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iForest - Biogeosciences and Forestry

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Assessing the influence of different Synthetic Aperture Radar parameters and Digital Elevation Model layers combined with optical data on the identification of argan forest in Essaouira region, Morocco

El Houcine El Moussaoui   , Aicha Moumni, Abderrahman Lahrouni

iForest - Biogeosciences and Forestry, Volume 17, Issue 2, Pages 100-108 (2024)
doi: https://doi.org/10.3832/ifor4183-016
Published: Apr 24, 2024 - Copyright © 2024 SISEF

Research Articles


Forest resource conservation necessitates a deeper understanding of forest ecosystem processes and how future management decisions and climate change may affect these processes. Argania spinosa (L.) Skeels is one of the most popular species in Morocco. Despite its ability to survive under harsh drought, it is endangered due to soil land removal and a lack of natural regeneration. Remote sensing offers a powerful resource for mapping, assessing, and monitoring the forest tree species at high spatio-temporal resolution. Multi-spectral Sentinel-2 and Synthetic Aperture Radar (SAR) time series combined with Digital Elevation Model (DEM) over the Argan forest in Essaouira province, Morocco, were subjected to pixel-based machine learning classification and analysis. We investigated the influence of different SAR data parameters and DEM layers on the performance of machine learning algorithms. In addition, we evaluated the synergistic effects of integrating remote sensing data, including optical, SAR, and DEM data, for identifying argan trees in the Smimou area. We collected data from Sentinel-2, Sentinel-1, SRTM DEM, and ground truth sources to achieve our goal. Testing different SAR parameters and integrating DEM layers of different resolutions with other remote sensing data showed that the Lee Sigma filter with a size of 11×11 and a DEM layer of 30 m resolution gave the best results using the Support Vector Machine algorithm. Significant improvements in overall accuracy (OA) and kappa index (K) were observed in the following phase. After applying a smoothing technique, the combined use of two Sentinel constellation products improved map accuracy and quality. For the best scenario (VV+NDVI), the OA was 88.32% (K = 0.85), while for scenarios NDVI+DEM and VH+NDVI+DEM, the OAs were 93.25% (K = 0.91) and 93.01% (K = 0.91), respectively. Integrating a DEM layer with SAR and optical data has significantly improved the accuracy in the classification of vegetation types, especially in our study area which is characterized by high environmental heterogeneity.

  Keywords


Argan Forest, Sentinel-2, GLCM Texture, SAR Parameters, DEM, Satellite Image Classification

Authors’ address

(1)
El Houcine El Moussaoui 0000-0002-3198-9195
Aicha Moumni 0000-0002-0203-8462
Abderrahman Lahrouni 0000-0002-2118-8570
Department of Physics, Faculty of Sciences Semlalia, Cadi Ayyad University, Marrakech (Morocco)

Corresponding author

 
El Houcine El Moussaoui
elhou.moussaoui@gmail.com

Citation

El Moussaoui EH, Moumni A, Lahrouni A (2024). Assessing the influence of different Synthetic Aperture Radar parameters and Digital Elevation Model layers combined with optical data on the identification of argan forest in Essaouira region, Morocco. iForest 17: 100-108. - doi: 10.3832/ifor4183-016

Academic Editor

Agostino Ferrara

Paper history

Received: Jul 15, 2022
Accepted: Apr 17, 2024

First online: Apr 24, 2024
Publication Date: Apr 30, 2024
Publication Time: 0.23 months

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