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

2026-04-02

AI Summary

Effective forest health monitoring requires high-resolution, large-scale remote sensing approaches to address both natural and anthropogenic disturbances. This study leverages 10-meter Sentinel-2 satellite data, integrating ecological and topographic contextual information with vegetation phenological cycles to develop a conditional quantile regression model based on the Normalized Difference Vegetation Index (NDVI). For the first time, this approach enables national-scale (Switzerland) detection of forest greenness anomalies at 10-meter resolution. The method substantially improves modeling accuracy during the greening-up period, produces spatially coherent browning anomaly maps, explains 65% of the variability in the median seasonal cycle, and successfully identifies multiple known disturbance events. These results provide quantitative assessment and visual support for forest conservation efforts.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• forest browning
• forest disturbance
• high-resolution monitoring
• NDVI anomalies
• Sentinel-2

Innovation

• Sentinel-2
• NDVI anomaly detection
• quantile regression
• forest browning
• high-resolution monitoring