The way in which temporal dynamics structure ecological systems under the influence of a changing environment has long interested ecologists. Tackling the hierarchical structure of complex temporal patterns is a necessary step towards a more complete description of the fundamental nature of temporal dynamics in ecosystems. In pursuance of this task, remote sensing data provide valuable information to classify and describe functional features of ecosystems across scales. To approach the temporal complexity of ecosystems, we proposed a stepwise procedure based on combinations of big data techniques and time series analyses applied to data series of the Normalized Difference Vegetation Index (NDVI). The aim was to classify the temporal patterns and identify the differences in temporal dynamics of vegetation, by means of the frequency-domain and time-frequency domain components of a 20-year period of NDVI time series, respectively. In addition, we analysed the influence of climate in the temporal dynamics of vegetation. In particular, we applied archetype analysis to fast Fourier transform coefficients, using pixels as analytical units and frequencies as variables, of a large study area from North Patagonia, Argentina. Then, the most representative pixels for each archetype were used to analyse the explained variance by climatic predictor variables (temperature and precipitation) using a multiplicative model, whereas NDVI temporal dynamics was also described by means of time-frequency domain components with wavelet analysis, respectively. Six archetypes of temporal dynamics of arid and semi-arid rangelands were identified, as well as the main patterns of their distinctive frequencies through time and spatial location, respectively. The differences in temporal dynamics among archetypes were partly associated with climate and spatial features such as topography. The procedure was sensitive to capturing these temporal patterns, even those with a low data representation or noisy series while synthesizing information for an easy interpretation. Results are discussed in the light of future opportunities to combine this kind of information with other sources of information, aiming at the development of reliable land degradation and desertification assessment and monitoring tools.

在变化的环境影响下，时间动力学构造生态系统的方式引起了生态学家的长期关注。解决复杂时间模式的层次结构是朝着更完整地描述生态系统中时间动态的基本本质迈出的必要步骤。为了完成这项任务，遥感数据提供了有价值的信息，可以对各种规模的生态系统进行分类和描述。为了解决生态系统的时间复杂性，我们提出了一个基于大数据技术和时间序列分析相结合的分步程序，该时间序列分析应用于归一化植被指数（NDVI）的数据序列。目的是对时间模式进行分类，并确定植被时间动态的差异，分别通过NDVI时间序列的20年周期的频域和时频域分量。此外，我们分析了气候对植被时间动态的影响。特别是，我们将原型分析应用于来自阿根廷北巴塔哥尼亚的一个大型研究区域的快速傅立叶变换系数，使用像素作为分析单位，将频率作为变量。然后，使用乘法模型，将每种原型的最具代表性的像素用于通过气候预测变量（温度和降水）来分析解释的方差，而通过小波分析分别通过时频域分量来描述NDVI时空动态。 。确定了干旱和半干旱牧场的六个时空动力学原型，以及它们分别通过时间和空间位置的独特频率的主要模式。原型之间时间动态的差异部分与气候和空间特征（如地形）有关。该过程对于捕获这些时间模式非常敏感，即使是那些数据表示量低或噪声级的时间模式，在合成信息时也易于理解。根据将来将此类信息与其他信息源结合起来的机会，对结果进行了讨论，目的是开发可靠的土地退化和荒漠化评估与监测工具。原型之间时间动态的差异部分与气候和空间特征（如地形）有关。该过程对于捕获这些时间模式非常敏感，即使是那些数据表示量低或噪声级的时间模式，在合成信息时也易于理解。根据将来将此类信息与其他信息源结合起来的机会，对结果进行了讨论，目的是开发可靠的土地退化和荒漠化评估与监测工具。原型之间时间动态的差异部分与气候和空间特征（如地形）有关。该过程对于捕获这些时间模式非常敏感，即使是那些数据表示量低或噪声级的时间模式，在合成信息时也易于理解。根据将来将此类信息与其他信息源结合起来的机会，对结果进行了讨论，目的是开发可靠的土地退化和荒漠化评估与监测工具。