad (2)$$

$$F-score = 2 \times \frac{Precision \times Recall}{Precision + Recall} \qquad (3)$$

$$Overall Accuracy (OA) = \frac{\sum TP}{\sum (TP + FP + FN + TN)} \qquad (4)$$

where TP, FP, FN, and TN represent the numbers of true positives, false positives, false negatives, and true negatives, respectively.

The results show a marked improvement in classification accuracy over time. The 1990 map, based on Landsat 4/5 data, achieved a
<figure><img src="image_7.png" /><figcaption>Fig. 5. Classification performance metrics for LULC mapping using Random Forest on Landsat 5 imagery (1990) versus Landsat 8 imagery (2020).</figcaption></figure>

precision of 81%, recall of 79%, F₁-score of 78%, and OA of 79%. In comparison, the 2020 classification, derived from Landsat 8 data, achieved a precision of 88%, recall of 87%, F₁-score of 86%, and OA of 87% (Table 4 and Fig. 5). The 7% increase in overall accuracy (from 81% in 1990 to 88% in 2020) despite identical RF hyperparameters and similar training samples can be attributed to the improved spectral resolution and radiometric quality of Landsat 8 relative to Landsat 5, as well as landscape dynamics over the thirty-year interval that enhanced class separability. Consequently, intrinsic differences in sensor characteristics and temporal scene heterogeneity, rather than methodological inconsistencies, account for the observed fluctuation in precision, recall and F₁-score (Table 4). The overall accuracies of 81% (Landsat -5, 1990) and 88% (Landsat -8, 2020) align well with documented improvements in sensor performance. For instance, Mountrakis and Heydari (2023) reported that Landsat -8 classifications consistently outperform those based on Landsat TM by about 5%–7% in urban and peri-urban environments. Similarly, deep learning experiments over Manitoba showed an increase from 80.66% (Landsat -5/7) to 88.04% (Landsat -8), a gain of 7.4% attributable to enhanced radiometric resolution (Tsenov et al., 2023). Uncertainties in validation arise from factors such as geolocation errors, mixed-pixel effects, and potential inaccuracies in the reference data. Although the use of 12,000 points provides robust statistical support, classification outputs are inherently influenced by sensor limitations and the quality of the reference dataset. Moreover, assuming perfect reference data may overlook potential sources of error, particularly when relying on high-resolution imagery as a substitute for field observations.

## 3. Results

### 3.1. Spatiotemporal detection, of key trends in the dynamics of coastal wetlands ecosystems related to urbanisation at global scale

The temporal mapping of coastal wetland ecosystems and urban areas, as derived from our methodological approach, revealed a slight but measurable decrease in the extent of inland wetlands located upstream of coastal watersheds. These included wet meadows, humid forests, and continental water bodies. This decline can be directly correlated with the progression of urban sprawl along the Brittany coastline. Between 1990 and 2020, the expansion of urbanised areas was particularly pronounced with an increase of almost 14%, from 790 km² to 900 km² (Fig. 6), leading to increased soil sealing and a reduction in natural habitats. The “built-up” class (assessing urban

<table><caption>Table 4
Classification accuracy results for 1990 (Landsat 4/5) and 2020 (Landsat 8).</caption><thead><tr><th>Year</th><th>Satellite</th><th>Precision (%)</th><th>Recall (%)</th><th>F1-score (%)</th><th>OA (%)</th></tr></thead><tbody><tr><td>1990</td><td>Landsat 4/5</td><td>81</td><td>79</td><td>78</td><td>79</td></tr><tr><td>2020</td><td>Landsat 8</td><td>88</td><td>87</td><td>86</td><td>87</td></tr></tbody></table>

sprawl) in our RF classifications corresponds to impervious surfaces, primarily buildings, roads, and other artificial structures. Urban expansion was quantified by applying this classification across the entire study Area of Interest (AOI) along the Brittany coast, thereby mapping both coastal wetlands and their immediately adjacent land parcels. By calculating the change in built-up area within buffer zones upstream and downstream of mapped wetlands between 1990 and 2020, we were able to contextualise coastal wetland dynamics relative to surrounding urban growth. The impermeabilisation of soils in these regions not only disrupts hydrological processes but also exacerbates the vulnerability of upstream wetlands to anthropogenic pressures. These statistics were obtained directly by studying the areas of each classification for each land use class. A change detection study was also conducted to highlight the ecosystems most affected and the new types of land use.

