Deep Change Vector Analysis to Map Bark Beetle Outbreaks in Open Sentinel-2 Data

Open remote sensing science has been recently boosted by the free availability of Sentinel-2 images of planet Earth acquired with the Copernicus programme. In particular, processing open Sentinel-2 images with Artificial Intelligence (AI) techniques holds great potential for revolutionizing data science applications in many domains of Earth sciences. In this paper, we explore the potential of an unsupervised learning method designed to process Sentinel-2 images of Earth’s forest scenes and automate the inventory of forest tree dieback caused by bark beetle outbreaks. Specifically, we describe PHANTASM: a method to identify forest tree dieback patches performing the Change Vector Analysis (CVA) of bi-temporal Sentinel-2 images of forest scenes. While the traditional CVA strategy is based on the analysis of pixel-wise differences in spectral values, we enrich the Sentinel-2 spectrum with both a selection of Spectral Vegetation Indexes and a Spectral-Spatial Deep Embedding. The Spectral Vegetation Indexes are pre-defined combinations of spectral bands commonly designed to enhance the accuracy of semantic segmentation models trained to map bark beetle stress in spectral data. The Deep Embedding is a spectral-spatial representation of Sentinel-2 pixels trained with a deep neural network. In particular, we use a pre-trained, semantic segmentation U-Net to obtain the Deep Embedding that models the spatial relationship among neighbouring spectral pixels. We assess the effectiveness of the proposed method in a case study regarding bark beetle outbreaks in Sentinel-2 images of forest scenes in the Czech Republic.