Landscape structure analysis of Kansas at three scales

Landscape and Urban Planning 52 (2000) 45±61

Landscape structure analysis of Kansas at three scales Jerry A. Grif®tha,*, Edward A. Martinkob, Kevin P. Pricea a

Department of Geography and Kansas Applied Remote Sensing Program, University of Kansas, Lawrence, KS 66045, USA Department of Ecology and Evolutionary Biology, Kansas Applied Remote Sensing Program and Kansas Biological Survey, University of Kansas, Lawrence, KS 66045, USA

b

Received 18 January 2000; received in revised form 2 June 2000; accepted 17 August 2000

Abstract Recent research in landscape ecology has sought to de®ne the underlying structure of landscape pattern as quanti®ed by landscape pattern metrics. One method used by researchers to address this question involves statistical data reduction techniques. In this study, principal components analysis (PCA) was performed on 27 landscape pattern metrics derived from a Kansas land cover data base at three spatial resolutions: 30 m, 100 m, and 1 km. A hexagonal sampling grid was used to subset the landscape and FRAGSTATS software was used to calculate landscape pattern metrics. The PCA reduced the number of variables from 27 to 5. A ®ve-component PCA solution explained between 81 and 89% of the variation in the data set. The components were interpreted as overall landscape texture, patch shape and size, cropland and grassland class-speci®c metrics, patch interspersion, and a nearest neighbor attribute. These ®ve dimensions were identi®ed at each resolution level. The ®rst component was stable throughout the resolution levels, whereas the order of importance changed for the latter four components. That most components consistently appeared at each resolution level supports the use of the same subset of pattern metrics for landscape monitoring in the region at different resolutions. The individual metrics emerging as most important were similar to those noted in other research and include the modi®ed Simpson's diversity index, the area-weighted mean patch fractal dimension, the interspersion and juxtaposition index, and the largest patch index for grasslands. # 2000 Elsevier Science B.V. All rights reserved. Keywords: Landscape monitoring; Landscape metrics; Principal components analysis; Kansas

1. Introduction Landscape ecology deals with the biological, physical, and societal causes and consequences of spatial variation in landscapes. New spatial tools such as geographic information systems (GIS) and remote sensing have given geographers and ecologists unprecedented capacity to quantify land cover pattern and

*

Corresponding author. Tel.: ‡1-785-864-3515; fax: ‡1-785-864-0392. E-mail address: [email protected] (J.A. Griffith). 0169-2046/00/$20.00 # 2000 Elsevier Science B.V. All rights reserved. PII: S 0 1 6 9 - 2 0 4 6 ( 0 0 ) 0 0 1 1 2 - 2

understand spatial heterogeneity and landscape structure (Turner and Carpenter, 1998). This technology has simpli®ed landscape structure characterization through measures referred to as landscape pattern metrics. Landscape pattern metrics are measurements designed to quantify and capture aspects of landscape pattern and include such measures as fragmentation indices, patch shape indices, or the percentage of an area occupied by the largest contiguous patch of grassland. Metrics capturing aspects of landscape pattern are needed to correlate landscape spatial pattern with important environmental attributes, and to test hypotheses concerning the relationship

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J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61

of landscape pattern to human and ecological processes. The structure of landscapes can include both composition and con®guration. Landscape composition refers to features related to the presence or amount of land cover types without being spatially explicit, whereas landscape con®guration refers to the spatial distribution of cover types within the landscape and includes measures of the placement of cover types relative to one another or shapes of patches (McGarigal and Marks, 1995). For a stateof-the-art review on quantifying landscape pattern, see Gustafson (1998), and for background on landscape pattern metrics, see O'Neill et al. (1996, 1988) and Turner (1989). The number of measures used as landscape pattern metrics and produced by several computer programs is seemingly in®nite (Mladenoff and DeZonia, 1999; McGarigal and Marks, 1995; Baskent and Jordan, 1995; Baker and Cai, 1992; Turner, 1990). Landscape pattern metrics can be calculated on the overall landscape, a speci®c land cover class such as forest, or on each patch (contiguous unit) of land cover. Considering all of these levels, one commonly used software program, FRAGSTATS, can calculate a staggering 100‡ metrics. Because the number of available metrics is potentially so large, many are correlated. One focus of research, therefore, has been ®nding a subset of pattern metrics comprising a minimum set to adequately describe landscape pattern (EPA, 1994). A recent thread in the landscape ecology literature have been the data reduction analyses of pattern metrics through factor analysis (FA) and principal components analysis (PCA) (Rogers, 1993; Riitters et al., 1995; Cain et al., 1997; Tinker et al., 1998).

