# An open-source multi-sensor methodology for the mapping of mangrove structural types using remote-sensing in Madagascar

Quentin Marsal, Elodie Blanchard, Jean-François Faure, Eric Delaitre, Christophe Revillion, Gwenaelle Pennober, Fenozo Heritiana Andriamanantena, Ravaka Randrianatoandro, Thibault Catry

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

## To cite this version:

Quentin Marsal, Elodie Blanchard, Jean-François Faure, Eric Delaitre, Christophe Revillion, et al. An open-source multi-sensor methodology for the mapping of mangrove structural types using remote-sensing in Madagascar. EO for Africa Symposium 2024 (EO4Africa), Sep 2024, Frascati - ESRIN ESA Centre, Italy. . ird-04882698

HAL Id: ird-04882698

https://ird.hal.science/ird-04882698v1

Submitted on 13 Jan 2025

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# An open-source multi-sensor methodology for the mapping of mangrove structural types using remote-sensing in Madagascar

Quentin Marsal¹; Elodie Blanchard¹,²; Jean-François Faure¹; Eric Delaitre¹; Christophe Revillion¹; Gwenaelle Pennober¹; Fenozo Heritiana Andriamanantena³; Ravaka Randrianatoandro⁴; Thibault Catry¹

¹ UMR ESPACE-DEV, IRD, Univ Montpellier, Univ Guyane, Univ Réunion, Univ Antilles, Montpellier, France - quentin.marsal@ird.fr

² La TeleScop, Castelnau-le-Lez, France

³ University of Mahajanga, Madagascar

⁴ University of Antananarivo, Madagascar

## Introduction

The aim of the **SOS-MAMBO project**, funded by CNES (French Space Agency) in the frame of the TOSCA program, is to develop a new **methodological framework based on open-source multi-sensor and multi-resolution remote sensing combined to in-situ measurements to map mangroves types at fine scale**. The test site is the Bombetoka Bay, located in the North-West of **Madagascar**, at the Betsiboka River mouth in the Indian Ocean. In the Bombetoka Estuary, islets are continuously evolving as a result of erosion and accretion processes. On those islets, multiple mangroves species at different development stages can be found.

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

## Data and Method

1. Mangroves areas are extracted from **High Spatial Resolution (HSR) Sentinel-2 images** (10 meters) using a mask from the MangMap platform [1], or from a supervised Random Forest classification calculated with the IOTA2 processing chain [2], or from Global Mangrove Watch (GMW) [3].

2. Mangroves pixels of a **Very High Spatial Resolution (VHSR) Pléiades** image (0.5 m) are extracted with the HSR derived mask.

3. A textural analysis of the extracted pixels is applied with the **FOTOTEX algorithm** [4].

4. An **unsupervised k-means classification** is applied on the textural indices calculated with Fototex.

5. **Field observations** are used to **label** the results of the k-means classification.

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

**FOTOTEX** for canopy texture analysis
(modified from Teillet and al. 2021
https://doi.org/10.3390/rs13122398)

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

## Results

<figure><img src="image_5.png" /><figcaption>VHSR Masked Pléiades (Panchromatic band)</figcaption></figure>

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

<figure><img src="image_7.png" /><figcaption>Fototex Textural Analysis</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Unsupervised K-means classification</figcaption></figure>

### Field observations

* Tree density (○)

* Tree DBH (V)

* Tree height (↑↓)

* Tree crown

* Tree health

* Phenological state

* Mangroves species

<figure><img src="image_9.png" /><figcaption>Fototex Pléiades windows for each k-means classes</figcaption></figure>

<figure><img src="image_10.png" /></figure>

The field observations are used to label the unsupervised k-means classification, giving a map of the structural typology of the mangroves of the Bombetoka Estuary.

## Conclusion

We developed a framework to monitor the **mangroves typology at fine scale** with **open access remote sensing** data. Two field campaigns have been realized in 2023 and 2024, during which more than **200 field observations** of the Bombetoka mangroves phenological and geometrical data were collected. Correlations between the field observations and the **textural indices** are currently analysed, with the next step being the large scale classification mapping. Those results should be able to help mangroves **monitoring actors, stakeholders and policy makers** to improve **mangroves conservation decisions**. An early version of the processing chain is available at https://framagit.org/quentinmarsal/sos_mambo, allowing for textural analysis and unsupervised k-means classification of mangroves area.