Problem
Research questions and friction points this paper is trying to address.
forest browning
forest disturbance
high-resolution monitoring
NDVI anomalies
Sentinel-2
Country-wide, high-resolution monitoring of forest browning with Sentinel-2
🤖 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.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Sentinel-2
NDVI anomaly detection
quantile regression
forest browning
high-resolution monitoring
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