Modeling Forest Tree Data Using Sequential Spatial Point Processes

Adil Yazigi (), Antti Penttinen (), Anna-Kaisa Ylitalo (), Matti Maltamo (), Petteri Packalen () and Lauri Mehtätalo ()
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Adil Yazigi: University of Eastern Finland
Antti Penttinen: University of Jyväskylä
Anna-Kaisa Ylitalo: Natural Resources Institute Finland (Luke)
Matti Maltamo: University of Eastern Finland
Petteri Packalen: University of Eastern Finland
Lauri Mehtätalo: Bioeconomy and Environment Unit, Natural Resources Institute Finland (Luke)

Journal of Agricultural, Biological and Environmental Statistics, 2022, vol. 27, issue 1, No 6, 88-108

Abstract: Abstract 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.

Keywords: Functional summary statistics; History-dependent model; Maximum likelihood; Ordered sequence; Spatial point processes (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s13253-021-00470-2

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