Deep Learning and Image Analytics for Forest Land Management: Classification, and Object Detection

Abstract

Rapid urbanization and industrialization have contributed to deforestation which has led to a severe degradation of the environment and biodiversity loss. Traditional field survey methods are not applicable in the monitoring of large forest areas due to the fact that such methods are manual, costly and time consuming as well as subject to human error. The current methods of remote sensing cannot effectively detect various tree species and the number of trees in large areas as well. To address these issues, this paper introduces a computerized system of tree detection and species identification on high-resolution image data and image analysis methods. The suggested system uses the combination of remote sensing data and deep learning objects detection and classification models to identify, count, and classify tree species using large forest cover. The framework has higher accuracy and faster processing in comparison with traditional surveys and simple approaches of remote sensing. This methodology is useful in the mass evaluation of biodiversity and gives accurate information on the monitoring of forests and land-use studies. The system allows making precise and prompt decisions based on the data to achieve sustainable forest management and conservation of natural resources by facilitating precise and timely counting of trees and identifying the species with the use of the system by environmental scientists, conservationists, and policymakers.