Application of Image Analytics for Tree Enumeration for Diversion of Forest Land

Abstract

Accurate tree enumeration is essential for responsible forest land diversion in development projects. Conventional manual surveys are slow, costly, and prone to errors. This paper introduces a cutting-edge image analytics solution that leverages satellite imagery and aerial photos to automate tree counting. The primary objective is to develop a robust system that identifies and categorizes trees by crown size, and environmental conditions. Advanced computer vision algorithms are integrated with machine learning models to analyze the imagery. Rigorous validation processes, including comparisons with ground-truth data from manual surveys, ensure high accuracy and reliability. The results are impressive. This solution significantly accelerates tree enumeration, eliminating resource-intensive manual efforts. It consistently demonstrates precision with minimal false positives and negatives. Moreover, it categorizes trees efficiently by species, offering a comprehensive view of the forested area. This project significance lies in its contribution to responsible and sustainable land development practices. By automating tree enumeration, it equips stakeholders with timely, precise data for informed decisions about land usage, conservation, and environmental impact assessments. The solution strikes a balance between development and ecological preservation, optimizing resource allocation while minimizing environmental impact in forested regions. In conclusion, this innovative image analytics solution revolutionizes forest land diversion, enabling efficient and ecologically conscious decision-making. It addresses the critical need for accurate tree enumeration in the face of developmental challenges, fostering responsible land use and environmental stewardship.