应用于芬兰森林清查记录的高斯过程数据中的主导特征识别
Dominant-feature identification in data from Gaussian processes applied to Finnish forest inventory records
摘要：
在空间数据中，位置相关的变化导致了被称为特征的连接结构。变化发生在不同的空间尺度上，可能源于不同的底层过程。每一种尺度都有其自身的主要特征。在这里，我们介绍了一种统计方法，用于识别这些尺度和它们的主要特征的数据从高斯过程。这种识别包括通过尺度空间分解来识别主导特征，并通过估计潜在过程的协方差函数参数及其与潜在驱动因素的关联来评估特征属性。我们分析芬兰森林资源清查数据从20世纪20年代使用这种优势特征识别方法，并确定最常见的芬兰树木，包括苏格兰松，挪威云杉，桦树，和其他本土落叶树木的断面积估计的变化尺度。比较这些树种的尺度相关特征及其属性，我们确定了土壤和人为驱动因素对其断面积空间分布的不同影响。这些数据进行了分析，第一次在其规模的变化，并由此产生的规模依赖的地图和估计是一个重要的贡献，芬诺斯堪的纳维亚的历史森林生态。到目前为止，这种分析是不可能的传统方法。
英文摘要：
In spatial data, location-dependent variation leads to connected structures known as features. Variations occur at different spatial scales and possibly originate from distinct underlying processes. Each of these scales is characterized by its own dominant features. Here we introduce a statistical method for identifying these scales and their dominant features in data from Gaussian processes. This identification involves credibly recognizing the dominant features by scale-space decomposition and assessing feature attributes by estimating covariance function parameters of the underlying processes and their associations to potential drivers. We analyze Finnish forest inventory data from the 1920s using this dominant-feature identification method and identify the scales of variation in basal area estimates of most common Finnish trees, including Scots pine, Norway spruce, birch, and other native deciduous trees. Comparing the resulting scale-dependent features and their attributes in these tree species, we identify the different effects of edaphic and anthropogenic drivers on the spatial distribution of their basal areas. These data are analyzed for the first time in terms of their scale of variation, and the resulting scale-dependent maps and estimates are an essential contribution to the historical forest ecology of Fennoscandia. Until now, this analysis was not possible with conventional methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
