# Inventory of landslides affecting the coastal cliffs of Charente-Maritime (France): Typology, frequency, surface area and observation methods

Dorian Béranger, Jean-Michel Carozza, Yannick Thiery

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Dorian Béranger, Jean-Michel Carozza, Yannick Thiery. Inventory of landslides affecting the coastal cliffs of Charente-Maritime (France): Typology, frequency, surface area and observation methods. The International Rock Coast Conference, Jun 2025, Trieste (Italy), Italy. hal-05120605

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Submitted on 19 Jun 2025

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# Inventory of landslides affecting the coastal cliffs of Charente-Maritime
## (France): Typology, frequency, surface area and observation methods

Dorian BÉRANGER¹,², Jean-Michel CAROZZA¹, Yannick THIERY²

1. La Rochelle Université, UMR 7266 CNRS - LIENS, 2 Rue Olympe de Gouges, 17000 La Rochelle, France

2. BRGM, Direction Risques et Prévention / Unité Risques Côtiers et Changement Climatique, 25 Av. Léonard de Vinci, 33600 Pessac, France

Corresponding author : d.beranger@brgm.fr

<figure><img src="image_1.png" /></figure>

RÉGION
Nouvelle-
Aquitaine

<figure><img src="image_2.png" /></figure>

La Charente
Maritime

<figure><img src="image_3.png" /></figure>

cns
La Rochelle
Université

<figure><img src="image_4.png" /></figure>

Géosciences pour une Terre durable

brgm

IRC2025

a state of the art

## Context and Research Question

* There are 60km of rocky cliffs;

* There is no data on the process of erosion;

* Studying these dynamics is necessary to better adapt these coastlines to hazards.

* What are the characteristics of landslide-driven erosion on rocky cliffed coasts in Charente-Maritime?

* What is the relationship between the frequency and intensity of these landslide events?

* Which methods are best suited to monitor and understand cliff erosion dynamics ?

## Landslides types

<figure><img src="image_5.png" /><figcaption>Rockfall</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Flow</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Topple</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Flow debris</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>Slide</figcaption></figure>

● Varnes, 1978

● Dikau et al., 1996

● Hungr et al., 2014

## 3 study sites

<figure><img src="image_10.png" /><figcaption>1. La Flotte</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>2. Pointe du Chay</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>3. Pointe de Chassiron</figcaption></figure>

<figure><img src="image_13.png" /></figure>

## Method

### Fieldwork inventory

* Quaterly acquisition

* 4 photos per landslide

* Scar analysis

* Many indicators collected

* 3D information

<figure><img src="image_14.png" /></figure>

<table><thead><tr><th rowspan="2"></th><th colspan="2">Pointe du Chay</th><th colspan="2">Pointe de Chassiron</th><th colspan="2">La Flotte</th></tr><tr><th>Date</th><th>Number of landslides</th><th>Date</th><th>Number of landslides</th><th>Date</th><th>Number of landslides</th></tr></thead><tbody><tr><td>Survey 1</td><td>14/11/2023</td><td>11</td><td>21/12/2023</td><td>6</td><td>14/12/2023</td><td>19</td></tr><tr><td>Survey 2</td><td>10/04/2024</td><td>12</td><td>28/02/2024</td><td>0</td><td>15/04/2024</td><td>8</td></tr><tr><td>Survey 3</td><td>11/07/2024</td><td>2</td><td>30/05/2024</td><td>10</td><td>22/07/2024</td><td>2</td></tr><tr><td>Survey 4</td><td>10/10/2024</td><td>0</td><td>28/08/2024</td><td>3</td><td>13/10/2024</td><td>6</td></tr><tr><td>Survey 5</td><td>21/12/2024</td><td>1</td><td>18/10/2024</td><td>0</td><td>21/01/2025</td><td>24</td></tr><tr><td>Survey 6</td><td>24/03/2025</td><td>4</td><td>20/01/2025</td><td>12</td><td>16/04/2025</td><td>4</td></tr><tr><td>Survey 7</td><td>-</td><td>-</td><td>30/04/2025</td><td>7</td><td>-</td><td>-</td></tr></tbody></table>

### Photogrammetric inventory

* Half-yearly acquisition

* UAV RTK

* Agisoft Metashape

* Cloud Compare

* 3D information

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /></figure>

<table><thead><tr><th></th><th>Date</th><th>Number of images</th><th>Points density</th><th>Spatial resolution</th></tr></thead><tbody><tr><td>Pointe du Chay</td><td>06/02/2024</td><td>463</td><td>0.587 pts/cm²</td><td>1.3 cm/px</td></tr><tr><td>Pointe de Chassiron</td><td>07/10/2024</td><td>882</td><td>0.599 pts/cm²</td><td>1.29 cm/px</td></tr><tr><td>Pointe du Chay</td><td>13/03/2024</td><td>578</td><td>0.226 pts/cm²</td><td>2.1 cm/px</td></tr><tr><td>Pointe de Chassiron</td><td>28/11/2024</td><td>788</td><td>0.319 pts/cm²</td><td>1.77 cm/px</td></tr><tr><td>La Flotte</td><td>07/03/2024</td><td>782</td><td>0.101 pts/cm²</td><td>3.15 cm/px</td></tr><tr><td>La Flotte</td><td>03/12/2024</td><td>1241</td><td>0.163 pts/cm²</td><td>2.47 cm/px</td></tr></tbody></table>

