Towards Targeted Change Detection with Heterogeneous Remote Sensing Images for Forest Mortality Mapping

TL;DR

This paper introduces a novel method combining image-to-image translation and one-class classification to detect subtle forest mortality changes in heterogeneous remote sensing data, improving ecological disturbance mapping.

Contribution

It presents a new approach that effectively detects weak ecological change signals using deep learning-based image translation and OCC, tailored for multisource satellite imagery.

Findings

Successfully mapped forest mortality caused by moth outbreaks.

Enhanced detection of subtle ecological disturbances.

Demonstrated effectiveness on Landsat-5 and RADARSAT-2 data.

Abstract

Several generic methods have recently been developed for change detection in heterogeneous remote sensing data, such as images from synthetic aperture radar (SAR) and multispectral radiometers. However, these are not well suited to detect weak signatures of certain disturbances of ecological systems. To resolve this problem we propose a new approach based on image-to-image translation and one-class classification (OCC). We aim to map forest mortality caused by an outbreak of geometrid moths in a sparsely forested forest-tundra ecotone using multisource satellite images. The images preceding and following the event are collected by Landsat-5 and RADARSAT-2, respectively. Using a recent deep learning method for change-aware image translation, we compute difference images in both satellites' respective domains. These differences are stacked with the original pre- and post-event images and passed to an OCC trained on a small sample from the targeted change class. The classifier produces a credible map of the complex pattern of forest mortality.