Advanced Spatial-Temporal Framework for Deforestation Monitoring with CNN-LSTM Hybrid Architecture

Deforestation poses significant threats to environmental sustainability and reliable and real time monitoring solutions are needed. We propose a hybrid framework for detecting deforestation using camera trap imagery that combines Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Extracting spatial features through the CNN layers and temporal sequences performed through the LSTM layers, ensures a greater classification. To deal with class imbalance and generalize, the methodology uses ensemble techniques and data augmentation. We obtained superior performance for the proposed model compared to traditional methods (Random Forest and SVM) with an accuracy of 94%, precision of 92%, recall of 93% and an AUC-ROC of 96%. The Grad-CAM visual explanations revealed regions that affect model decision, providing insights into the interpretability of the model. Although there are limitations such as environmental biases and occasionally misclassifications, the proposed model is a scalable and efficient solution for real time deforestation detection. The results obtained are relevant to the development of advanced monitoring systems that support global conservation efforts and sustainable forest management.