Interestingly, the anticipated large-scale regression of coastal wetlands, as suggested by our review of the previous literature (Gibbs, 2000; Culver et al., 2015; Deng et al., 2024), had not occurred. In fact, recent data indicate a slight increase in the salt marshes along the Brittany coastline. This positive trend can be attributed to robust conservation measures implemented over the past decades. Key initiatives include the establishment of Natura 2000 sites (Maiorano et al., 2007), the designation of RAMSAR zones (Matthews et al., 1993), particularly in the Bay of Audierne, and the active land acquisition and preservation efforts led by the ‘Conservatoire du Littoral’. These policies have played a pivotal role in protecting and, in some cases, restoring these critical habitats. However, this overall stability masks certain localised declines (Fig. 7). For example, peat bogs and low marshes have shown signs of degradation and reduction. Furthermore, the analysis of these ecosystems is made challenging by technical limitations inherent in the Landsat 30 m resolution data, which fails to accurately distinguish such complex and fragmented habitats. The high rates of omission and misclassification compromised the reliability of this dataset. To counter the issue, we excluded peat bogs and low marshes from a detailed assessment in this study.

We opted for a pixel-based classification approach due to the 30 m spatial resolution of Landsat imagery, which limits the effectiveness
<figure><img src="image_8.png" /><figcaption>Fig. 6. Generation of spatial statistics and comparative analysis of surface changes in major coastal wetland ecosystems and urban areas from LANDSAT SITS.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Fig. 7. Mapping of ecosystems in connection with urbanisation between 1990 and 2020 based on 4 major trends.</figcaption></figure>

of object-based methods in detecting small and fragmented coastal features. Pixel-based methods ensure temporal consistency, avoid subjective segmentation parameters, and are more suitable for automated, large-scale mapping. Post-classification filtering was applied to reduce noise and improve spatial coherence. These findings underscore the importance of targeted conservation policies for coastal wetlands, as they have been proven effective for stabilising or even improving specific habitats such as salt marshes. At the same time, they highlight the need for improved data acquisition techniques, such as higher-resolution RS, to monitor vulnerable and nuanced wetland systems more effectively. This is particularly true for environments at risk from continued urban and agricultural expansion.

Our analysis revealed three dominant land change trends, with roughly 20% uncertainty in pixel classification. The results are based

on the classification metrics from 1990–2020, and the trends are quantified in Table 5 and illustrated in Fig. 7. First, urban expansion has dominated the study area, converting 418–628 km² of natural lands, primarily mesophilous grasslands which accounted for 261–391 km² or 62% of the urbanisation. Second, forest systems show divergent trajectories. Humid forests declined by a net 33 km² with a loss of 26–32 km² to wet meadows and 4–5 km² to urban areas. By contrast, dry forests expanded by 126–165 km² through regeneration. Third, the wetland reductions (41–61 km²) reflect ecological shifts rather than urbanisation, and salt marshes and wet meadows were the most affected. The approximately 20% uncertainty range is relevant to all transitions. It accounts for classification discrepancies between 1990 and 2020 and indicates conservative estimates. Overall, the patterns
## Land cover mapping Brittany (France) in 1990 Land cover mapping Brittany (France) in 2020

<figure><img src="image_10.png" /><figcaption>Fig. 8. Comparison of spatial dynamics of coastal wetland ecosystems in connection with urbanisation by Application of RF classification.</figcaption></figure>

<caption>**Table 5**</caption>

The generation of spatial statistics concerning the dynamics of the main ecosystems in relation to urban areas.

