Dominant features of spatial data are connected structures or patterns that emerge from location-based variation and manifest at specific scales or resolutions. To identify dominant features, we propose a sequential application of multiresolution decomposition and variogram function estimation. Multiresolution decomposition separates data into additive components, and in this way enables the recognition of their dominant features. A dedicated multiresolution decomposition method is developed for arbitrary gridded spatial data, where the underlying model includes a precision and spatial-weight matrix to capture spatial correlation. The data are separated into their components by smoothing on different scales, such that larger scales have longer spatial correlation ranges. Moreover, our model can handle missing values, which is often useful in applications. Variogram function estimation can be used to describe properties in spatial data. Such functions are therefore estimated for each component to determine its effective range, which assesses the width-extent of the dominant feature. Finally, Bayesian analysis enables the inference of identified dominant features and to judge whether these are credibly different. The efficient implementation of the method relies mainly on a sparse-matrix data structure and algorithms. By applying the method to simulated data we demonstrate its applicability and theoretical soundness. In disciplines that use spatial data, this method can lead to new insights, as we exemplify by identifying the dominant features in a forest dataset. In that application, the width-extents of the dominant features have an ecological interpretation, namely the species interaction range, and their estimates support the derivation of ecosystem properties such as biodiversity indices.

空间数据的主要特征是连接的结构或模式，这些结构或模式从基于位置的变化中出现，并以特定的比例或分辨率显示。为了确定主要特征，我们提出了多分辨率分解和变异函数估计的顺序应用。多分辨率分解将数据分离为附加成分，从而可以识别其主要特征。针对任意网格化空间数据开发了一种专用的多分辨率分解方法，其中基础模型包括精度和空间权重矩阵以捕获空间相关性。通过在不同尺度上进行平滑处理，将数据分为各个部分，从而使更大尺度具有更长的空间相关范围。而且，我们的模型可以处理缺失值，这在应用程序中通常很有用。变异函数估计可用于描述空间数据中的属性。因此，针对每个组件估算此类功能，以确定其有效范围，从而评估主要特征的宽度范围。最后，贝叶斯分析可以推断出确定的主要特征，并判断这些特征是否确实不同。该方法的有效实现主要依赖于稀疏矩阵数据结构和算法。通过将该方法应用于模拟数据，我们证明了其适用性和理论上的合理性。在使用空间数据的学科中，这种方法可以带来新的见解，正如我们通过识别森林数据集中的主要特征所举例说明的。在该应用中，主要特征的宽度范围具有生态学意义，