Spatio-temporal variations of climate-vegetation interactions in Central Asia have been given a lot of attention recently. However some serious methodological drawbacks of previous studies prevented thorough assessment of such interactions. In order to avoid the limitations and improve the analysis we used spatially explicit time series of NDVI (normalized difference vegetation index), temperature and precipitation which were decomposed to seasonal and trend components on perpixel basis using STL (seasonal decomposition of time series by loess). Trend and seasonal components of NDVI, precipitation and temperature were assessed pixelwise for temporal correlations with different lags to understand the patterns of their interaction in Kyrgyzstan and adjoining regions. Based on these results a coefficient of determination was calculated to understand the extent to which NDVI is conditioned by precipitation and temperature variations. The images with the lags of time series correlation minima and maxima for each pixel and coefficients of NDVI determination by temperature and precipitation were subjected to cluster analysis to identify interaction patterns over the study area. The approach used in this research differs from previous regional studies by implementation of seasonal decomposition and analyzing the full data without spatial or seasonal averaging within predetermined limits prior to the analysis. NDVI response to temperature and precipitation was assumed to be spatially variable in its sign, strength and lag, thus a separate model was developed for each pixel. The results were assessed with cluster analysis to identify spatial patterns of temporal interactions, decrease dimensionality and facilitate their comprehensiveness. The research resulted in 5 spatial clusters with different patterns of NDVI interaction with temperature and precipitation on intraand interannual scales. The highest correlation scores between NDVI and temperature at the seasonal scale were found at 0-4 months lag and between NDVI and precipitation at 1-5 months lag. At high elevations of 3000-4000 m above sea level, both precipitation and temperature occurred to be facilitating factors for vegetation development, whereas temperature was rather a limiting factor at lower elevations of 200-1300 m a.s.l. We developed maps of the NDVI coefficient of determination by both temperature and precipitation. Only deserts and glaciers had low coefficients of determination (adjusted R2) on the seasonal scale (0.1-0.3), whereas areas with vegetation were greatly conditioned by temperature and precipitation (0.7-0.95). On the trend scale, dense vegetation and bare areas had low coefficient of determination (0.1-0.3), whereas areas with average vegetation cover were greatly controlled by the climatic factors (0.7-0.9).

中文翻译：

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吉尔吉斯斯坦植被与气候相互作用模式：基于时间序列分析的空间离散化

中亚气候-植被相互作用的时空变化近年来备受关注。然而，先前研究的一些严重的方法学缺陷阻碍了对这种相互作用的彻底评估。为了避免限制和改进分析，我们使用了 NDVI（归一化差异植被指数）、温度和降水的空间显性时间序列，这些时间序列使用 STL（黄土时间序列的季节性分解）在每像素基础上分解为季节和趋势分量。 . 对 NDVI、降水和温度的趋势和季节性成分进行逐像素评估，以了解具有不同滞后的时间相关性，以了解它们在吉尔吉斯斯坦和邻近地区的相互作用模式。根据这些结果计算确定系数，以了解 NDVI 受降水和温度变化影响的程度。对每个像素的时间序列相关最小值和最大值滞后的图像以及由温度和降水确定的 NDVI 系数进行聚类分析，以识别研究区域内的交互模式。本研究中使用的方法与以前的区域研究不同，它实施季节性分解并分析完整数据，而在分析之前没有在预定范围内进行空间或季节性平均。假设 NDVI 对温度和降水的响应在其符号、强度和滞后方面是空间可变的，因此为每个像素开发了一个单独的模型。使用聚类分析对结果进行评估，以识别时间相互作用的空间模式，降低维度并促进其综合性。该研究产生了 5 个具有不同模式的 NDVI 与年内和年际尺度上的温度和降水相互作用的空间集群。在季节尺度上，NDVI 与温度之间的相关性得分最高出现在 0-4 个月的滞后期，NDVI 与降水之间的相关性得分在 1-5 个月的滞后期。在海拔 3000-4000 m 的高海拔地区，降水和温度似乎都是植被发育的促进因素，而在 200-1300 m asl 的低海拔地区，温度则是一个限制因素我们开发了 NDVI 决定系数图受温度和降水影响。只有沙漠和冰川在季节性尺度（0.1-0.3）上具有较低的决定系数（调整后的 R2），而有植被的地区受温度和降水的影响很大（0.7-0.95）。在趋势尺度上，茂密植被和裸露区域的决定系数较低（0.1-0.3），而平均植被覆盖的区域受气候因素影响较大（0.7-0.9）。