<table><thead><tr><th>Change process</th><th>Area range<sup>a</sup></th></tr></thead><tbody><tr><td><strong>New Urban Areas</strong></td><td>to</td></tr><tr><td>From Grasslands</td><td>260.6 to 390.8 km<sup>2</sup></td></tr><tr><td>From Wetlands (All ecosystems)</td><td>25.0 to 37.6 km<sup>2</sup></td></tr><tr><td>From Forests</td><td>20.1 to 30.1 km<sup>2</sup></td></tr><tr><td>From Other Covers</td><td>112.9 to 169.3 km<sup>2</sup></td></tr><tr><td><strong>Total Built-up Expansion</strong></td><td>418.6 to 627.8 km<sup>2</sup></td></tr><tr><td><strong>Wetland Decrease</strong></td><td>to</td></tr><tr><td>To Urban Areas</td><td>25.0 to 37.6 km<sup>2</sup></td></tr><tr><td>To Other Land Covers</td><td>15.8 to 23.7 km<sup>2</sup></td></tr><tr><td><strong>Total Wetland Reduction</strong></td><td>40.8 to 61.3 km<sup>2</sup></td></tr><tr><td><strong>Forest Dynamics</strong></td><td>to</td></tr><tr><td>Wet Forest Loss</td><td>33.0 to 39.6 km<sup>2</sup></td></tr><tr><td>Non-humid Forest Gain</td><td>125.7 to 165.4 km<sup>2</sup></td></tr></tbody></table>

Ranges reflect ±20% uncertainty in pixel classification, derived from average classification accuracy metrics for 1990 and 2020.

highlight the growing urban pressure on grasslands, and the changes in forests and wetlands reveal complex ecological dynamics (see Fig. 8).

## 3.2. Spatiotemporal detection, of dynamic of coastal wetland ecosystems in connection with urbanisation and coastal erosion at local scale (Mont-Saint-Michel Bay and Audierne Bay)

To understand the dynamics of coastal wetlands between 1990 and 2020, and to analyse the processes that influenced them, we selected two study areas. This enabled a focused analysis of two RAMSAR sites. At the regional scale, encompassing the whole of Brittany, little change was observed in the wetland surface area. There was a noticeable increase in the extent of salt marshes, which is a positive development for the ecosystem services they provide. However, this does not necessarily mean that all coastal wetlands in Brittany are in good ecological health; neither does it rule out the possibility that natural and/or anthropogenic processes are affecting the marshes to some degree.

The first case study focuses on the Brittany side of Mont-Saint-Michel Bay (Fig. 9) and a small part in the Normandy region. Classification analyses revealed a 36.8% increase in the surface area of salt marshes over 30 years. Starting from 1990, the marshes expanded from 19 km² to 26 km². This positive development was correlated with several landscape and natural area protection measures that were implemented in this territory. Mont-Saint-Michel Bay is highly attractive (with Mont-Saint-Michel being the second or third most visited monument in the world). Public policies in this area have encouraged coastal preservation efforts. This increase also indicates that of the 15 km² net growth in all salt marsh surface across Brittany (i.e along its 2700 km of coastline), nearly half (46% or 7 km²) occurred solely within the Mont-Saint-Michel Bay area. The increase in the surface area of salt marshes in the Bay of Mont-Saint-Michel between 1990 and 2020 bears witness to the ecological resilience of this ecosystem and the potential effectiveness of the protection policies put in place. This development is part of the project to re-establish the maritime character of Mont-Saint-Michel, carried out between 2009 and 2014,<fnref n="4" /> which has restored the natural dynamics of the tides by removing structures such as the dyke-road. At the same time, international recognition of the bay as a Ramsar site since 1994, and its inclusion in the Natura 2000 network through sites FR2500077 and FR2510048,<fnref n="5" /> have strengthened measures to preserve wetlands and specific habitats such as herbs and salt marshes. These statutes have encouraged more rigorous ecological management, creating conditions conducive to the extension of salt marshes and the protection of halophilic plant species such as the puccinellie. Although this territory contains the region's most remarkable wetland, the concentration indicates an imbalance within the broader territory and does not necessarily reflect the overall dynamics of coastal wetlands across Brittany.