### Remote sensing inventory

* Orthophotographs every four years

* Digitization of the surface area lost between two dates

* QGIS or ArcGis Pro

* Cliff-top indicator

* 2D information

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<table><thead><tr><th>Years</th><th>References</th><th>Digitization error (m)</th><th>Pixel error (m)</th><th>Orthorectification error (m)</th><th>Overall error (m)</th></tr></thead><tbody><tr><td>2003</td><td>IGN, BD<br>Ortho®</td><td>0,22</td><td>0,5</td><td>1</td><td>1,13947356</td></tr><tr><td>2006</td><td>IGN, BD<br>Ortho®</td><td>1,19</td><td>0,5</td><td>1</td><td>1,63281965</td></tr><tr><td>2010</td><td>IGN, BD<br>Ortho®</td><td>0,85</td><td>0,5</td><td>1</td><td>1,40445719</td></tr><tr><td>2014</td><td>IGN, BD<br>Ortho®</td><td>1,43</td><td>0,2</td><td>1</td><td>1,75638834</td></tr><tr><td>2018</td><td>IGN, BD<br>Ortho®</td><td>2</td><td>0,2</td><td>1</td><td>2,24499443</td></tr><tr><td>2021</td><td>IGN, BD<br>Ortho®</td><td>0,98</td><td>0,2</td><td>1</td><td>1,41435498</td></tr></tbody></table>

### Frequency-Area Distribution (FAD) analyses

* Using Landslides Area Fitting Tool v1.0

(Bounab et al., 2024)

<figure><img src="image_19.png" /></figure>

* Inverse Gamma theoretical distribution

* Number of bins : 50

* Power laws can be realized through inventories

(Dewez et al., 2013)

## Results

### Fieldwork inventory

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

### Photogrammetric inventory

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

### Remote sensing inventory

<figure><img src="image_29.png" /></figure>

<figure><img src="image_30.png" /></figure>

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>

<figure><img src="image_34.png" /></figure>

## Synthesis

Types identification

Volumes / Areas

calculation

Temporality

<table><tr><td>Fieldwork inventory</td><td>Very good</td><td>Good</td><td>Very good</td></tr><tr><td>Photogrammetric inventory</td><td>Good</td><td>Very good</td><td>Very good</td></tr><tr><td>Remote sensing inventory</td><td>Bad</td><td>Average</td><td>Average</td></tr></table>

* Remote sensing does not allow for the accurate identification of types landslides;

* All three methods face the same limitation : its impossible to determine whether some landslides are actually a multitude of events occuring in the same location;

* Photogrammetry remains the acquisition method that produces the most accurate results.

### Calculation of landslide occurrence probability

<table><thead><tr><th></th><th colspan="4">Landslide Area (m²)</th></tr></thead><tbody><tr><td rowspan="4">Probability of occurrence</td><td>0 – 1 m²</td><td>1 – 10 m²</td><td>10 – 100 m²</td><td>100 – 1000 m²</td></tr><tr><td>Inverse Gamma</td><td>19.25 %</td><td>52.28 %</td><td>21.64 %</td><td>5.2 %</td></tr><tr><td>Double Pareto</td><td>19.03 %</td><td>19.81 %</td><td>25.58 %</td><td>4.78 %</td></tr></tbody></table>

<figure><img src="image_35.png" /></figure>

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>

## Conclusion and perspectives

* The cliffs of Charente-Maritime are eroding due to landslides that displace small areas;

* The two main types of landslides are rockfalls and debris flows;

* UAV photogrammetry and fieldwork are the most suitable approaches.

* Study the temporality of probability;

* Continue taking inventory of the rest of the department using remote sensing methods;

* Continue inventories using photogrammetry and fieldwork at the remaining study sites.

## Bibliography

Dewez T.J.B., Rohmer J., Regard V., Cnudde C. (2013) - Probabilistic coastal cliff collapse hazard from repeated terrestrial laser surveys: case study from Mesnil Val (Normandy, northern France). Proceedings 12th International Coastal Symposium (Plymouth, England), Journal of Coastal Research, Special Issue No. 65, pp. 702-707.

Dikau R., Brunsden D., Schrott L., Isben M.-L. (1996) - Landslide recognition : Identification, Movement and Causes. Report n°1 of the European Commission

Bounab A., El Kharim Y., El Kharrim M., Sahrane R. (2024) The performance of landslides frequency-area distribution analyses using a newly developed fully automatic tool. Applied Geomatics, 16, 789-796.

Hungr O., Leroueil S., Picarelli L. (2014) - The Varnes classification of landslide types, an update. Landslides, 11 (2), 167-194.

Varnes D.-J. (1978) - Slope movement types and processes. In Schuster R.-L., Krizek R.-J. (Eds) : Landslides, analysis and control, special report 176 : Transportation research board, National Academy of Sciences, Washington D.-C., 11-33.

This work, financed by the Department of the Charente-Maritime and the BRGM (French geological Survey), is part of the DYN-FALAISES research project.

The DYN-FALAISES research project is led by the BRGM and financed by a coastal research agreement between the Nouvelle-Aquitaine Region and the BRGM.