A Multilevel Analysis of the Impact of Land Use on Interannual Land-Cover Change in East Africa
A Multilevel Analysis of the Impact of Land Use on Interannual Land-Cover Change in East Africa
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DOI：
10.1007/s10021-007-9026-y
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发表时间：
2007-03
期刊：
影响因子：
3.7
通讯作者：
S. Serneels;M. Linderman;E. Lambin
中科院分区：
文献类型：
--
作者：
S. Serneels;M. Linderman;E. Lambin
AbstractThe的目的，这项研究的特点是短期的土地覆盖变化过程中检测到的东非，一组变化的度量，允许量化的年际变化植被生产力，植被物候的变化和两者的结合。我们测试了在何种程度上土地利用，火灾活动和牲畜放牧修改植被对短期降雨量变化的响应，在东非，这是如何反映在变化指标来自中分辨率成像光谱仪（MODIS）时间序列的遥感数据。我们使用了一个层次的方法来解开人类活动和气候变化的贡献，在东非短期植被变化的模式在不同层次的组织。我们的研究结果清楚地表明，土地利用显着影响植被对降雨变率的时间序列的MODIS数据测量。不同土地利用类型的地区对年际气候变率的反应不同，导致变化指数的值因土地利用类型而异。土地利用的影响更多地反映在植被生产力的年际变化和植被的总体变化上，而物候的变化主要是由气候变率驱动的，并以类似的方式影响大多数植被类型。我们的多层次方法改进了模型，并清楚地表明，气候影响在不同的尺度上发挥作用，而不是土地利用，火灾和草食动物放牧。它帮助我们了解研究区域内生物群落之间的动态，并调查不同尺度上影响短期变化指数的不同因素的相对重要性。
AbstractThe aim of this study was to characterize the short-term land-cover change processes that were detected in Eastern Africa, based on a set of change metrics that allow for the quantification of interannual changes in vegetation productivity, changes in vegetation phenology and a combination of both. We tested to what extent land use, fire activity and livestock grazing modified the vegetation response to short-term rainfall variability in East Africa and how this is reflected in change metrics derived from MODerate Imaging Spectrometer (MODIS) time series of remote sensing data. We used a hierarchical approach to disentangle the contribution of human activities and climate variability to the patterns of short-term vegetation change in East Africa at different levels of organization. Our results clearly show that land use significantly influences the vegetation response to rainfall variability as measured by time series of MODIS data. Areas with different types of land use react in a different way to interannual climate variability, leading to different values of the change indices depending on the land use type. The impact of land use is more reflected in interannual variability of vegetation productivity and overall change in the vegetation, whereas changes in phenology are mainly driven by climate variability and affect most vegetation types in similar ways. Our multilevel approach led to improved models and clearly demonstrated that climate influence plays at a different scale than land use, fire and herbivore grazing. It helped us to understand dynamics within and between biomes in the study area and investigate the relative importance of different factors influencing short-term variability in change indices at different scales.