In contrast to the pronounced expansion of salt marshes potentially driven by conservation policies in the Mont-Saint-Michel Bay—wetland

<footnote><sup>a</sup> Minor transitions (<10 km²) aggregated into “Other Covers”.</footnote>

<footnote n="4">⁴ More information here: https://www.geosoc.fr/metiers-formations/domaines-d-activites/environnement-patrimoine/pour-en-savoir-plus/le-littoral/253-projet-de-retablissement-du-caractere-maritime-du-mont-saint-michel-msm/fil e.html.</footnote>

<footnote n="5">⁵ More information here: https://inpn.mnhn.fr/site/natura2000/FR2510048</footnote>
<figure><img src="image_11.png" /><figcaption>**Fig. 9.** Focus on the Mont Saint-Michel bay: Analysis of Salt Marsh dynamics.</figcaption></figure>

dynamics in Audierne Bay appear more subdued. There, localised pres-
sures such as urban sprawl and coastal retreat have contributed to a
slight net decline in wetland surface area. The second focus was on
Audierne Bay (Fig. 10). Here, statistical analyses indicated an approxi-
mate 8% reduction in the total surface area of wetlands (encompassing
all ecosystems, including salt marshes, wet meadows, wet forests, peat
bogs, and lowland freshwater marshes) in the past 30 years. This
decline is not particularly significant; in addition, a classification preci-
sion margin of 10% to 20% for certain categories means it may be even
smaller. Nonetheless, this case study illustrates that although wetlands
at the regional scale show little change overall, with mainly localised
expansion, the trends were not uniformly observed across all areas.

Audierne Bay is designated RAMSAR site, and several factors neg-
atively impact the health of the wetland in the area. The combination
of urban sprawl with a 24% increase in urbanised areas upstream of
the wetland and coastal erosion downstream creates a dual pressure.
Based on Landsat SITS archives, we estimated that between 1990 and
2020, certain parts of the wetland eroded at a rate of 1.63 m per
year. This meant almost 50 m of coastline were lost over 30 years
in the most highly eroded areas. The calculation of coastal change
included an automated shoreline extraction that was based on the lost
area method (Pierson et al., 2025), which ensures the reproducible
monitoring of shoreline dynamics. To quantify shoreline evolution, we
extracted the shoreline from the two land cover classifications by iden-
tifying the boundaries of terrestrial land cover classes adjacent to the
intertidal zone. We then implemented a shoreline change detection tool
to calculate annual surface changes over time. Technical specifications
are detailed in the supplementary documentation.<fnref n="6" /> Although the wet-
land retreats and adapts to the expanding coastline, it is increasingly
constrained by expanding urban areas. This dual threat implies that
the wetlands will be at risk of further degradation over time if these
processes intensify.

In addition, the growth of urban areas in general, and in this
case upstream of the coastal wetland catchment area, can also be
problematic. The reason is that polluting waste may be discharged
into the wetland's hydrological cycle (Lundqvist and Falkenmark, 2000;

Rodriguez-Hernandez et al., 2013; Jacobson, 2011). Combined with
certain agricultural practices, particularly intensive pig farming in Brit-
tany, such pollution can affect the water quality. Usually, coastal
wetlands have the role of purifying water before discharging it into the
sea (Merchán et al., 2013; Mirenge et al., 2023; Verhoeven and Setter,
2010).

4. Discussion

Our study reveals a slight increase in the salt marshes of Brittany,
with a net gain of 16 km² over 30 years, primarily concentrated mainly
in the Mont Saint-Michel Bay. By contrast, a slight overall decrease in
coastal wetlands was observed across the region, encompassing various
ecosystems. Ecosystems were not equally affected; for instance, there
was a particularly pronounced decline in upstream forested wetlands.
It is important to interpret the results of this macro-scale analysis
with caution because the processes that drive wetland degradation or
improvement can be highly localised. Notable examples include the Bay
of Audierne, the Mont Saint-Michel Bay, the Bay of Saint-Brieuc, and
the Crozon Peninsula.

4.1. *Comparison with other studies*

The existing literature on coastal wetlands, such as the studies
by Wang et al. (2024), Zang et al. (2020), Wang et al. (2019), Camilleri
et al. (2017), and Li et al. (2018), has focused mainly and predomi-
nantly focuses on the wetlands themselves, limiting their analysis to the
AOI strictly defined as the wetland zone. These studies have tended to
overlook the surrounding areas, which play a crucial role in shaping
the wetland dynamics. Our study introduces an efficient and scalable
methodology that not only characterises the coastal wetlands but also
integrates the adjacent areas, encompassing both downstream and up-
stream zones. This broader scope enables a more holistic understanding
of the environmental and anthropogenic drivers that affect wetland
ecosystems.

