Machine Learning Analysis Reveals Deforestation Risks in Pakistan's Semi-Arid Regions

Strategic Insights -

Researchers at Beijing Forestry University have conducted a study using machine learning and remote sensing to analyze deforestation and fragmentation dynamics in Pakistan's semi-arid regions between 2001 and 2021. The research found that deforestation and forest degradation in these regions drive critical environmental challenges. The study used multi-source remote sensing data, Google Earth Engine (GEE), and machine learning algorithms (Random Forest and Extreme Gradient Boosting) to assess the impact of human activities on forest ecosystems.

Key Takeaways:

  • The study achieved high accuracy in land use/land cover classification, with Random Forest achieving 94.81% overall accuracy and XGBoost demonstrating 90.17% accuracy in spatial fragmentation analysis.
  • The net forest cover increased by 3.48% between 2001 and 2021, but structural fragmentation intensified, with a 3.4% increase in edge forests and a 2.9% rise in isolated islets.
  • High-risk deforestation zones (9.09% of the landscape) were concentrated near urban-agricultural frontiers, driven by road proximity and elevation.
  • Urban areas expanded by 20.2% between 2010 and 2021, displacing cropland and barren land, which declined by 1.94%.
  • Morphological Spatial Pattern Analysis (MSPA) revealed persistent connectivity loss, with core forests representing only 3.51% of the study area.
  • The research concluded that conservation strategies prioritizing landscape connectivity and mitigating connectivity-based degradation are necessary in semi-arid regions.

Statistics:

  • 94.81%: Accuracy of Random Forest in land use/land cover classification
  • 90.17%: Accuracy of XGBoost in spatial fragmentation analysis
  • 3.48%: Increase in net forest cover between 2001 and 2021
  • 3.4%: Increase in edge forests
  • 2.9%: Rise in isolated islets
  • 9.09%: Proportion of high-risk deforestation zones
  • 20.2%: Increase in urban areas between 2010 and 2021
  • 1.94%: Decline in cropland and barren land between 2010 and 2021
  • 3.51%: Proportion of core forests representing the study area

Sources:

  • NewsRx. Studies in the Area of Machine Learning Reported from Beijing Forestry University [Assessing Deforestation and Degradation Risks In Pakistan (2001-2021): a Machine Learning and Remote Sensing Perspective]. Global Warming Focus. November 3, 2025; p 4339.
  • Environmental Technology & Innovation. Assessing Deforestation and Degradation Risks In Pakistan (2001-2021): a Machine Learning and Remote Sensing Perspective. 2025; 40.