Abstract Climate change threatens natural ecosystems, including biodiversity, and has emerged as the greatest challenge to human well-being. To support sustainable forest use, it is important to accurately map the distribution of afforestation and how the forests have changed over time. Climate change is affected by land use and land cover (LULC) since it alters biogeochemical cycles. Pakistan's declining forest cover has motivated policymakers to undertake the Green Growth Initiative (GGI). In accordance with its commitment to the Bonn Challenge, Pakistan executed the Billion Tree Afforestation Project (BTAP) to restore forests covering approxi-mately 0.35 million Ha. To estimate forest cover mapping accuracy, we developed a novel method to combine Sentinel-1, Sentinel-2 data, and vegetation indices to assess forest cover change. The methods and models were used to create forest maps for the period of 2016 to 2022. The optimal mapping approach was chosen after classifying the land cover using two machine learning-based classifiers: random forests (RF) and support vector machines (SVM). Finally, the spatiotemporal distribution of the changes induced by afforestation was generated. Based on the classifications of land cover, forest and non-forest identification was carried out. The results show that RF produces more accurate results, with kappa coefficient of 94-97% & 0.93-0.96 respectively. The classification accuracy and Kappa coefficient of the SVM model ranges from 92-94% & 0.91-0.95. Overall, forested areas in the study area increased by 3.7%. The techniques used in this study are cost-effective for accurately monitoring changes in forest cover.
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