A Novel Approach for Rapid Detection of Forest Degradation and Diseases Through Anomaly Analysis of Sentinel-2 Spectral Data

Abstract

Forest degradation is an ongoing global issue, with significant environmental impacts that necessitate efficient monitoring and management. This paper presents a simple yet effective method for detecting forest degradation using freely available Sentinel-2 satellite data and an anomaly detection approach. The aim of this study was to develop an accessible and reliable technique that could match the performance of more complex algorithms while using minimal computational resources. The research focused on spectral bands with 10-20 m resolution and vegetation indices (NDVI, NDMI, GCI, PSSRa) to analyze forest damage in the Harz region. The method involved identifying anomalies in the spectral data relative to randomly selected reference points from healthy forest areas, which were verified with high-resolution imagery from Google Earth Pro. The results demonstrated that specific Sentinel-2 bands, particularly B3 and B5, were the most informative for detecting damaged forests, while vegetation indices were less effective. By analyzing anomalies in these bands, we successfully tracked forest degradation from 2020 to 2024, revealing a significant increase in damage between 2020 and 2021, with a total of 68.1 thousand hectares of forest lost by 2024. The theoretical relevance of this study lies in the development of a cost-effective and straightforward method for forest monitoring, while the practical relevance is evident in its potential for large-scale forest management and conservation. This method provides an efficient tool for monitoring forest health with minimal data requirements and computational effort, offering a promising solution for forest managers and conservationists worldwide.