A Bayesian hierarchical model for estimating spatial and temporal variation in vegetation phenology from Landsat time series
A Bayesian hierarchical model for estimating spatial and temporal variation in vegetation phenology from Landsat time series
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DOI：
10.1016/j.rse.2017.03.020
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发表时间：
2017-06
影响因子：
13.5
通讯作者：
Cornelius Senf;Dirk Pflugmacher;M. Heurich;T. Krueger
中科院分区：
文献类型：
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作者：
Cornelius Senf;Dirk Pflugmacher;M. Heurich;T. Krueger
物候是反映植被对全球变化响应的重要指标。来自粗分辨率传感器的卫星观测（即，MODIS和AVHRR）已被广泛用于研究气候异常对植被物候的影响。粗分辨率传感器的优点是每天观测整个地球仪，但粗糙的空间分辨率，以及相对较短的时间跨度覆盖的中分辨率成像光谱仪，在分析物候的大规模趋势的显着缺点。来自中等分辨率传感器Landsat的时间序列数据可以克服这些问题。然而，由于陆地卫星的观测频率较低，用粗分辨率数据开发的物候方法不能直接转移。在这里，我们展示了一个新的贝叶斯层次模型框架估计年际变化的植被物候从陆地卫星时间序列，同时控制空间变化。该方法汇集了所有可用的观测，以估计物候参数的空间变化，同时专门建模年际变化作为随机效应项。贝叶斯方法的优点是能够结合其他物候学和气候观测的先验知识，以减少估计值的变异性，以及更可靠的不确定性估计。我们演示和评估的建模框架与案例研究的变化春季物候阔叶树在巴伐利亚森林国家公园在南部德国。结果表明，该模型能较好地估计物候参数的时空变化。季节开始时的时间变化与地面萌芽变化的测量结果总体上非常一致（r= 0.82 [0.80-0.84]）。我们提出的建模框架将有助于更好地监测和了解植被物候变化的尺度尚未探索的物候社区。
Phenology is a key indicator of vegetation response to global change. Satellite observations from coarse resolution sensors (i.e., MODIS and AVHRR) have been widely used to study impacts of climate anomalies on vegetation phenology. The advantage of coarse resolution sensors are daily observations across the entire globe, but the coarse spatial resolution, as well as the relatively short time span covered by MODIS, are significant drawbacks in analyzing landscape-scale trends in phenology. Time series data from the medium resolution sensor Landsat may overcome these issues. However, because of Landsat's lower observation frequency, phenological methods developed with coarse resolution data are not directly transferable. Here, we demonstrate a new Bayesian hierarchical modeling framework for estimating inter-annual variation in vegetation phenology from Landsat time series while controlling for spatial variation. The method pools all available observations to estimate the spatial variation in phenological parameters, while specifically modeling inter-annual variation as random effect terms. The advantage of a Bayesian approach is the ability to incorporate prior knowledge from other phenology and climate observations to reduce variability in the estimates, as well as a more robust estimation of uncertainty. We demonstrate and evaluate the modeling framework with a case study of changing spring phenology in broad-leaved trees in the Bavarian Forest National Park in southern Germany. Results show that the model estimated the spatial and temporal variation in phenological parameters precisely. Temporal variation in start of season showed overall strong agreement with ground-based measures of bud-break variability (r= 0.82 [0.80–0.84]). Our proposed modeling framework will help to better monitor and understand changes in vegetation phenology at scales yet unexplored by the phenological community.