Deep Learning Evaluation of Grassland Restoration Outcomes for Threatened Fauna

Abstract

Introduction: Grasslands are important ecosystems that play host to many endangered species; unfortunately, however, habitat degradation, vegetational changes, and disturbances to the environment have resulted in a decrease in both the diversity of species and the stability of the ecosystem. The assessment of the results of the restoration effort is possible using a highly developed monitoring technique able to analyze intricate links between habitat recovery and species reaction. Materials and Methods: The framework consists of ecological observations, characteristics of vegetation, habitat structural parameters, climate factors, species occurrence information, and satellite images from degraded and restored grasslands. The collected data have been pre-processed and analyzed through deep learning methods to discover patterns regarding vegetation recovery, habitat suitability, and animal presence. The model's performance has been assessed in terms of accuracy, precision, recall, and F1-score. Results and Discussion: Deep learning algorithm outperformed both traditional machine learning models and CNN algorithms with an accuracy of 96.3%, precision of 95.8%, recall of 96.1%, and F1 score of 95.9%. It is clear from the above results that deep learning models are able to recognize restoration effects such as vegetation improvement, environmental stability, and habitat connectivity. Conclusion: The research shows that deep learning-aided ecological assessment is an effective means of monitoring the results of grassland rehabilitation. The suggested model will be useful for large-scale biodiversity monitoring, better conservation decision making, and data-based protection of endangered animal species populations.