Ecol Appl. 2014 Jan;24(1):84-93. doi: 10.1890/13-0307.1.
Information on landscape-scale patterns in species distributions and community types is vital for ecological science and effective conservation assessment and planning. However, detailed maps of plant community structure at landscape scales seldom exist due to the inability of field-based inventories to map a sufficient number of individuals over large areas. The Carnegie Airborne Observatory (CAO) collected hyperspectral and lidar data over Kruger National Park, South Africa, and these data were used to remotely identify > 500 000 tree and shrub crowns over a 144-km2 landscape using stacked support vector machines. Maps of community compositional variation were produced by ordination and clustering, and the importance of hillslope-scale topo-edaphic variation in shaping community structure was evaluated with redundancy analysis. This remote species identification approach revealed spatially complex patterns in woody plant communities throughout the landscape that could not be directly observed using field-based methods alone. We estimated that topo-edaphic variables representing catenal sequences explained 21% of species compositional variation, while we also uncovered important community patterns that were unrelated to catenas, indicating a large role for other soil-related factors in shaping the savanna community. Our results demonstrate the ability of airborne species identification techniques to map biodiversity for the evaluation of ecological controls on community composition over large landscapes.
有关物种分布和群落类型的景观尺度模式的信息对于生态科学以及有效的保护评估和规划至关重要。然而，由于基于实地的清查无法在大面积上对足够数量的个体进行绘图，因此很少有关于景观尺度植物群落结构的详细地图。卡内基航空天文台（CAO）在南非克鲁格国家公园收集了高光谱和激光雷达数据，这些数据被用于使用堆叠支持向量机远程识别超过 500000 棵树木和灌木树冠，面积为 144 平方公里。通过排序和聚类生成了群落组成变化的地图，并通过冗余分析评估了坡地地形土壤变化在塑造群落结构中的重要性。这种远程物种识别方法揭示了整个景观中木质植物群落的空间复杂模式，仅凭基于实地的方法无法直接观察到这些模式。我们估计，代表连锁序列的地形土壤变量解释了 21%的物种组成变化，而我们还发现了与连锁无关的重要群落模式，表明其他与土壤相关的因素在塑造热带稀树草原群落方面起着重要作用。我们的结果表明，机载物种识别技术能够绘制生物多样性地图，以评估大景观中生态控制对群落组成的影响。
