Biodiversity and Ecosystem Monitoring using deep learning

The growing impacts of climate change, habitat degradation, and human intervention have intensified the urgency for effective biodiversity and ecosystem monitoring. Traditional methods such as field surveys and manual species identification, though valuable, are increasingly challenged by limitations in scale, frequency, and accuracy. This study addresses the need for scalable, automated solutions by leveraging Deep Learning(DL) models to analyze multi-modal ecological data. The research specifically applies Convolutional Neural Networks(CNNs) for spatial analysis of high-resolution satellite imagery (Sentinel-2 and Landsat) and camera trap datasets (Snapshot Serengeti), and Long Short-Term Memory (LSTM) networks for processing temporal bioacoustic recordings (from Xeno-canto and Rainforest Connection). The methodology involves structured data preprocessing, model training, evaluation using standard metrics, and a final integration into a hybrid framework combining CNN and LSTM outputs. The hybrid model outperformed individual networks with 94.5% accuracy. This research confirms that deep learning, hybrid architectures offer a powerful solution for biodiversity monitoring.