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

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.

Figures (7)

• Figure 1: Exemplary predicted NDVI ranges and anomalies for 4 forest pixels. Green curves denote the upper quartiles ($f_{0.75}(t)$), red curves denote the lower quartiles ($f_{0.25}(t)$). Interquartile ranges are shaded in red. Dashed lines indicate the anomaly thresholds derived from those curves. NDVI observations that fall within the anomaly thresholds are coloured black, observations labelled as anomalies are coloured by their anomaly score, with lighter colour denoting more negative scores (larger deviations from the normal range).
• Figure 2: Example anomaly map for forest pixels based on NDVI observations from 22/08/2023 and 24/08/2023 combined. A pixel is flagged if it is detected as anomalous on either of the days. The three panels on the left show a magnified view of the anomalies at the indicated regions on the map. Normal pixels are coloured in black and anomalous pixels are shaded in red according to their anomaly score.
• Figure 3: D$^2_{\text{pinball}}$ per day of the year for each of the predicted quartiles. The daily values are shown alongside the 7-day rolling mean. For each day of the year, the value of D$^2_{\text{pinball}}$ expresses the fraction of pinball loss explained when compared to a model that uses the empirical quantile at that day of the year (the climatology baseline). A value of 0 corresponds to a model that predicts the same value at all locations per day of the year.
• Figure 4: Daily fraction of forest pixels affected by negative NDVI anomalies. The negative anomaly fractions are computed per day of the year and smoothed with a 7-day rolling average. The dashed horizontal line indicates the overall fraction of anomalies detected at forest pixels. Fractions are relative to the number of valid observations at a pixel. The vertical lines indicate the season starts on March 1 (spring), June 1 (summer), September 1 (autumn) and December 1 (winter).
• Figure 5: Fraction of observations per pixel affected by negative NDVI anomalies ("browning") over the entire observation period. Fractions are relative to the number of valid observations at a pixel. The histogram on the right shows the distribution of anomaly fraction values across all pixels. The three panels on the left show a magnified view of the anomaly fractions at the indicated regions on the map.