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

• URL: http://arxiv.org/abs/2604.02074v1
• Date: Thu, 02 Apr 2026 14:05:22 GMT
• Title: Country-wide, high-resolution monitoring of forest browning with Sentinel-2
• Abstract summary: We present a scalable approach for country-wide mapping of forest greenness anomalies at the 10 m resolution of Sentinel-2 data.<n>We learn a predictive quantile model of the normalised difference vegetation index (NDVI) derived from Sentinel-2 data.<n>The resulting expected seasonal cycles are used to detect NDVI anomalies across Switzerland between April 2017 and August 2025.
• Score: 21.30119488990051
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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