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A comparative methodological approach for argan forest classification using Landsat imagery.

El Houcine El Moussaoui1, Aicha Moumni2, Saïd Khabba1,3

  • 1LMFE, Faculty of Sciences Semlalia, Cadi Ayyad University, 40000, Marrakesh, Morocco.

Environmental Monitoring and Assessment
|January 30, 2025
PubMed
Summary
This summary is machine-generated.

This study mapped the argan forest in Morocco using remote sensing data from 1985 and 2019. Integrating a digital elevation model (DEM) with resampled normalized difference vegetation index (NDVI) achieved the highest accuracy for land cover mapping.

Keywords:
Argan forestDEMLandsatRemote sensingResampling techniqueSupervised and unsupervised classification

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Area of Science:

  • Environmental Science
  • Remote Sensing
  • Geographic Information Systems (GIS)

Background:

  • Argan forests in Morocco face significant environmental and anthropogenic pressures, necessitating effective monitoring strategies.
  • Remote sensing technology offers a valuable tool for assessing vegetation health and land cover dynamics over time.
  • Understanding land cover changes is crucial for the conservation of the ecologically and economically important argan landscape.

Purpose of the Study:

  • To evaluate supervised (Support Vector Machine, Maximum Likelihood, Minimum Distance) and unsupervised (Isodata) classification methods for argan forest mapping.
  • To assess the impact of resampling techniques and Digital Elevation Model (DEM) integration on classification accuracy.
  • To map land cover changes in the argan forest of the Smimou area, Essaouira province, between 1985 and 2019.

Main Methods:

  • Utilized Landsat-5 and Landsat-8 satellite imagery from 1985 and 2019 for the Smimou area.
  • Compared supervised classification algorithms (SVM, ML, MD) and unsupervised Isodata classification.
  • Investigated the influence of resampling methods on Normalized Difference Vegetation Index (NDVI) products and integrated DEM data.

Main Results:

  • Maximum Likelihood classification yielded high overall accuracies (OA): 89.62% (1985) and 87.58% (2019).
  • Resampled NDVI products improved OA to 91.60% (1985) and 88.85% (2019).
  • Integration of DEM with resampled NDVI achieved the highest OA: 92.27% (1985) and 92.37% (2019), demonstrating the benefit of combined data.

Conclusions:

  • The Maximum Likelihood classifier is effective for argan forest mapping.
  • Resampling techniques and DEM integration significantly enhance classification accuracy for monitoring argan forest dynamics.
  • This study provides a robust methodology for tracking changes in vital Moroccan argan landscapes using remote sensing.