Landscape structure metrics are widely used for developing relationships with measures of biodiversity, for assessing landscape change and for comparing landscapes under different management strategies (Saura, et al., 2008; Simova and Gdulova, 2012; Turner, et al., 2001). Despite this the extensive use of metrics has been criticised, with use considered inappropriate or inaccurate in several studies (Li and Wu, 2004; Peng, et al., 2010). Such a ‘misuse’ of landscape structure metrics has been attributed to a lack of understanding of metric calculation and associated limitations, which has arisen due to the increased availability of landscape pattern calculation software and the sheer number of available metrics (Li, et al., 2005;
Peng, et al., 2010; Turner, 2005).
Numerous landscape structure metrics have been developed for the quantification of landscape pattern (Fortin, et al., 2003; HainesYoung and Chopping, 1996; McGarigal, et al., 2012). In most cases, individual landscape structure metrics describe either the composition or the configuration of the landscape, and not both (Simova and Gdulova, 2012). For example, the most widely used measurement of landscape heterogeneity is the quantification of structural diversity which considers only the composition of the landscape, through the use of the Shannon’s diversity index (Fjellstad, et al., 2001; Tews, et al., 2004). As such, this index provides similar values of heterogeneity for landscapes despite having different configurations of land cover (Fortin, et al., 2003; Li and Wu, 2004). It is argued that indices which combine multiple components of spatial pattern into a single value are difficult to interpret (Gustafson, 1998; Li and Wu, 2004), consequently several metrics are often required to capture the various aspects of landscape pattern, and in turn landscape composition and configuration (Li, et al., 2005). Landscape pattern aspects summarised using these metrics include: area/edge, aggregation, diversity, and contrast (see section 1.6.2). The use of several landscape structure metrics ensures quantification of the different components of landscape structure, however many indices are redundant as they quantify the same information in different ways, e.g. total edge and edge density (when comparing landscapes of the same size) (McGarigal and Marks, 1995). Several metrics are also statistically correlated and, as such, are empirically redundant (Riitters, et al., 1995; Schindler, et al., 2008). Strong
60 correlation occurs between several landscape level metrics because they are based on the same variability in patch attributes that operate over the landscape scale (McGarigal and Marks, 1995).
In addition to the choice of metric to measure landscape pattern, another factor to be considered is spatial scale which influences metric behaviour (Baldwin, et al., 2004; Schindler, et al., 2008; Tischendorf, 2001). Spatial scale may refer to the extent (landscape size), grain size (pixel size) and thematic resolution of the map (classification level) (Simova and Gdulova, 2012). The calculation of landscape metrics via a moving window analysis provides another assessment of scale due to the size of the window that is specified by the user (Gaucherel, 2007; McGarigal and Marks, 1995). Many studies fail to identify the appropriate scale for the analysis of landscape pattern (Gustafson, 1998), and as such inaccurate inferences may be drawn when comparing landscapes (Simova and Gdulova, 2012).
Studies have investigated the effects of spatial scale on the behaviour of landscape structure metrics, yet often yield contrasting results and, to date, not all metrics have been studied (Baldwin, et al., 2004; Li and Wu, 2004; Simova and Gdulova, 2012). Contrasting results have been obtained mainly due to differences in the classification of landscapes being used (i.e. land cover classification) (Turner, et al., 2001). Identification of the most appropriate scale for capturing landscape patterns should therefore consider the effects of scale on a collection of landscapes derived from differing land cover classifications. Furthermore, metrics respond differently to changes in spatial scale depending on what attributes of the landscape they measure, adding further complexity to scale choice. Most notably, Wu et al., (2004; 2002) investigated the effect of grain size and extent on metric behaviour and identified predictable responses for some metrics, particularly in response to changes in grain size. Metrics were grouped into three categories based on their responses to changes in extent and grain size: type I metrics show predictable scaling relations, type II metrics show stepwise scaling relations and type III metrics show no predictable response (Wu, 2004; Wu, et al., 2002). Although these studies have identified the different responses of metrics to changes in scale, they have failed to consider how the discriminating ability of metrics changes with scale between different landscapes
61 or landscape types. The ability to discriminate between landscapes, particularly landscape types which differ in their intrinsic character (i.e. National Character Areas), is particularly important when using landscape structure metrics to aid landscape planning or to develop relationships with biodiversity (Garcia-Feced, et al., 2010). In an attempt to bridge this gap, Garcia-Feced et al., (2010) investigated whether the discriminating ability of metrics was consistent across scales, yet this considered only a limited number of metrics (eight) and only forested Mediterranean landscape types. To date no study has investigated the effect of scale on the discriminating ability of metrics between UK landscape types, using landscape data applicable to the planning and decision making made in the UK.
3.1.1 Aims
The aims of the work reported in this chapter are to: -
1. Identify metrics that discriminate between selected landscapes, and determine whether changing the grain size (resolution) impacts the ability of those metrics to discriminate between different landscapes at a national (NCA) and county (Warwickshire 1 km grid squares) scale.
2. Identify the best scale at which data needs to be collected for characterising landscapes and discriminating between them.
3. Compare the effect of spatial scale on landscape structural metrics between two different landscape data sources (LCM 2000 and PH1 2000) with different thematic resolution (land cover classification).
Work on these three aims will test the hypothesis that characterisation of landscape pattern and discrimination between landscapes by landscape structure metrics is consistent across scales.
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