The spatial structure of a forest stand is typically modeled by spatial point process models. Motivated by aerial forest inventories and forest dynamics in general, we propose a sequential spatial approach for modeling forest data. Such an approach is better justified than a static point process model in describing the long-term dependence among the spatial location of trees in a forest and the locations of detected trees in aerial forest inventories. Tree size can be used as a surrogate for the unknown tree age when determining the order in which trees have emerged or are observed on an aerial image. Sequential spatial point processes differ from spatial point processes in that the realizations are ordered sequences of spatial locations, thus allowing us to approximate the spatial dynamics of the phenomena under study. This feature is useful in interpreting the long-term dependence and spatial history of the locations of trees. For the application, we use a forest data set collected from the Kiihtelysvaara forest region in Eastern Finland.

林分的空间结构通常由空间点过程模型建模。受空中森林清单和一般森林动态的推动，我们提出了一种用于模拟森林数据的顺序空间方法。在描述森林中树木的空间位置与空中森林清单中检测到的树木的位置之间的长期依赖性时，这种方法比静态点过程模型更合理。在确定树木出现或在航拍图像上观察到的顺序时，树木大小可用作未知树龄的替代。顺序空间点过程与空间点过程的不同之处在于，实现是空间位置的有序序列，从而使我们能够近似所研究现象的空间动态。此功能可用于解释树木位置的长期依赖性和空间历史。对于应用程序，我们使用从芬兰东部 Kiihtelysvaara 森林地区收集的森林数据集。