Artificial Intelligence to the Rescue of the Ecuadorian Amazon: Monitoring Changes with Deep Learning

Despite the importance of the Amazon region due to its biodiversity, ecosystem services and its enormous contribution to reduce global warming, this region is currently facing critical threats and challenges such as deforestation, urban and agricultural expansion, massive forest fires, illegal/non-regulated mining, among others. Given its vast extension, timely monitoring aimed to mitigate these problems represents a complex task. The lack of adequate tools has hindered environmental monitoring and management in this region, highlighting the need to develop advanced techniques to address these issues. This study focuses on the implementation of methods to detect and classify land cover changes, using an portion of the Ecuadorian Amazon as a case study. Our proposed method combines spectral vegetation indices generated from Sentinel-2 satellite image and deep learning techniques. Multitemporal images have been collected and preprocessed, applying the Bitemporal Adapter Network (BAN) for change detection and ResNet152V2 for land cover classification. The BAN is then re-trained with a specific dataset for the Ecuadorian Amazon. Results attain a good level of accuracy (99.36 unchanged and 89.6 changed) showing that these techniques are effective not only for detecting changes, but also for classifying affected land cover types. These findings provide valuable information for the implementation of conservation and management policies in the Ecuadorian Amazon.