Country-wide, high-resolution monitoring of forest browning with Sentinel-2

TL;DR

This paper introduces a scalable, high-resolution method for monitoring forest browning across Switzerland using Sentinel-2 data, effectively detecting disturbances through anomaly modeling of NDVI.

Contribution

The study presents a novel country-wide approach combining ecological context and seasonal modeling to detect forest health anomalies at 10 m resolution.

Findings

Model explains 65% of NDVI variation.

Method reliably detects various disturbance types.

Produces coherent spatial anomaly patterns.

Abstract

Natural and anthropogenic disturbances are impacting the health of forests worldwide. Monitoring forest disturbances at scale is important to inform conservation efforts. Here, we present a scalable approach for country-wide mapping of forest greenness anomalies at the 10 m resolution of Sentinel-2. Using relevant ecological and topographical context and an established representation of the vegetation cycle, we learn a predictive quantile model of the normalised difference vegetation index (NDVI) derived from Sentinel-2 data. The resulting expected seasonal cycles are used to detect NDVI anomalies across Switzerland between April 2017 and August 2025. Goodness-of-fit evaluations show that the conditional model explains 65% of the observed variations in the median seasonal cycle. The model consistently benefits from the local context information, particularly during the green-up period. The approach produces coherent spatial anomaly patterns and enables country-wide quantification of forest browning. Case studies with independent reference data from known events illustrate that the model reliably detects different types of disturbances.