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首页> 外文期刊>Nonlinear processes in geophysics >A matrix clustering method to explore patterns of land-cover transitions in satellite-derived maps of the Brazilian Amazon
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A matrix clustering method to explore patterns of land-cover transitions in satellite-derived maps of the Brazilian Amazon

机译：一种矩阵聚类方法，用于探索巴西亚马逊卫星衍生地图中的土地覆盖过渡模式

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摘要

Changes in land-use systems in tropical regions, including deforestation, are a key challenge for global sustainability because of their huge impacts on green-house gas emissions, local climate and biodiversity. However, the dynamics of land-use and land-cover change in regions of frontier expansion such as the Brazilian Amazon are not yet well understood because of the complex interplay of ecological and socioeconomic drivers. In this paper, we combine Markov chain analysis and complex network methods to identify regimes of land-cover dynamics from land-cover maps (TerraClass) derived from high-resolution (30?m) satellite imagery. We estimate regional transition probabilities between different land-cover types and use clustering analysis and community detection algorithms on similarity networks to explore patterns of dominant land-cover transitions. We find that land-cover transition probabilities in the Brazilian Amazon are heterogeneous in space, and adjacent subregions tend to be assigned to the same clusters. When focusing on transitions from single land-cover types, we uncover patterns that reflect major regional differences in land-cover dynamics. Our method is able to summarize regional patterns and thus complements studies performed at the local scale.
机译：热带地区土地利用系统的变化，包括森林砍伐，是全球可持续性面临的主要挑战，因为它们对温室气体排放，当地气候和生物多样性具有巨大影响。但是，由于生态和社会经济驱动因素之间的复杂相互作用，人们还不太了解诸如巴西亚马逊等边疆扩张地区的土地利用和土地覆盖变化的动态。在本文中，我们结合了马尔可夫链分析法和复杂的网络方法，以从高分辨率（30？m）卫星图像中得出的土地覆盖图（TerraClass）识别土地覆盖动态。我们估计了不同土地覆盖类型之间的区域过渡概率，并在相似性网络上使用聚类分析和社区检测算法来探索主要土地覆盖过渡的模式。我们发现，巴西亚马逊河地区的土地覆被转变概率在空间上是异质的，并且相邻的次区域往往被分配给相同的集群。当关注单一土地覆盖类型的转变时，我们发现了反映土地覆盖动态主要区域差异的模式。我们的方法能够总结区域模式，因此可以补充在本地范围内进行的研究。

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