A major innovation in our approach lies in its scalability across
spatial scales. By incorporating altimetric data, our approach can be
adapted to study regions of varying size from small local zones to larger
regional landscapes. This flexibility ensures that the methodology can

<footnote n="6">⁶ https://letg.pages.in2p3.fr/qgis-plugin-coastline-change/doc/.</footnote>
<figure><img src="image_12.png" /><figcaption>Fig. 10. Coastal wetlands ecosystems evolution in Audierne bay: Urban expansion and coastal erosion dynamics between 1990 and 2020.</figcaption></figure>

be applied effectively for different geographic contexts. Researchers may focus on a single wetland system or could assess the wetland dynamics in a broader coastal region. This modularity provides an important advantage over previous studies, which have often been constrained by the scale of their analysis.

Another key distinction of our work is its temporal analysis. While many earlier studies relied on contemporary satellite imagery for static classification, our research utilised the Landsat SITS archive data spanning from 1990 to 2020. This extensive temporal coverage enabled a comprehensive evaluation of wetland evolution by tracking the changes in surface area over three decades. Mousazadeh et al. (2015) conducted a similar temporal study of the Anzali coastal wetland in Iran using multi-temporal Landsat imagery, along with some use of altimetric data. However, their approach was limited to Landsat 8 for recent classifications. Earlier cartographic work had already been conducted, and it did not achieve the same level of discrimination across coastal ecosystems. Our comparison highlights the efficiency of the proposed methodology, which integrates data from both Landsat 4/5 and Landsat 8 data, despite the spectral limitations of the earlier sensors. By leveraging this full temporal range, our study provides a more thorough and consistent assessment of wetland evolution over time. Moreover, the inclusion of altimetric data and additional sensors enhances the accuracy and completeness of the results. This comprehensive approach distinguishes our work from previous studies.

The method proposed in this study for detecting wetland and non-wetland areas also introduces an original perspective for understanding wetlands by integrating altimetric data and topographic indices. Topographical indices are useful to pre-locate potentially wet areas. By adding these variables, it is possible to produce an initial map of the wetlands. Once we have this initial pre-location known, it can be added directly to the time series, which is then implemented in the RF. By incorporating satellite data, our study establishes a synergistic relationship between altimetric information and optical satellite imagery. This combination significantly reduces the confusion between wetland-related ecosystem classes; for example, it enables an easier distinction between wet meadows and mesophilic meadows, or between wet forests and non-wet forests. The integration demonstrates the potential of combining different data sources to improve the classification accuracy for complex ecosystems.

Unlike previous studies, which often focus solely on classifying land cover within pre-defined wetland boundaries, this approach accounts for the surrounding land use and land cover. This broader perspective provides valuable insights into the dynamics of coastal wetlands by integrating their environmental context, offering a significant improvement for understanding wetland processes and interactions with adjacent ecosystems. Additionally, the use of temporal data in this study enabled the assessment of coastal wetland evolution over a 30-year period. These findings, despite certain limitations, provide valuable insights into the temporal and spatial dynamics of coastal wetlands in Brittany. They also highlight the factors that influence these ecosystems over time, offering a clear understanding of how both natural and anthropogenic factors affect the evolution of coastal wetlands.

Finally, this study presents a reproducible methodology that can be applied to other regions using different datasets, including alternative satellite imagery or more advanced algorithms. This flexibility underscores the broader applicability of the approach. One of the strengths of this study is its applicability across a wide range of spatial resolutions, from Landsat 4/5 imagery to very high-resolution aerial photographs (30 cm to 20 cm spatial resolution). All that is required are a Digital Elevation Model and optical satellite data; once these are acquired, topographic indices can be derived and, to some extent, spectral indices as well. Moreover, the RF classifier can be implemented with minimal modification regardless of the specific satellite data employed. In terms of future feasibility, both the increasing availability of high-resolution datasets and advances in computational resources render the deployment of these methods increasingly practical for a variety of applications. Overall, our study paves the way for further research on wetland dynamics and efforts to support conservation and land management in diverse geographical contexts.

