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A multi-scale spatiotemporal modeling approach to explore vegetation dynamics patterns under global climate change

机译：探索全球气候变化下植被动力学模式的多尺度时空建模方法

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摘要

Given the complexity of vegetation dynamic patterns under global climate change, multi-scale spatiotemporal explicit models are necessary in order to account for environmental heterogeneity. However, there is no efficient time-series tool to extract, reconstruct and analyze the multi-scale vegetation dynamic patterns under global climate change. To fill this gap, a Multi-Scale Spatio-Temporal Modeling (MSSTM) framework which can incorporate the pixel, scale, and time-specific heterogeneity was proposed. The MSSTM method was defined on proper time-series models for multi-temporal components through wavelet transforms. The proposed MSSTM approach was applied to a subtropical mountainous and hilly agro-forestry ecosystem in southeast China using the moderate resolution imaging spectroradiometer enhanced vegetation index (EVI) time-series data sets from 2001 to 2011. The MSSTM approach was proved to be efficient in characterizing and forecasting the complex vegetation dynamic patterns. It provided good estimates of the peaks and valleys of the observed EVI and its average percentages of relative absolute errors of reconstruction was low (6.65). The complexity of the relationship between vegetation dynamics and meteorological parameters was also revealed through the MSSTM method: (1) at seasonal level, vegetation dynamic patterns are strongly associated with climatic variables, primarily the temperature and then precipitation, with correlations slight decreasing (EVI-temperature)/increasing (EVI-precipitation) with altitudinal gradients. (2) At inter-annual scale, obvious positive correlations were primarily observed between EVI and temperature. (3) Despite very low-correlation coefficients observed at intra-seasonal scales, considerable proportions of EVI anomalies are associated with climatic variables, principally the precipitation and sunshine durations.
机译：考虑到全球气候变化下植被动态格局的复杂性，有必要采用多尺度时空显式模型来解释环境异质性。但是，没有有效的时间序列工具来提取，重建和分析全球气候变化下的多尺度植被动态格局。为了填补这一空白，提出了一种多尺度时空建模（MSSTM）框架，该框架可以合并像素，尺度和特定时间的异质性。通过小波变换，在针对多时间分量的适当时间序列模型上定义了MSSTM方法。使用2001年至2011年的中分辨率成像分光光度计增强植被指数（EVI）时间序列数据集，将拟议的MSSTM方法应用于中国东南部的亚热带山区和丘陵农林业生态系统。事实证明，MSSTM方法是有效的。表征和预测复杂的植被动态模式。它提供了对观测到的EVI的峰和谷的良好估计，并且其相对绝对绝对重建误差的平均百分比很低（6.65）。还通过MSSTM方法揭示了植被动力学与气象参数之间关系的复杂性：（1）在季节性水平上，植被动态模式与气候变量（主要是温度然后是降水）密切相关，相关性略有降低（EVI-温度）/随高度梯度增加（EVI降水）。 （2）在年际尺度上，EVI与温度之间主要存在明显的正相关。 （3）尽管在季节内尺度上观测到的相关系数很低，但相当大比例的EVI异常与气候变量有关，主要是降水和日照时间。

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  • 来源
    《GIScience & remote sensing》 |2016年第5期|596-613|共18页
  • 作者

    Qiu Bingwen; Wang Zhuangzhuang; Tang Zhenghong; Liu Zhe; Lu Difei; Chen Chongcheng; Chen Nan;

  • 作者单位

    Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou, Peoples R China;

    Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou, Peoples R China;

    Univ Nebraska, Community & Reg Planning Program, Lincoln, NE USA;

    Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou, Peoples R China;

    Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou, Peoples R China;

    Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou, Peoples R China;

    Fuzhou Univ, Key Lab Spatial Data Min & Informat Sharing, Minist Educ, Fuzhou, Peoples R China;

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  • 正文语种 eng
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  • 关键词

    Global climate change; MODIS EVI; multi-scale; spatiotemporal modeling; vegetation dynamics;

    机译：全球气候变化;MODIS EVI;多尺度;时空模拟;植被动力学;

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