# Monitoring Olive Tree and Identifying Olive-Growing Formation Using Very High Satellite Imagery: A Case Study of the Ouezzane Province in Morocco

Auréa Pottier, Jean-François Girres, Julien Blanco, Chaima Dalous, Joris

Vallier, Cosma Zydek, Eliott Yvetot, Younès Hmimsa

► To cite this version:

Auréa Pottier, Jean-François Girres, Julien Blanco, Chaima Dalous, Joris Vallier, et al.. Monitoring Olive Tree and Identifying Olive-Growing Formation Using Very High Satellite Imagery: A Case Study of the Ouezzane Province in Morocco. Living Planet Symposium, Jun 2025, Vienne (Austria), Austria. hal-05169929

HAL Id: hal-05169929

https://hal.science/hal-05169929v1

Submitted on 18 Jul 2025

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# Monitoring Olive Tree and Identifying Olive-Growing Formation Using Very High Satellite Imagery : A Case Study of the Ouezzane Province in Morocco

Auréa Pottier¹, Jean-François Girres²,³, Julien Blanco¹, Chaima Dalous⁴, Joris Vallier³, Cosma Zydek³, Eliott Yvetot³, Younès Hmimsa⁴

1. SENS, IRD, CIRAD, Univ de Montpellier Paul Valéry, Univ Montpellier - aurea.pottier@ird.fr

Reunion, Univ Antilles, Univ Avignon

3. Univ de Montpellier Paul Valéry

4. TEDAEEP Team Research, Abdelmalek Essaâdi University, Polydisciplinary

Faculty of Larache, Larache, Morocco

## Introduction

In Morocco, olive groves are a predominant feature of many agricultural landscapes, resulting from both a long agricultural history and more recent national programs such as the Green Morocco Plan (Plan Maroc Vert). Within this context, the ClimOliveMed and Future for Olive projects aimed to test a deliberative approach involving scientists, farmers, and olive sector stakeholders to identify desirable solutions and future scenarios for olive cultivation. From an environmental perspective, the project pursued two main objectives: (i) to identify the different types of olive groves, and (ii) to assess whether environmental factors — such as topography — influence their spatial distribution. Using very high-resolution Pléiades satellite imagery (2023), we developed a methodology combining supervised classification with spatial analysis based on geometric features, in order to map the different types of olive groves and examine the correlation between topography and the distribution of olive cultivation. For this study, we focused on the site of Bni Quolla (covering an area of 192 km²) as a pilot site to validate the method prior to its application at the scale of the Ouezzane province.

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

## Data & Method

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

1) **Classification of Pléiades** images using SVM algorithm to produce a **5-class land-cover map** (tree, other vegetation, bare soil, built area and open water)

<figure><img src="image_3.png" /><figcaption>(a) FS</figcaption></figure>

2) **Extraction of the « tree class »** after validation of the classification

<figure><img src="image_4.png" /><figcaption>(b) LG</figcaption></figure>

3) **Spatial and geometric rules** (distance between trees, polygon size, Thiessen polygons...) are applied to distinguish **3 types of olive groves**:

- (a) **forests and shrublands, FS** (wooded and shrubby areas with continuous tree canopy cover, typically forest or matorral habitats characterized by the presence of wild olive trees (Olea europaea var. sylvestris))

- (b) **linear groves, LG** (wooded areas where tree canopies are distinct and aligned in rows or follow the topography, usually more or less recent olive plantations)

- (c) **non-linear groves, NLG** (wooded areas with distinct trees but without a geometric pattern, usually more ancient olive groves)

<figure><img src="image_5.png" /><figcaption>(c) NLG</figcaption></figure>

4) **Analysis of the spatial distribution** of olive groves in relation to **slope gradients** derived from SRTM data

## Results

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

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

### Validation (40 plots for each class)

- FS: 82,5% (33 out of 40)

- LG: 85% (34 out of 40)

- NLG: 75% (30 out of 40)

### Total area of olive-grove formation detected

- FS: 30.6 km²

- LG: 25.35 km²

- NLG: 10.9 km²

### Influence of slope gradient on the spatial distribution of olive-grove formation

- FS in steep slope

- LG and NLG in moderate slope

- Non-wooded areas in gentle slope

## Conclusion

* Very high-resolution satellite imagery is effective for mapping different types of olive groves

* The method requires improvement to reduce misclassification and under-detection, particularly of small or young trees

* Integrating texture indices could be a promising approach to improve the method

* The methodology will be extended to the entire province

## Acknowledgments

This project was funded through Labex AGRO 2011-LABX-002, project n°2003-001 (under I-Site Muse framework) coordinated by Agropolis Fondation.

This work was made possible thanks to the DINAMIS platform - the French national facility for institutional procurement of very high-resolution (VHR) satellite imagery - which provided the Pléiades data.
https://dinamis.data-terra.org/en/homepage/

<figure><img src="image_8.png" /></figure>

Espace DEV

OBSERVATION SPATIALE, MODELES & SCIENCE IMPLIQUEE

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

sens
savoirs environnement sociétés

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

<figure><img src="image_11.png" /></figure>

Institut de Recherche pour le Développement FRANCE

<figure><img src="image_12.png" /></figure>

PAUL VALERY
MONTPELLIER 3

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

agropolis fondation

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