Data-Driven Spatial Analysis of Reforestation in Pakistan: Identifying Optimal Locations for Sustainable Forest Growth and Climate Change Mitigation

As the world’s second-largest carbon storehouse after oceans, forests mitigate global warming and its associated climate change challenges. Hence, forest conservation efforts such as reforestation are crucial in maintaining this vital ecological service. Current approaches to large-scale reforestation include considering factors like soil quality, climate, topography, and land-use history to narrow areas for reforestation. However, these methods often struggle to efficiently identify suitable land across vast and diverse landscapes, leading to suboptimal outcomes. Despite the promise of modern techniques like Geographic Information Systems (GIS), remote sensing, and spatial modeling for optimizing reforestation success and ecological benefits, their large-scale implementation for selecting suitable land remains limited. This paper proposes a novel technique using unsupervised machine learning and remotely sensed satellite data to assess reforestation potential across vast and diverse landscapes. The approach utilizes K-means clustering and Gaussian Mixture Models to group regions based on their indexes. Normalized Difference Water Index (NDWI) is used to measure water availability; Laterite, Biotite, and Gossan indexes are used for mineral content, and the Normalized Difference Built-up Index (NDBI) and Urbanization Index (UI) are used to measure urbanization of a region. By analyzing the variation in vegetation within these clusters, the model identifies areas with high potential for future forestation. Our proposed model achieved an accuracy of 92% compared to a validation data set obtained from the Conservator of Forests for the Rawalpindi district. The presented approach can potentially optimize reforestation efforts and maximize their impact on climate change mitigation, leading to benefits such as increased carbon sequestration in countries like Pakistan with limited forest cover.