Complex network-based time series remote sensing model in monitoring the fall foliage transition date for peak coloration
Complex network-based time series remote sensing model in monitoring the fall foliage transition date for peak coloration
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DOI：
10.1016/j.rse.2019.05.003
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发表时间：
2019-08
影响因子：
13.5
通讯作者：
C. Diao
中科院分区：
文献类型：
--
作者：
C. Diao
植被物候事件，尤其是叶色高峰，是对气候变化最为敏感的生态现象之一。与春季季节性重复发生的事件相比，秋季的物候仍然鲜为人知。对秋季物候的遥感监测为理解潜在的过程和机制提供了丰富的机会。然而，秋季树叶颜色的逐渐变化给远程估计关键物候转变日期带来了挑战。特别是，通过传统的基于曲线拟合的物候模型不能很好地捕捉到叶片高峰着色的过渡日期。此外，传统模型之间缺乏共识，这使得探索秋季物候过程的新的遥感表示是必要的。在这项研究中，我们开发了一种创新的基于复杂网络的物候模型，即“物候网络”，用于估计落叶高峰着色的过渡日期。物候网络模型通过分析光谱特征沿时间轨迹的集体变化来表征物候过程。一个新的网络度量，移动平均桥接系数，被设计来估计物候转换日期。以哈佛森林和哈伯德·布鲁克森林为参照点，结果表明，通过设计的物候网络模型估计的过渡日期与参照点的高峰着色期有很好的对应关系。通过光谱相似性形成的物候网络的独特结构区分了植被光谱特征在不同物候阶段的不同作用。本研究是将网络科学引入时间序列遥感模拟植被复杂物候过程的首次尝试。这种创新的基于网络的物候表示在改进遥感物候监测方面显示出巨大的潜力，并为后续植被对气候变化的物候响应建模提供了启示。
Vegetation phenological events, especially peak foliage coloration, are among the ecological phenomena that are most sensitive to climate change. Compared to spring seasonally recurring events, fall phenology remains much less understood. Remotely sensed monitoring of fall phenology provides a wealth of opportunities to understand the underlying processes and mechanisms. However, the gradual change of foliage color in the fall season makes it challenging to remotely estimate critical phenological transition dates. Particularly, the transition date for foliage peak coloration cannot be adequately captured via conventional curve fitting-based phenological models. Also the lack of consensus among the conventional models makes it desirable to explore new remotely sensed representations of the fall phenological process. In this study, we developed an innovative complex network-based phenological model, namely “pheno-network”, to estimate the fall foliage transition date for peak coloration. The pheno-network model characterizes the phenological process through analyzing the collective changes of spectral signatures along the temporal trajectory. A network measure, moving average bridging coefficient, is newly designed to estimate the phenological transition date. With Harvard Forest and Hubbard Brook Forest as reference sites, the results demonstrated that the transition date estimated through the devised pheno-network model corresponds well with the peak coloration period of the reference sites. The unique structure of the pheno-network formulated via spectral similarities differentiates the various roles of vegetation spectral signatures at different phenological stages. This study is the first attempt at introducing network science to time series remote sensing in modeling the complex phenological processes of vegetation. The innovative network-based phenological representation shows great potential in improving remotely sensed phenological monitoring and shedding light on the subsequent modeling of vegetation phenological responses to climate change.