## 4.2. Prospects for improvement

This study, based on Landsat 4/5 and 8 SITS (archival imagery) and an RF classification algorithm, offers valuable insights about the evolution of coastal wetlands in Brittany. It also highlights avenues for enhancing future research to improve both the spatial and temporal resolution of such an analysis. One significant improvement could be
the integration of advanced deep learning techniques, such as Convolutional Neural Networks (CNNs) and U-Net architectures. Similar work has already been conducted on monitoring coastal wetlands of the Crozon peninsula, incorporating a CNN but with higher-resolution data (Le Guillou et al., 2023). Another study on the fusion of High resolution (HR) and Very High Resolution (VHR) data for the monitoring of coastal wetlands in Brittany provided an additional case study of the Crozon Peninsula (Le Guillou et al., 2024). These methods have been shown to be effective for RS applications, particularly in capturing complex spatial patterns and fine spectral details that are critical for accurately delineating wetland boundaries. Deep learning was not selected for this study due to certain limitations in the dataset. Specifically, the resolution of Landsat imagery and the complexity of coastal ecosystems in Brittany resulted in a highly imbalanced training dataset, particularly for salt marshes and peat bogs, as well as an overall small sample size (as with data augmentation). However, deep learning could be a good solution where a large dataset enables sufficient training and data augmentation is possible. The use of adapted and advanced architecture to characterise complex shapes such as wetlands could potentially improve the results by enabling the use of images with better resolution. Deep learning is well suited to finer scales and large volumes of data. This methodology is inherently adaptable to enhancements leveraging higher-resolution imagery and advanced algorithms. First, integrating deep learning frameworks—such as convolutional neural networks (e.g., U-Net, ResU-Net, DeepLabV3) trained on high-resolution datasets from Sentinel-2 (10 m), SPOT-6/7 (1.5 m), or Pléiades (0.5 m) could significantly improve classification accuracy. Recent studies demonstrate that deep architectures outperform traditional RF. models for wetland mapping: for example, Gonzalez-Perez et al. (2022) found U-Net surpassed RF in multispectral + LiDAR data contexts, while Papoutsis et al. (2023) benchmarked EfficientNet and Wide Residual Networks on Sentinel-2, demonstrating superior F-Score performance compared to standard CNNs. Building a large training dataset from high-resolution images would capitalise on these architectures potential, enhancing both spatial detail and class discrimination in coastal wetland environments (Mahmoud et al., 2025; Onojeghuo and Onojeghuo, 2023; Govil et al., 2022; Zhang et al., 2019; Haq et al., 2021; Yadav et al., 2022).

Another promising avenue for enhancement involves incorporating other satellite images with a higher spatial resolution. When available, these images could be correlated with Landsat imagery to create a synergistic approach that enhances the spatial resolution of the classification (with super-resolution technique by example). By combining the high spatial detail of very high resolution images with the temporal consistency of Landsat data, this method would allow for more accurate mapping of smaller or fragmented wetland features that might otherwise be overlooked due to the lower resolution of satellite images. This approach could be particularly beneficial in regions with complex land cover, where distinguishing between wetlands and other land types is challenging. For example, in areas where certain ecosystems are undergoing worrying changes or are of scientific and/or economic interest. Our study relied on Landsat data from the 1990s, which offers limited resolution in both space and time. The emergence of high-resolution satellite imagery in recent years opens up new possibilities for more detailed analyses. With the availability of Sentinel-2 imagery, which provides a finer spatial resolution and more regular revisit times, as well as commercial high-resolution satellites such as WorldView, it is now possible to conduct more detailed temporal studies of coastal wetlands has significantly improved. The increased availability of such data since the 2010s (Hiriart and Saleh, 2010; Haq, 2022) will enable more frequent and precise monitoring of wetland dynamics, including the detection of subtle changes and the ability to monitor smaller-scale processes that were previously undetectable with Landsat data alone.

