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Evaluation of SAR and Optical Image Fusion Methods in Oil Palm Crop Cover Classification Using the Random Forest Algorithm

Author

Listed:
  • Jose Manuel Monsalve-Tellez

    (Colombian Oil Palm Research Center—Cenipalma, Oil Palm Agronomy Research Program, Geomatics Section, Calle 98 # 70–91, Bogotá 111121, Colombia
    Facultad de Ingeniería y Arquitectura, Universidad Católica de Manizales, Carrera 23 No 60–63, Manizales 170001, Colombia)

  • Jorge Luis Torres-León

    (Colombian Oil Palm Research Center—Cenipalma, Oil Palm Agronomy Research Program, Geomatics Section, Calle 98 # 70–91, Bogotá 111121, Colombia)

  • Yeison Alberto Garcés-Gómez

    (Facultad de Ingeniería y Arquitectura, Universidad Católica de Manizales, Carrera 23 No 60–63, Manizales 170001, Colombia)

Abstract

This paper presents an evaluation of land cover accuracy, particularly regarding oil palm crop cover, using optical/synthetic aperture radar (SAR) image fusion methods through the implementation of the random forest (RF) algorithm on cloud computing platforms using Sentinel-1 SAR and Sentinel-2 optical images. Among the fusion methods evaluated were Brovey (BR), high-frequency modulation (HFM), Gram–Schmidt (GS), and principal components (PC). This work was developed using a cloud computing environment employing R and Python for statistical analysis. It was found that an optical/SAR image stack resulted in the best overall accuracy with 82.14%, which was 11.66% higher than that of the SAR image, and 7.85% higher than that of the optical image. The high-frequency modulation (HFM) and Brovey (BR) image fusion methods showed overall accuracies higher than the Sentinel-2 optical image classification by 3.8% and 3.09%, respectively. This demonstrates the potential of integrating optical imagery with Sentinel SAR imagery to increase land cover classification accuracy. On the other hand, the SAR images obtained very high accuracy results in classifying oil palm crops and forests, reaching 94.29% and 90%, respectively. This demonstrates the ability of synthetic aperture radar (SAR) to provide more information when fused with an optical image to improve land cover classification.

Suggested Citation

  • Jose Manuel Monsalve-Tellez & Jorge Luis Torres-León & Yeison Alberto Garcés-Gómez, 2022. "Evaluation of SAR and Optical Image Fusion Methods in Oil Palm Crop Cover Classification Using the Random Forest Algorithm," Agriculture, MDPI, vol. 12(7), pages 1-19, July.
    • Handle: RePEc:gam:jagris:v:12:y:2022:i:7:p:955-:d:854225
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    Citations

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    Cited by:

    1. Manel Khlif & Maria José Escorihuela & Aicha Chahbi Bellakanji & Giovanni Paolini & Zeineb Kassouk & Zohra Lili Chabaane, 2023. "Multi-Year Cereal Crop Classification Model in a Semi-Arid Region Using Sentinel-2 and Landsat 7–8 Data," Agriculture, MDPI, vol. 13(8), pages 1-21, August.
    2. Sa’ad Ibrahim, 2022. "Improving Land Use/Cover Classification Accuracy from Random Forest Feature Importance Selection Based on Synergistic Use of Sentinel Data and Digital Elevation Model in Agriculturally Dominated Lands," Agriculture, MDPI, vol. 13(1), pages 1-22, December.

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