Abstract Ensuring the sustainable protection of forestry ecosystems faces numerous challenges. One significant hurdle is the constant threat of illegal logging and deforestation. Despite various regulations and conservation efforts, enforcing these measures can be difficult, particularly in remote or poorly monitored areas. Additionally, the increasing global demand for timber and other forest products puts immense pressure on these ecosystems, leading to overexploitation and habitat degradation. In this manuscript, Self-Focused Hierarchical Augmented Convolution Neural Network (SAHD-CNN) optimized with Tasmanian Devil Optimization (TDO) algorithm is proposed. Initially data is taken from Global Leaf Area Index (LAI) dataset. Afterward the input data is fed to Adaptive Distorted Quantum Matched-Filter. The pre-processing output is provided to Self-Focused Hierarchical Augmented Convolution Neural Network (SAHD-CNN) to effectively classifying Forestry Ecosystem Protection (FEP) for high, medium, and low. The weight parameters of the SAHD-CNN are optimized using Tasmanian Devil (TD) Optimization method. The proposed method is implemented in MATLAB working platform. The FEP-SAHDCNN technique attains higher accuracy value of 99% than the existing techniques such as Forestry Ecosystem Protection based Particle swarm Optimization (FEP-PSO) Accuracy value is 65%, Forestry Ecosystem Protection using Evaluation-based Neural Network (FEP-EN) Accuracy value is 82%, and FEP-GRS Accuracy value is 79%. Thus, the proposed method gives optimal output than the existing methods.
