Detection of Deforestation Using Prisma Hyperspectral and Deep Learning (1DCNN) in the Amazon Forest

Accurate detection of deforestation and logging activities is useful to monitor large scale damages in the Amazon forests. In this study, we focused on the use of deep learning based one dimensional convolutional neural network (1D-CNN) with Hyperspectral Precursor of the Application Mission (PRISMA) hyperspectral data for the detection of deforestation in the Amazon Forest. The PRISMA data was pre-processed to remove noisy bands, water absorption and some blue spectrum bands. Three main classes were identified and sampled as ground truth for classification: forest, deforestation and waterbodies. 1D-CNN were parameterised to obtain a classified map and then accuracy assessment was performed. Model achieved a very high overall accuracy of 98.92%, confirming that the method can be used for accurate mapping of deforestation.