Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology
Coupled Spatiotemporal Characterization of Monsoon Cloud Cover and Vegetation Phenology
复制标题
季风云量与植被物候的耦合时空特征
DOI：
10.3390/rs11101203
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
A. Kwarteng
中科院分区：
文献类型：
--
作者：
D. Sousa;C. Small;A. Spalton;A. Kwarteng
在季风生态系统中，植被物候通常受到季节降水的时间和强度的调节。季节性降水在空间和时间上往往具有显著的年际变化。对景观生态的严格定量理解需要关于季节降水和植被物候之间关系强度的空间明确信息，以及系统的年际变率。为了准确地估计这些信息，它必须基于空间和时间上一致的测量。光学卫星图像档案可以提供这些测量。卫星图像提供了以下两方面的观测：a)大气参数，如季风云量的时间和空间范围；b)物候参数，如植被变绿和衰老的时间和空间范围。这项工作提出了一种从光学图像时间序列中捕获大气和物候参数的方法。该方法利用单一光谱指数的经验正交函数（EOF）分析，统一表征季风云量和植被物候的时空动态。这可以通过充分利用绿色植被、土壤和云的可见光和近红外反射率的差异来实现。将图像时间序列转换成由低阶主分量组成的时间特征空间（TFS）。时间特征空间的结构揭示了云量和植被物候在时空上不同的年周期。为了说明这种技术，我们将其应用于对阿拉伯半岛南部佐法尔山脉的季节性云雾森林的回顾性分析。我们的结果量化了已知的（但以前未绘制的）季风持续时间和植被群落响应的局部梯度。各个生态子系统之间也有明显的区别，每个子系统内部都有一致的高程梯度。新的观测结果也出现了，例如变绿/早变绿事件和云持续时间的空间模式。这种方法在概念上很简单，可以用来描述地球上任何地方的其他季风环境。
In monsoonal ecosystems, vegetation phenology is generally modulated by the timing and intensity of seasonal precipitation. Seasonal precipitation is often characterized by substantial interannual variability in both space and time. A rigorous quantitative understanding of the ecology of the landscape requires spatially explicit information regarding the strength of the relationship between seasonal precipitation and vegetation phenology, as well as the interannual variability of the system. For this information to be accurately estimated, it must be based on spatially and temporally consistent measurements. The optical satellite image archive can provide these measurements. Satellite imagery offers observations of both a) atmospheric parameters such as the timing and spatial extent of monsoon cloud cover; and, b) phenological parameters, such as the timing and spatial extent of vegetation green-up and senescence. This work presents a method to capture both atmospheric and phenological parameters from an optical image time series. The method uses Empirical Orthogonal Function (EOF) analysis of a single spectral index for unified characterization of the spatiotemporal dynamics of both monsoon cloud cover and vegetation phenology. This is made possible by leveraging well-understood differences in the visible and near infrared reflectance of green vegetation, soil, and clouds. Image time series are transformed into a temporal feature space (TFS) that is comprised of low-order Principal Components. The structure of the temporal feature space reveals spatiotemporally distinct annual cycles of both cloud cover and vegetation phenology. In order to illustrate this technique, we apply it to the retrospective analysis of a seasonal cloud forest in the Dhofar Mountains of the southern Arabian Peninsula. Our results quantify known (but previously unmapped) local gradients in monsoon duration and vegetation community response. Individual ecological subsystems are also clearly distinguishable from each other, and consistent elevation gradients emerge within each subsystem. Novel observations also emerge, such as regreening/early greening events and spatial patterns in cloud duration. The method is conceptually straightforward and could be applied to characterize other monsoon environments anywhere on Earth.
影响因子：
6.2
作者：
Xiaoyang Zhang;Senthilnath Jayavelu;Lingling Liu;M. Friedl;G. Henebry;Yan Liu;C. Schaaf;A. Richardson;Joshua Gray
通讯作者：
Xiaoyang Zhang;Senthilnath Jayavelu;Lingling Liu;M. Friedl;G. Henebry;Yan Liu;C. Schaaf;A. Richardson;Joshua Gray
影响因子：
5.4
作者：
Fisher, Joshua B.;Lee, Brian;Hook, Simon
通讯作者：
Hook, Simon