Intelligent Tree Enumeration and Forest Analysis System for Environmental Monitoring

Abstract

Forest monitoring plays a critical role in sustainable environmental management, biodiversity conservation, and climate change mitigation. Traditional methods for tree enumeration, species classification, and green cover estimation are labor-intensive, prone to human error, and inefficient for large-scale applications. This research presents an automated image-based forest monitoring system that integrates deep learning and remote sensing techniques to enhance accuracy and efficiency. The proposed framework serves as a robust tool for forest management authorities, policymakers, and researchers seeking data-driven solutions for environmental monitoring and conservation planning.