用哨兵-2数据实现瑞士10米分辨率森林退绿监测
Country-wide, high-resolution monitoring of forest browning with Sentinel-2
- 基于哨兵-2影像构建植被指数预测模型，融合生态与地形背景信息
- 模型解释了65%的季节性植被变化，可识别2017至2025年瑞士森林异常
- 适用于大范围森林健康监测，适合生态与环境政策制定者使用
自然和人为干扰正影响全球森林健康。大范围监测森林扰动对保护工作至关重要。本文提出一种可扩展的方法，利用哨兵-2数据在10米分辨率下实现瑞士全国范围的森林绿度异常监测。结合生态与地形背景信息，以及植被周期的已有表征，学习归一化植被指数（NDVI）的预测分位数模型。基于此模型生成的预期季节周期用于检测2017年4月至2025年8月期间瑞士各地的NDVI异常。拟合优度评估显示，条件模型解释了中位季节周期65%的观测变异。模型在春季返青期尤其受益于局部背景信息。结果呈现连贯的空间异常模式，支持全国尺度的森林退绿量化。独立参考数据验证表明，该模型能可靠检测多种类型扰动事件。
原文摘要 · Abstract (English)
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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