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Area of Science:

  • Forestry and Environmental Science
  • Remote Sensing and Geospatial Technology
  • Artificial Intelligence in Ecology

Background:

  • Pakistan's forest cover is critically low, below the recommended 20-30%, largely due to deforestation.
  • Traditional forest estimation methods are insufficient for effective monitoring and supervision.
  • Advanced remote sensing and machine learning offer potential solutions for accurate forest assessment.

Purpose of the Study:

  • To evaluate the effectiveness of remote sensing and machine learning for forest cover estimation in Pakistan.
  • To compare the performance of Artificial Neural Network (ANN) and Random Forest (RF) algorithms using Sentinel-2 satellite imagery.
  • To provide accurate forest cover data for improved conservation and management strategies.

Main Methods:

  • Utilized Sentinel-2 satellite images, including single-layer and temporal layer stacked images.
  • Applied supervised classification algorithms: Artificial Neural Network (ANN) and Random Forest (RF).
  • Assessed classification accuracy using overall accuracy and kappa coefficients for both training and testing datasets.

Main Results:

  • Temporal layer stacked images with RF algorithm achieved the highest accuracy (99.79% training, 96.98% validation).
  • ANN and RF algorithms estimated forest cover at 31.07% and 31.17% respectively in District Abbottabad.
  • Both algorithms demonstrated high accuracy, with RF slightly outperforming ANN in this study.

Conclusions:

  • Advanced remote sensing techniques, particularly Sentinel-2 imagery, are highly effective for forest cover assessment.
  • Machine learning algorithms like ANN and RF significantly improve the accuracy and efficiency of forest monitoring.
  • This research provides a robust methodology for enhancing forest management and conservation efforts in Pakistan.