arrangement of tonal change (or in the case of land cover maps, change in land cover type) (Reed, 1986). The landscapes analyzed by Rogers were of limited geographic extent, however, and only class-level metrics for cut and non-cut forests were evaluated. Riitters et al. (1995) performed a factor analysis of 55 landscape metrics calculated from 85 USGS (200 m resolution) Land Use Data (LUDA) quadrangles. More research is needed to determine if the same dimensions and pattern metrics found in past studies are identi®ed using different data sets. Riitters et al. (1995) recommend analyzing maps of different scale, evaluating additional metrics, or considering different regions. Most of the pattern metrics they used were landscape-level rather than class-level (e.g. cropland or grassland) metrics. In a separate PCA of many of the metrics used by Riitters et al. (1995), Cain et al. (1997) found four consistent factors, the most important of which was image texture as represented by maximum patch size or contagion. Regardless of spatial resolution or number of attributes, this dimension explained the most variance. The pattern emerging from these analyses is that overall landscape texture, and patch shape and size are recurring underlying structural components of landscape pattern. In fact, Li and Reynolds (1995) contend that ®ve attributes theoretically describe landscape structure: (a) number of cover types, (b) proportion of each type, (c) spatial arrangement of patches, (d) patch shape, and (e) contrast between patches. Demonstrating whether these attributes appear in different landscapes, such as an agricultural landscape, would be useful to assess their robustness.

1.1. Past studies of landscape metrics using PCA

In the current study, con®rmatory analysis of past PCA studies is performed. Incorporated in this study, however, is a broader set of metrics, a larger land area than in some past studies, and a predominantly agricultural region. Speci®c objectives of this research are to: (1) identify the principal landscape pattern measures for characterizing the agricultural landscape of Kansas, and (2) determine whether the main dimensions of landscape pattern across Kansas, as characterized by the chosen pattern metrics, are the same at three spatial resolutions: 30 m, 100 m, and 1 km.

Examples of past studies using PCA include Rogers (1993), who performed PCA on 26 landscape pattern metrics calculated with FRAGSTATS on forested areas in British Columbia. Her analysis resulted in three interpretable components identi®ed as a composite measure of texture or interspersion (density, edge, numeric abundance), a measure of areal extent (patch size, relative abundance), and spatial distribution of patches. ``Texture'' is important for describing landscape pattern and is de®ned as the frequency or

1.2. Objectives

J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61

2. Methods 2.1. Study area and land cover data set The state of Kansas was chosen as the study area because of the availability of a statewide land cover dataset with an estimated 90% accuracy (Whistler et al., 1995). Land use in Kansas is predominantly dry farming and rangeland in the west with a growing concentration of center-pivot irrigation ®elds. More intensive row crop agriculture (largely soybean and corn) occurs as one moves eastward. Urbanization occurs primarily in the eastern half of the state, with focal centers in Wichita and the lower Kansas River Valley (Fig. 1). Derived from 30 m Landsat Thematic Mapper (TM) satellite imagery from 1988 to 1991 (Whistler et al., 1995), the classi®ed images were converted to a vector data set for public distribution as contractually speci®ed. Because the original classi®ed raster data set was not available, this vector data set was rasterized to 30 m, 100 m and 1 km pixel sizes,

47

resulting in three data resolution levels. The six land cover categories represented in the dataset were urban, cropland, grassland, forest, water, and barren. 2.2. Landscape pattern metrics The public domain software package FRAGSTATS 2.0 was used to calculate landscape pattern metrics (McGarigal and Marks, 1995). Land cover patches were delineated as contiguous cells of the same cover type in the cardinal directions only. Procedures for determining a subset of metrics from FRAGSTATS for analysis followed Riitters et al. (1995). For the 97 initial landscape metrics examined, Spearman correlation coef®cients for pairs of metrics from the 100 m data set were examined. Pairs of metrics with correlation coef®cients over 0.9 were examined, with one of the variables often excluded from further analysis. Some metrics that were not adjusted for size were also excluded, because some of the sampling units straddled the edge of the land cover map and thus were

Fig. 1. State of Kansas, with boundaries of ecoregions (based on Omernik, 1987).