The limitations of using archival data from 1990 remain a challenge in the diachronic temporal analysis of coastal wetland changes. As newer satellite systems provide more frequent and higher-quality data,

future studies will be better equipped to analyse long-term trends with greater temporal resolution. This will make it increasingly easier to track the dynamic changes in these ecosystems over time and provide more accurate projections of their future evolution in response to both anthropogenic and natural forces. The use of Synthetic Aperture Radar (SAR) images alone, or in synergy with optical imagery, can also be an asset for detecting coastal wetlands. SAR data provide unique advantages for wetland monitoring, particularly for detecting surface water and soil moisture regardless of the weather conditions. Radar signals (e.g., Sentinel-1 C-band) penetrate cloud cover and interact differently with water surfaces (low backscatter) versus wet vegetation (high backscatter from double-bounce effects). This distinction enables reliable mapping even in cloudy regions. When combined with optical imagery (e.g., Sentinel-2), SAR improves wetland classification accuracy by distinguishing inundated vegetation from dry land. SITS analysis further captures seasonal hydrological changes, making radar an interesting tool for long-term wetland conservation studies.

### 4.3. Limits

While Landsat satellite imagery has been valuable for large-scale environmental monitoring, its relatively coarse spatial resolution can pose a challenge when applied to Brittany's fine-grained coastal ecosystems. With a pixel size of 30 m, the Landsat sensor is often insufficient to capture the complexity of the small and fragmented habitats that are typical of this region. Many coastal ecosystems in Brittany, such as peatlands and low marshes, can span only a few hundred square meters, making them smaller than one or two pixels in Landsat imagery. As a result, these habitats may be underrepresented, misclassified, or entirely omitted from an analysis.

This issue is particularly evident in the case of peatbog, where their unique characteristics are often confused with other wetland types, such as humid forests or wet meadows. Such misclassifications arise because the micro-characteristics that distinguish peatlands, such as their specific vegetation composition, water saturation, and soil properties, are not discernible at Landsat resolution. The exclusion of peat bogs and low marshes, necessitated by Landsat's 30-m resolution limitations in achieving sufficient classification accuracy for these habitats, may introduce two key biases. First, it risks underrepresenting ecologically significant wetland components that disproportionately contribute to carbon sequestration and hydrological regulation. Second, this omission could artificially homogenise landscape connectivity assessments, potentially leading to overestimates of fragmentation trends in adjacent habitats and underestimates of ecotonal transition zones. To mitigate these limitations in future work, synergistic approaches could be implemented, such as integrating Sentinel-1 SAR data to exploit differential backscatter responses from saturated peat soils, or leveraging targeted VHR imagery (e.g., WorldView-3/Pléiades) to calibrate Landsat time-series classifications within key subregions during hydrologically sensitive seasons. This aspect impedes the ability to accurately discriminate between habitat types, especially in areas where ecosystems are tightly interwoven or occur in small patches.

The coarse resolution not only limits the precision of habitat mapping, but also affects the reliability of assessments about the extent of a wetland and its condition. Such inaccuracies can hinder the evaluation of conservation policies and obscure critical habitat trends, which may lead to underestimation of the need for restoration or protection measures. To address these limitations, future studies should consider incorporating higher-resolution RS data, such as imagery from Sentinel-2 which has a 10-m resolution.

Except for peatbog, the coastal wetland ecosystems in Brittany are well classified using Landsat imagery, provided that specific conditions are met. Coastal salt marshes, for instance, are reliably detected when their surface area is sufficiently large, as their vegetation structure and spectral characteristics are distinct enough to be captured by the sensor's 30-m resolution. Similarly, humid forests and wet meadows are
generally distinguishable because of their rather homogeneous spectral signatures, which allow for effective differentiation at this spatial scale. For tidal flats and intertidal zones, such as mudflats and sandflats, classification is highly accurate when sufficient temporal data is available to account for tidal variability. Multi-temporal Landsat datasets, capturing areas at different stages of the tidal cycle, enhance the detection of these dynamic habitats by providing a more comprehensive view of their extent and conditions.

Furthermore, continental water bodies, such as lakes and large ponds, do not generally pose classification challenges as long as their surface area exceeds the minimum detection threshold of the sensor. The relatively straightforward spectral contrast between water and surrounding terrestrial features usually ensures reliable identification. These observations highlight the importance of habitat size and temporal coverage in maximising the utility of Landsat data.

