A data mining approach for understanding topographic control on climate-induced inter-annual vegetation variability over the United States
A data mining approach for understanding topographic control on climate-induced inter-annual vegetation variability over the United States
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DOI：
10.1016/j.rse.2005.05.017
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发表时间：
2005-09
影响因子：
13.5
通讯作者：
A. White;Praveen Kumar;D. Tcheng
中科院分区：
文献类型：
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作者：
A. White;Praveen Kumar;D. Tcheng
气候变化和植被动态之间复杂的反馈关系是深入研究的主题，因为它对加深我们对全球生物地球化学循环的理解具有重要意义。在此背景下，我们解决了一个重要问题：“地形如何影响植被对自然气候波动的响应？”我们通过使用长期（1989-2001 年 13 年期间）月平均、每两周最大值复合归一化植被指数 (NDVI) 数据分析很大区域（美国大陆）的植被年际变化来探讨这个问题。这些数据是通过1公里分辨率的卫星遥感获得的。通过数据挖掘技术的新颖实施，我们表明北太平洋气候振荡和 ENSO 现象影响广泛地理区域的逐年植被变化。此外，植被对这些波动的响应取决于各种地形属性，例如海拔、坡度、坡向和与水分汇聚区的接近程度，尽管前两个是主要控制因素。因此，陆地植被对气候波动的动态响应表现出巨大的空间异质性，与地形引起的变异密切相关。这些发现表明，现有气候模型中的植被动态表示可能不够充分，因为这些模型没有考虑到这种依赖性。因此，经常用于指导政策决策的气候模型需要更好地纳入这些依赖性，以评估不断变化的气候情景下的陆地碳固存。
The complex feedback relationship between climate variability and vegetation dynamics is a subject of intense investigation for its implications in furthering our understanding of the global biogeochemical cycle. We address an important question in this context: “How does topography influence the vegetation's response to natural climate fluctuations?” We explore this issue through the analysis of inter-annual vegetation variability over a very large area (continental United States) using long-term (13-year period of 1989–2001), monthly averaged, biweekly maximum value composite normalized difference vegetation index (NDVI) data. These data are obtained from satellite remote sensing at 1-km resolution. Through the novel implementation of data mining techniques, we show that the Northern Pacific climate oscillation and the ENSO phenomena influence the year-to-year vegetation variability over an extensive geographical domain. Further, the vegetation response to these fluctuations depends on a variety of topographic attributes such as elevation, slope, aspect, and proximity to moisture convergence zones, although the first two are the predominant controls. Therefore, the dynamic response of terrestrial vegetation to climate fluctuations, which shows tremendous spatial heterogeneity, is closely linked to the variability induced by the topography. These findings suggest that the representation of vegetation dynamics in existing climate models, which do not incorporate such dependencies, may be inadequate. Therefore, climate models that are regularly employed to guide policy decisions need to better incorporate these dependencies for the assessment of terrestrial carbon sequestration under evolving climate scenarios.