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J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61

not fully covered by the land cover data set. Metrics that were not area-weighted were excluded, as were those that were inappropriate in this predominantly non-forested region, such as core area metrics. Metrics that were exceptions to the above rules were retained due to the exploratory nature of this study. For example, the modi®ed Simpson's diversity index, the Shannon diversity index, and contagion were retained even though all are highly correlated, because it was desired to see which variable was most strongly correlated with a resulting component. Other variables retained were the cropland mean shape index and the grassland mean edge contrast index, which applies user-de®ned weights of contrast between edge types. Assigned weights were based on logical values of how edge type might relate to water quality, with weights highest for edges having higher intensity human use next to natural edges (forest and grassland). Metrics were calculated for both the entire landscape, and also for the grassland and cropland cover classes. After omitting variables based on the above criteria, 27 landscape metrics remained for the PCA, including 14 class-speci®c and 13 landscape-level metrics. Table 1 lists acronyms and provides brief description of the metrics used in this study. 2.3. Principal components analysis (PCA) PCA is a mathematical transformation that forms new variables that are linear combinations of the original variables (Stevens, 1996), and that are uncorrelated with each other. Ideally, only a few components are needed to account for most of the variance, hence the use of PCA as a data reduction technique. One product of PCA is a set of ``factor loadings,'' which are the correlations between each original landscape pattern metric and the speci®c component. Interpretation of each component is a subjective process, and is based on which metrics load highly on each component (i.e. are highly correlated). To perform a PCA, it was necessary to subdivide the Kansas land cover data set to obtain samples and variance. Sixty-seven samples were clipped from the Kansas land cover data set using 2560 km2 hexagons (Fig. 2). Choice of sampling unit was based on the availability of the hexagonal sampling frame used by the US Environmental Protection Agency's Environmental Monitoring and Assessment Program to monitor land-

Fig. 2. Land cover map of Kansas based on 30 m Landsat TM data, shown with the hexagonal sampling grid used to subset the data base. Landscape pattern metrics were then calculated for each of the hexagonal land cover subunits. The land cover data is from Whistler et al. (1995).

scapes and ecosystems (White et al., 1992; Hunsaker et al., 1994). The hexagon size was selected to optimize tractability of data processing while still allowing an adequate number of samples to perform a PCA. Also, hexagons had to be large enough to minimize situations in the 1 km data set resulting from aggregation effects that often resulted in only one or two patches of cover types occurring within a hexagon. These situations prevented the calculation of several metrics, including coef®cient of variation, standard deviation, and the interspersion and juxtaposition index. Hexagons straddling the edge of the state and having more than 10% of their area outside the state boundary were not included in the analysis. FRAGSTATS was then run on each hexagonal landscape sample unit. The number of components chosen to retain in a PCA can be determined through several methods (Sharma, 1996). In practice, however, subjective judgment and interpretability of the components play a large role in determining how many to retain (Sharma, 1996). After evaluating iterations using 4-, 5-, and 6component models, a ®ve-component model was judged best for comparing across the three spatial resolution levels. To increase interpretability, an orthogonal varimax rotation, which maintained noncorrelation between the components, was used on the resulting component scores. Finally, Spearman rank correlation coef®cients were calculated to examine relationships of the components among the three resolution levels.

