Smart Reforestation Using AI and Geospatial Analysis (International Journal of Engineering & Extended Technologies Research (IJEETR), 2026)
Effective reforestation requires aligning tree species with site-specific environmental conditions, traditionally dependent on expert knowledge and labor-intensive geographic analysis. This paper presents Smart Reforestation using AI and Geospatial Analysis, an AI-driven framework for automating species suitability assessment and generating optimized planting strategies. The system integrates Google Earth Engine to extract environmental attributes such as elevation, temperature, soil pH, and climate variables for any geographic location. These features are processed using a supervised neural network trained to evaluate compatibility between environmental conditions and tree species requirements. Satellite imagery is analyzed using computer vision techniques to identify plantable zones by excluding roads, buildings, and water bodies. The framework enables scalable, data-driven, and reproducible reforestation planning. Experimental results demonstrate reliable suitability predictions aligned with ecological criteria. This approach enhances accuracy, efficiency, and sustainability, supporting large-scale restoration efforts and improving transparent decision-making
Keywords: Smart reforestation; Generative AI; Geospatial analysis; Google Earth Engine; Computer vision; Species suitability prediction; Spatial optimization