Analysing the spatial structure of the Estonian landscapes: which landscape metrics are the most suitable for comparing different landscapes?

Abstract

We calculated 15 landscape metrics on 35 Estonian landscapes and performed factor and principal component analyses in order to determine which landscape metrics work on Estonian Basic Map and which do not. The results showed that there are four main components that describe landscape structure: dominance, contrast, shape complexity, and composition. We suggest the following landscape metrics for measuring these aspects respectively: ED or SIDI, TECI or ECON_MN, SHAPE_MN, and PRD. However, the selection of the metrics always depends on the purpose of the study and the character of the ecological process. Principal component analysis also showed that heights and plains have more heterogeneous landscape than lowlands. Moreover, although the heights (Otepää and Haanja) and the plains with urban areas (Tallinn and Tartu) have high fragmentation and heterogeneity, they have different contrast values: urban areas have very low contrast while in the case of heights contrast is high.

Factors biasing the correlation structure of patch level landscape metrics

“…Therefore, it is advisable 466 to choose the metrics which can be justified in the given analysis. 467 This is in accordance with the findings of Uueemaa et al (2011) and 468 Leitao and Ahern (2002).…”

Indicadores De Paisagem Da Bacia Do Rio Iraí Na Região Metropolitana De Curitiba (Pr)

“…Na Figura 3 são apresentados os indicadores de forma que descrevem a complexidade geométrica geral dos polígonos. O indicador SHAPE_MN pode dar informações da influência humana sobre paisagens, porque o seu valor é significativamente menor para áreas com padrão de paisagem mais homogêneo (UUEMAA et al, 2011). Portanto, o valor inferior apresentado pela água pode estar relacionado ao formato dos lagos e represas artificiais, como é o caso da represa do Iraí, que recobre uma área bastante expressiva.…”

“…Este índice que quantifica características fundamentais da paisagem é útil em várias análises estruturais da paisagem (UUEMAA et al, 2011).…”

Landscape Metrics: Past Progress and Future Directions

“…The growing number of available indices created new challenges for landscape ecologists in the form of correlation and redundancy. Researchers responded by focusing investigations on selecting parsimonious sets of metrics using established methods such as factor analysis [46], classification trees [8], and principal components analysis (PCA) [47,48]. Advances in this realm have led to increased sensitivity analyses in recent years to improve landscape metric selection [49][50][51] and identify consistent patterns across both spatial scale and time [52,53].…”

“…Specifically, Uuemaa et al [48] found that a large component of the variation of ecological response variable(s) is only moderately well-explained with landscape metrics when using correlation analysis. Where studies have been successful at uncovering relationships between spatial patterns and ecological processes [66], the relationships are often not statistically significant and have limited explanatory power, thus, they may not provide any real causal understanding of the underlying ecological mechanisms [12, 69•].…”