J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61

49

Table 1 FRAGSTATS metrics used in the principal components analysisa Acronym

Metric name (units)

Description

Patch density/size CRPD CRMPS GRMPS MPS PD

Cropland patch density (no./100 ha) Cropland mean patch size (ha) Grassland mean patch size (ha) Mean patch size (ha) Patch density (no./100 ha)

Patch density of cropland patches Mean patch size of cropland patches Mean patch size of grassland patches Total landscape area divided by the total number of patches Number of patches divided by total landscape area

Texture CONTAG

Contagion (%)

Approaches 100 when the distribution of adjacencies of individual cells among patch types becomes increasingly uneven (more clumped). Equals 0 when all patch types are equally adjacent to each other (i.e. fragmented) Sum of length of all edge segments divided by total area Edge density of grassland class The LPI of the grassland class Percentage of the landscape composed of the largest patch Diversity measure; increases with number of patch types and as the proportional distribution of area among patch types becomes more equitable Diversity measure; equals minus the sum, across all patch types, of the proportional abundance of each patch type, multiplied by that proportion. Increases under same conditions as MSIDI

ED GRED GRLPI LPI MSIDI

Edge density (m/ha) Grassland edge density (m/ha) Grassland largest patch index (%) Largest patch index (%) Modified Simpson's diversity index

SHDI

Shannon diversity index

Patch shape AWMPFD

Area-weighted mean patch fractal dimension

AWMSI

Area-weighted mean shape index

CRAWMPFD

Cropland area-weighted mean patch fractal dimension Cropland area-weighted shape index

CRMSI GRAWMPFD Nearest neighbor CRMNN GRMNN CRNNSD

Grassland area-weighted mean patch fractal dimension Cropland mean nearest neighbor district (m) Grassland mean nearest neighbor (m) Cropland nearest neighbor standard deviation (m)

MNN

Mean nearest neighbor distance (m)

GRNNCV NNCV

Grassland nearest neighbor coefficient of variation (%) Nearest neighbor coefficient of variation (%)

NNSD

Nearest neighbor standard deviation (m)

Patch shape complexity measure, weighted by patch area; AWMPFD approaches 1 for shapes with simple perimeters, and 2 for complex shapes Mean patch shape complexity, weighted by patch area; equals 1 when all patches are circular and increases as patches become non-circular AWMPFD applied to the cropland class only Mean shape index applied to cropland class only, not area weighted AWMPFD applied to the grassland class only MNN applied to the cropland class only Mean nearest neighbor distance between grassland patches Nearest neighbor distance standard deviation of cropland patches Sum of distances to the nearest neighboring patch of the same type, based on nearest edge-to-edge distance, for each patch of the corresponding patch type, divided by the number of patches of the same type Grassland nearest neighbor coefficient of variation of grassland patches The standard deviation in nearest neighbor distances divided by the mean nearest neighbor distance of the corresponding patch type, multiplied by 100 The square root of the sum of squared deviations of each patch's nearest neighbor distance, divided by the number of patches

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J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61

Table 1 (Continued ) Acronym

Metric name (units)

Description

Interspersion CRIJI GRMECI

Cropland interspersion/juxtaposition (%) Grassland mean edge contrast (%)

The IJI applied to the cropland class only Approaches 0 as the contrast of grassland edges lessens; GRMECI equals 100 when all grassland edge is maximum contrast Approaches 0 when distribution of adjacencies among patch types becomes increasingly uneven; IJI equals 100 when all patch types are equally adjacent to all other patch types

IJI

a

Interspersion and juxtaposition index (%)

Full descriptions of these metrics, and equations for their calculations are provided in McGarigal and Marks (1995).

3. Results 3.1. Correlation analysis Using the 27 metrics, there were 351 correlation pairs (full correlation matrix not shown). A notable observation and trend from the full matrices was the large number of signi®cant correlations among the metrics at 30 and 100 m, with fewer correlations at 1 km. At 30 m, there were 148 (42%) signi®cant correlation coef®cients with r-values 0.5, while at 100 m there were 152 (43%). At 1 km, there were only 101 (29%) signi®cant correlation coef®cients with rvalues 0.5. Tables 2±4 show subsets of the full correlation matrices focusing on seven metrics for each of the resolution levels. The matrices show the redundancy of several metrics, the generally lower correlation between metrics at 1 km, the difference between landscape versus class-level metrics, and the potential utility of some less frequently used metrics. For example, contagion and edge density (CONTAG, ED) are frequently used metrics in landscape studies at 30 and 100 m and both were highly correlated to most of the other metrics. At 1 km, the number of signi®cant correlations involving CONT and ED decreased as did their strength, although edge density was still highly correlated to the diversity indices (SHDI, MSIDI) at 1 km. At 30 and 100 m, the patch shape metric AWMPFD was not highly correlated to any others, indicating its ability to explain distinct information. Some nearest neighbor (NN) metrics also appear to explain new information, while others are redundant. Even though both NNCV (coef®cient of variation) and NNSD (S.D.) represent variation in nearest neighbor

