Zusammenfassung
Strukturelle Diversität, ein wichtiger Aspekt bei der Bewertung von Lebensräumen, dient als Eingangsgröße in ökologischen Modellen und als Grundlage von Kartierungen. Sie ist jedoch nicht direkt meßbar, sondern wird charakterisiert über Indizes, die wiederum auf meßbaren Größen aufbauen, wie Anzahl, Größe, Form und Randlinienlänge von Einzelflächen und dem Abstand zwischen Einzelflächen.
Die vorliegende Studie analysiert anhand eines Kartenbeispieles, in welchem die beiden. Flächenklassen Wald und Nichtwald unterschieden werden, vier grundsätzliche Eigenschaften ausgewählter Indizes: Differenzierungseigenschaften, ihre gegenseitige statistische Unabhängigkeit, Skaleninvarianz und Erfaßbarkeit über Stichprobenerhebungen.
Der Flächenanteil der größten Einzelfläche (LPI), der Landschaftsformindex (LSI) und der mittlere Formindex (MSI), der Randlinienlänge und Anzahl Einzelflächen kombiniert, sind in den untersuchten Beispielen gut zur Strukturanalyse geeignet. Die fraktalen IndizesMPFD undDLFD sind zwar prinzipiell zur Strukturcharakterisierung geeignet, für das vorhandene Datenmaterial zeigten diese Indizes jedoch nur unbefriedigende differenzierende Eigenschaften.
Tendenzfreie Schätzungen aus Stichproben sind nur für solche Indizes möglich, die aus tendenzfrei schätzbaren Komponenten bestehen (Fläche, Randlinienlänge). Auch die fraktale Flächen/Umfang Dimension (DLFD) lieferte diesbezüglich befriedigende Ergebnisse.
Summary
Structural diversity is an important and difficult to measure landscape characteristic. It cannot be measured directly but is described by indices, that base upon measurable attributes such as number, size, shape and edge-length of individual areas and distance between areas.
On the basis of a sample map the study analyses four principal properties of indices: Their ability to differentiate between obviously different structures, their mutual statistical independence, their scale invariance and the possibility to estimate them from samples.
The indicesLPI, LSI andMSI proved to be suitable for an analysis of spatial structure. The fractal indicesMPFD andDLFD showed less ability to differentiate between different structures.
Unbiased sample-based estimates (using square windows of the entire map) are feasible only for indices which are composed of components which can be estimated without bias themselves (absolute area, length of edge). Also the fractal area/perimeter dimension gave good estimates in the sampling study.
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Traub, B., Kleinn, C. Zur Erfassung von Flächenfragmentierung und struktureller Diversität. Forstw Cbl 118, 39–50 (1999). https://doi.org/10.1007/BF02768972
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DOI: https://doi.org/10.1007/BF02768972