Analysis and modeling of spatial stand structures. Methodological considerations based on mixed beech-larch stands in lower saxony

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

163 Zitate (Scopus)

Abstract

The first part of this paper highlights spatial stand structure as the central stand characteristic and introduces methods of pattern identification. This involves two nearest-neighbour methods for the identification of stand structures, i.e., the aggregation index R by Clark and Evans [Clark, Ph.J. and Evans, F.C., 1954. Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology 35 (4) 445-453.] for univariate patterns and the segregation index S by Pielou [Pielou, E.C., 1977. Mathematical Ecology. Wiley.] for univariate patterns. Both were used to describe the structure of 53 experimental plots of mixed beech-larch stands in Lower Saxony which provided the data base for this investigation. The second part of the study deals with the development of the STRUGEN stand structure generator, designed for the modeling and reproduction of spatial stand structures. To generate stand structures, a two-dimensional homogeneous Poisson process is used as well as a set of two-dimensional distribution functions which determine mixture type and intermingling intensity of main and associated tree species. Moreover, a distance function secures minimum distances between competing neighbouring trees. Consequently, the produced pattern is tile result of a combination of an inhomogeneous Poisson process (for generating mixture units) and a hard-core process (for securing minimum distances between neighbours). The STRUGEN generator was designed and successfully used for the investigation of 53 mixed beech-larch stands. It provides initial values and stand structures for distance-dependent single-tree models from estimated qualitative stand characteristics. STRUGEN is a useful tool and allows initial, pragmatic steps towards fully utilising available qualitative and quantitative information to diagnose the state of a forest and to predict its growth.

OriginalspracheEnglisch
Seiten (von - bis)237-253
Seitenumfang17
FachzeitschriftForest Ecology and Management
Jahrgang97
Ausgabenummer3
DOIs
PublikationsstatusVeröffentlicht - 9 Okt. 1997
Extern publiziertJa

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