distances, NNSD was correlated to most other metrics at 30 and 100 m. NNCV, in contrast, was not strongly correlated to many other metrics. The correlation tables also show some differences between two seemingly similar metrics. Contagion and the interspersion and juxtaposition index (IJI) super®cially appear related because they both address spatial distribution of patches, plus they were moderately correlated with each other. Unlike contagion, IJI was not as highly correlated to the diversity indices (SHDI, MSIDI), and was not as highly correlated to other metrics at 1 km. This suggests that IJI measures something different from contagion. Yet another contrast useful to examine is between a landscapelevel patch shape metric (AWMPFD) versus the same metric applied to a speci®c cover class, e.g. grassland (GRAWMPFD). Tables 2±4 show that GRAWMPFD was correlated to many other metrics at all resolution levels, while the landscape-level AWMPFD was not. 3.2. Principal components analysis Eigenvalues and the amount of variance explained by each component are shown in Table 5. The rotated component matrices and factor loadings for each resolution level are shown in Table 6, and are interpreted in the following sections. 3.2.1. 30 m and 100 m resolution land cover databases Results for the 30 and 100 m data sets were similar enough that they can be described together. The ®rst component was interpreted as a representation of overall landscape texture because landscape metrics

J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61

51

Table 2 Spearman's rank correlation matrix between seven selected landscape metrics and the original 27 landscape pattern metrics used in the analysis of the 30 m land cover data basea

Patch size/density CRMPS CRPD GRMPS MPS PD Texture CONTAG ED GRED GRLPI LPI MSIDI SHDI Patch shape AWMPFD AWMSI CRAWMPFD CRMSI GRAWMPFD Nearest neighbor CRMNN CRNNSD GRMNN GRNNCV MNN NNCV NNSD Interspersion CRIJI GRMECI IJI

AWMPFD

CONTAG

ED

GRAWMPFD

IJI

NNCV

NNSD

ÿ0.45 0.39

0.67 ÿ0.82 0.31 0.85 ÿ0.85

ÿ0.74 0.87 ÿ0.39 ÿ0.93 0.92

ÿ0.86 0.84 0.34 ÿ0.65 0.65

ÿ0.73 0.72

0.82 ÿ0.87

ÿ0.86 0.86

0.30 ÿ0.25 ÿ0.38

ÿ0.95

ÿ0.66 0.64 0.68 0.82 ÿ0.39 0.58 0.62

ÿ0.63 0.68 0.62 0.34 ÿ0.44 0.41 0.62

ÿ0.33 0.33 ÿ0.30 0.39 0.40 0.28

ÿ0.95 ÿ0.94 ÿ0.27 0.79 ÿ0.91 ÿ0.98

ÿ0.71 0.81 0.91

ÿ0.30 ÿ0.36

0.39 0.47

0.36

ÿ0.59 ÿ0.66

0.51 0.64

ÿ0.36 ÿ0.27 ÿ0.50

0.26 0.46 0.69

ÿ0.34 ÿ0.55 ÿ0.78

ÿ0.35

0.88

ÿ0.93

ÿ0.32

0.80

ÿ0.85

ÿ0.85 0.51 ÿ0.53 ÿ0.29 ÿ0.68

ÿ0.55 0.65 ÿ0.63

0.59 ÿ0.71 0.68

ÿ0.72 0.55

0.91 0.38

0.99

0.40 0.47 ÿ0.47 0.61

ÿ0.36 0.37 0.55

0.27 ÿ0.68 ÿ0.72 ÿ0.45 ÿ0.83 0.87 ÿ0.89

ÿ0.25

0.66 ÿ0.33 ÿ0.29 ÿ0.51 ÿ0.26 0.27 ÿ0.25 0.45 ÿ0.31 0.48 ÿ0.45

0.93 ÿ0.93 0.80 ÿ0.85 ÿ0.83 ÿ0.38 0.53 ÿ0.59 ÿ0.75 ÿ0.32 ÿ0.40 ÿ0.44 ÿ0.68 0.28 0.80 ÿ0.25 0.88 0.45

