Satellite Based Forest Stand Detection Using Artificial Intelligence

Satellite-based forest stand detection plays a crucial role in monitoring and managing forest ecosystems, especially with the increasing challenges posed by deforestation and climate change. This project aims to enhance the detection of forest stands using satellite imagery and artificial intelligence. The system leverages a multi-class image classification approach, utilizing deep learning techniques to classify satellite images into 44 distinct classes of forest stands. Three primary algorithms have been implemented: ResNet-50, a powerful convolutional neural network known for its deep architecture; CNN + Wavelet Attention, which integrates the attention mechanism with wavelet transforms for improved feature extraction; and GradCam++, a technique that enhances interpretability by providing visual explanations for model decisions. These algorithms are applied to analyze high-resolution satellite images and classify them into various forest stand categories, improving the accuracy and reliability of forest monitoring. The project employs Python and deep learning frameworks such as Keras and TensorFlow for model implementation and evaluation. The results show that the combination of these deep learning models offers significant potential for satellite-based forest stand detection, supporting sustainable forest management and conservation efforts.

Keywords: Satellite-based detection, Forest Stand Classification, Deep Learning, ResNet-50, CNN + Wavelet Attention, GradCam++, Multi-class Classification, Artificial Intelligence, Forest Monitoring, Sustainable Forest Management, Python, TensorFlow, Keras.