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Abstract：
Forest dieback processes linked to drought are expected to increase due to climate warming. Remotely sensed data offer several advantages over common field monitoring methods such as the ability to observe large areas on a systematic basis and monitoring their changes, making them increasingly used to assess changes in forest health. Here we aim to use a combined approximation of fieldwork and remote sensing to explore possible links between forest dieback and land surface phenological and trend variables derived from long Landsat time series. Forest dieback was evaluated in the field over 31 plots in a Mediterranean, xeric Pinus pinaster forest. Landsat 31-year time series of three greenness (EVI, NDVI, SAVI) and two wetness spectral indices (NMDI and TCW) were derived covering the period 1990-2020. Spectral indices from time series were decomposed into trend and seasonality using a Bayesian estimator while the relationships of the phenological and trend variables among levels of damage were assessed using linear and additive mixed models. We have not found any statistical pieces of evidence of extension or shortening patterns for the length of the phenological season over the examined 31-year period. Our results indicate that the dieback process was mainly related to the trend component of the spectral indices series whereas the phenological metrics were not related to forest dieback. We also found that plots with more dying or damaged trees displayed lower spectral indices trends after a severe drought event in the middle of the 1990s, which confirms the Landsat-derived spectral indices as indicators of early-warning signals. Drops in trends occurred earlier for wetness indices rather than for greenness indices which suggests that the former could be more appropriate for dieback detection, i.e. they could be used as early warning signals of impending loss of tree vigor.
摘要：
由于气候变暖，与干旱相关的森林枯萎过程预计会增加。遥感数据比普通的现场监测方法具有若干优势，例如能够系统地观察大面积区域并监测其变化，使它们越来越多地用于评估森林健康的变化。在这里，我们旨在使用野外工作和遥感的组合近似来探索森林枯萎与长期Landsat时间序列得出的地表物候和趋势变量之间的可能联系。在地中海地区的31块土地上对森林的枯萎进行了评估，干燥的皮松森林。Landsat31年三个绿色的时间序列(EVI，NDVI,SAVI）和两个湿度光谱指数（NMDI和TCW）是1990-2020年期间得出的。使用贝叶斯估计器将时间序列中的光谱指数分解为趋势和季节性，而使用线性和加性混合模型评估了破坏水平之间的物候和趋势变量之间的关系。在所检查的31年期间，我们尚未发现任何有关物候季节长度延长或缩短模式的统计证据。我们的结果表明，枯萎过程主要与光谱指数系列的趋势成分有关，而物候指标与森林枯萎无关。我们还发现，在1990年代中期发生严重干旱事件后，树木死亡或受损树木较多的地块显示出较低的光谱指数趋势，这证实了Landsat衍生的光谱指数作为预警信号的指标。湿度指数而不是绿色指数的趋势下降发生得更早，这表明前者可能更适合用于枯萎检测，即它们可以用作树木活力即将丧失的预警信号。