## 5. Conclusion

The research provides in this paper a detailed and reliable assessment of the dynamics of coastal wetlands in Brittany over a period of 30 years (1990–2020). The analysis employed Landsat-4/5 images for the period 1990 and Landsat-8 for 2020, combined with an RF classification approach. The methodology proved to be highly effective and suitable for studying Atlantic European temperate coastal wetlands. It integrates spectral indices and topographic data into an ML algorithm, applied on a nested spatial scale and in relation to the evolution of urbanisation and coastal erosion (partially). The proposed methodology reduced the classification errors among similar land cover types, such as the distinction between wet meadows versus mesophilic meadows or between wet forests versus non-wet forests. This effective and original synergy improved the accuracy and ecological relevance of the results, and represents a significant step forward in wetland monitoring approaches.

The study successfully highlights the dual dynamics of wetland systems in Brittany: the decline of certain ecosystems and the expansion or stabilisation of others. The use of temporal datasets allows for the detection of key processes driving wetland evolution, including anthropogenic pressures such as urban sprawl and agricultural expansion, as well as natural factors like coastal erosion. For instance, while the surface area of inland wetlands, such as wet forests and meadows, has decreased due to urban encroachment and soil sealing. By contrast, the salt marshes have experienced localised increases, particularly in areas like Mont-Saint-Michel Bay. These findings highlight the multifaceted nature of wetland changes which are shaped by both degradation and preservation dynamics.

Despite relying on Landsat imagery (with its inherent resolution limitations), the study achieved meaningful results that contribute to a better understanding of wetland evolution in complex ecosystems like Brittany's coastal areas. The methodology demonstrates a balance between technical feasibility and ecological insight. It also offers a replicable framework that can be adapted to other regions. Furthermore, our work underscores the importance of historical ecology as a context for understanding certain trends, by revealing how centuries of anthropogenic modification have shaped the current state of the wetlands. Climate change has further amplified these dynamics. Rising sea levels and intensified storm surges increasingly threaten coastal wetlands. Nonetheless, these ecosystems continue to play a critical role, acting both as carbon sinks to mitigate climate change while also creating natural buffers against coastal erosion and flooding. Their degradation could trigger feedback loops that would exacerbate both the loss of biodiversity and climate vulnerability. In a context for climate change, recent projections of sea level rise (SLR) by the Inter-governmental Panel on Climate Change (IPCC)<fnref n="7" /> estimate an increase

ranging from 0.6 to 1.1 m by 2100 (Khojasteh et al., 2021; Doughty et al., 2019; Lizcano-Sandoval et al., 2024), with more recent studies suggesting a broader range of 0.5 to 1.9 m, emphasising the potential for extreme scenarios. This rise is expected to accelerate the salinisation of coastal lands and lead to the permanent inundation of low-lying coastal areas, significantly altering natural habitats. Coastal wetlands may be able to migrate inland to higher ground if suitable space is available; for instance, studies along the U.S. Gulf Coast have modelled such migrations under three SLR scenarios (0.5 m, 1.0 m, and 1.5 m). However, in highly urbanised regions, this migration is often blocked, resulting in “coastal squeeze” and the loss of these ecosystems. A synthesis of current research concludes that inland migration alone will be insufficient to offset the loss of coastal wetland areas, due to both topographical and urban development constraints (Borchert et al., 2018a,b).

The spatial and statistical information generated by this research could contribute to the improvement of conservation efforts and the sustainable management of coastal wetlands in Brittany (France). By identifying the drivers of wetland change and their spatial patterns, this work provides a valuable basis for targeted preservation initiatives. Furthermore, the study demonstrates the potential benefits of integrating data from multiple sources and ML to monitor wetland dynamics effectively, paving the way for further advancements in RS methodologies. As new technologies, such as higher-resolution satellite imagery and deep learning algorithms, become more accessible, the framework established here can be refined and expanded to support more detailed and accurate assessments. The concepts presented here can be applied to wetland systems across the world.

## CRediT authorship contribution statement

**Adrien Le Guillou:** Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Simona Niculescu:** Validation, Funding acquisition, Formal analysis, Conceptualization.

## Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Adrien Le Guillou reports financial support and article publishing charges were provided by University of Western Brittany. Simona Niculescu reports financial support was provided by University of Western Brittany. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

All materials, including data and codes related to our findings, are provided as follows. The code, data and metadata of Landsat images is available here: https://zenodo.org/records/15721404 (Le Guillou and Niculescu, 2025a).

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