Abstract
This contribution presents methods that can be used to describe and analyse forest structure and diversity with particular reference to CCF management. Despite advances in remote sensing, mapped tree data in large observation windows are very rarely available in CCF management situations. Thus, although we present methods of second order statistics (SOC), the emphasis is on nearest neighbor statistics (NNS). The first section gives a general introduction and lists the objectives of the chapter. Methods of analysing non-spatial structure and diversity are presented in the second section. The third section introduces procedures for analysing unmarked and marked patterns of forest structure and diversity. Relevant R codes are provided to facilitate application of the methods. Examples of measuring differences between patterns and of reconstructing forests from samples are also presented. Finally, in Sect. 4 we discuss some important issues and summarize the main findings of this chapter.
Author contributions Lead author, text and calculations (unless indicated otherwise); co-authors, design of the chapter and contribution to data sets.
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- 1.
A total enumeration would be a fairly hopeless endeavour. For example, in a forest containing 47 trees (with 20 trees of species 1, 15 of species 2 and 12 of species 3), there are
$$ \left( \begin{array}{l} 47 \\ 20 \end{array} \right)*\left( \begin{array}{l} 27 \\ 15 \end{array} \right)*\left( \begin{array}{l} 12 \\ 12 \end{array} \right) = 1.697*{10^{{20}}} $$different ways in which the species can be assigned to the available positions.
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Gadow, K.v. et al. (2012). Forest Structure and Diversity. In: Pukkala, T., von Gadow, K. (eds) Continuous Cover Forestry. Managing Forest Ecosystems, vol 23. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2202-6_2
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