There are many software packages that provide methods for analysing patterns observed in remote sensed image files and raster grids. Some of these focus on the process of classifying pixels or grid cells, in order to identify land-use and vegetation cover. A second and often related procedure is that of describing and analysing the observed patterns. For example, it has been found that the quality of classification of forested regions can be considerably improved through the use of texture analysis (Zhang et al., 2004), which may utilise measures of the kind described below or a range of other forms of surface variability analysis.

As we have seen in the preceding subsections, descriptive statistics may be simple summaries of the frequencies of particular classification values or categories, which are non-spatial attributes of the dataset, or they may provide spatial measures or landscape metrics. A substantial number of such metrics have been devised over the last 50 years, principally in the field of landscape ecology, and a number of software packages and toolkits developed to undertake such analysis. The most well-known package, Fragstats, provides much of the background for other packages, such as Fragstats for ArcView, Patch Analyst (which provides both vector and raster-based patch analysis) and the pattern analysis facilities in SAGA.

The TEXTURE set of image transform operators in Idrisi supports several such measures, and we start by using this facility to clarify the operation of some of these metrics. TEXTURE actually supports four separate sets of image texture analysis: variability; fractal analysis; class frequency; and edge analysis. These analyses are typically performed on integer-valued image files only. Figure 5‑13 illustrates the range of measures provided for the first of the categories, variability. The input is a single image file and the output is again an image file, this time with each pixel representing the computed (i.e. transformed) value computed from the input image. Note that a kernel of pixels is used for each calculation, in this case selectable from 3x3, 5x5 or 7x7 pixel blocks. These operations are essentially pre-defined map algebra focal operations, as described in Section 4.6. The second category of texture analysis functions, fractal dimension analysis takes a single input image provides the source data and the output is a generated fractal dimension image based on estimated pixel dimension values using a 3x3 kernel. The fractal dimension is itself computed using the estimated slope (or reflectance value equivalent if a remotely-sensed image) over the kernel (see also, Section 6.7.1.16).

The remaining two sets of texture analysis provided in Idrisi, class frequency and edge analysis are fairly self-explanatory. The class frequencies are computed for each pixel for a pre-specified class over a 3x3, 5x5 (quasi-octagonal) or 7x7 (quasi-octagonal) neighbourhood and then mapped. For a given class, 40 say, most pixels will show a frequency of 0, some will show 1, 2… etc. up to the possible maximum of nxn where n is the kernel size. Edge analysis is effectively edge enhancement or filtering, as described in Section 4.6.2, i.e. operations applied using a kernel to every pixel, with one of 8 directional 3x3 filter kernels applied (derived from Pratt, 1991, and Jain, 1989), e.g.

North filter:

row 1:  1  1  1

row 2:  1 -2  1

row 3: -1 -1 -1

Below, we provide summary details of both the non-spatial and spatial metrics based on an edited version of the Fragstats documentation (with their kind permission). Fragstats originated text is shown with a greyed background. As noted above, these metrics cover a wide range of measures, many of which are closely correlated, and only a subset of which are implemented in widely available software packages.

The common usage of the term “landscape metrics” refers exclusively to indices developed for categorical map patterns. Landscape metrics are algorithms that quantify specific spatial characteristics of patches, classes of patches, or entire landscape mosaics. A plethora of metrics has been developed to quantify categorical map patterns. These metrics fall into two general categories: those that quantify the composition of the map without reference to spatial attributes, and those that quantify the spatial configuration of the map, requiring spatial information for their calculation (McGarigal and Marks 1995, Gustafson 1998).

Although a large part of landscape pattern analysis deals with the identification of scale and intensity of pattern, landscape metrics focus on the characterisation of the geometric and spatial properties of categorical map patterns represented at a particular scale (grain and extent). Thus, while it is important to recognise the variety of types of landscape patterns and goals of landscape pattern analysis, Fragstats focuses on landscape metrics as they are applied in landscape ecology… Landscape is not necessarily defined by its size; rather, it is defined by an interacting mosaic of patches relevant to the phenomenon under consideration (at any scale).

P: Patch-level metrics are defined for individual patches, and characterise the spatial character and context of patches. In most applications, patch metrics serve primarily as the computational basis for several of the landscape metrics, for example by averaging patch attributes across all patches in the class or landscape; the computed values for each individual patch may have little interpretive value.

C: Class-level metrics are integrated over all the patches of a given type (class). These may be integrated by simple averaging, or through some sort of weighted-averaging scheme to bias the estimate to reflect the greater contribution of large patches to the overall index. There are additional aggregate properties at the class level that result from the unique configuration of patches across the landscape.

L: Landscape-level metrics are integrated over all patch types or classes over the full extent of the data (i.e., the entire landscape). Like class metrics, these may be integrated by a simple or weighted averaging, or may reflect aggregate properties of the patch mosaic. In many applications, the primary interest is in the pattern (i.e., composition and configuration) of the entire landscape mosaic.