A Deep Semantic Segmentation Approach to Map Forest Tree Dieback in Sentinel-2 Data

Abstract

Massive tree dieback events triggered by various disturbance agents, such as insect outbreaks, pests, fires, and windstorms, have recently compromised the health of forests in numerous countries with a significant impact on ecosystems. The inventory of forest tree dieback plays a key role in understanding the effects of forest disturbance agents and improving forest management strategies. In this article, we illustrate a deep learning approach that trains a U-Net model for the semantic segmentation of Sentinel-2 images of forest areas. The proposed U-Net architecture integrates an attention mechanism to amplify the crucial information and a self-distillation approach to transfer the knowledge within the U-Net architecture. Experimental results demonstrate the significant contribution of both attention and self-distillation to gaining accuracy in two case studies in which we perform the inventory mapping of forest tree dieback caused by insect outbreaks and wildfires, respectively.