ÿ0.69 0.81 ÿ0.83

a

Only signi®cant correlation coef®cients are shown, with bold numbers signi®cant at a ˆ 0:01; others are signi®cant at a ˆ 0:05. Descriptions and names for all abbreviated metrics can be found in Table 1. For the seven metrics, AWMPFD: area-weighted mean patch fractal dimension, CONTAG: contagion, ED: edge density, GRAWMPFD: grassland area-weighted mean patch fractal dimension, IJI: interspersion and juxtaposition index, NNCV: nearest neighbor coef®cient of variation, NNSD: nearest neighbor standard deviation.

that were the most highly correlated with it describe map attribute and cell type frequencies, or the overall clumpiness or fragmentation of land cover types (e.g. MSIDI, LPI, SHDI, and CONTAG). The second component was interpreted as a representation of patch shape/size because metrics describing shape and size (AWMPFD, AWMSI, MPS, and GRMNN) were most highly correlated with it. The third component was identi®ed as patch interspersion because IJI and CRIJI were most highly correlated to it, as

were other variables indirectly affected by interspersion, such as edge contrast and nearest neighbor distance coef®cient of variation (GRMECI and NNCV). The fourth component identi®ed grassland and cropland class-level metrics, since the highest loading pattern metrics were GRLPI, GRMPS, and CRAWMPFD. At 30 m, a single nearest neighbor metric, NNCV, was moderately correlated with the ®fth component, but contributed little to the overall explained variance. At 100 m, the ®fth component was

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J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61

Table 3 Spearman's rank correlation matrix between seven selected landscape metrics and the original 27 landscape pattern metrics used in the analysis of the 100 m land cover data basea

Patch size/density CRMPS CRPD GRMPS MPS PD Texture CONTAG ED GRED GRLPI LPI MSIDI SHDI Patch shape AWMPFD AWMSI CRAWMPFD CRMSI GRAWMPFD Nearest neighbor CRMNN CRNNSD GRMNN GRNNCV MNN NNCV NNSD Interspersion CRIJI GRMECI IJI

AWMPFD

CONTAG

ED

GRAWMPFD

IJI

NNCV

NNSD

ÿ0.29 0.25

0.67 ÿ0.81 0.32 0.89 ÿ0.89

ÿ0.71 0.84 ÿ0.39 ÿ0.92 0.92

ÿ0.82 0.78 0.47 ÿ0.49 0.49

ÿ0.72 0.71

0.28 ÿ0.28

ÿ0.82 0.82

0.33 ÿ0.33

0.74 ÿ0.82 0.30 0.92 ÿ0.92

ÿ0.96

ÿ0.56 0.51 0.54 0.88 ÿ0.37 0.51 0.54

ÿ0.63 0.62 0.56 0.32 ÿ0.40 0.41 0.63

0.26

0.93 0.51 ÿ0.42

ÿ0.47 ÿ0.35 ÿ0.32 ÿ0.29 ÿ0.26 0.28

ÿ0.96 ÿ0.95 ÿ0.26 0.78 ÿ0.90 ÿ0.97

0.99 ÿ0.73 0.83 0.91

0.41

0.24 ÿ0.54

ÿ0.56

ÿ0.28 0.51

0.51 0.50 0.71

ÿ0.60 ÿ0.60 ÿ0.76

0.88

ÿ0.91

ÿ0.81 0.32 ÿ0.41

0.80

ÿ0.81

ÿ0.51

ÿ0.62 0.66 ÿ0.63

0.63 ÿ0.68 0.62

0.26 ÿ0.64 0.49

0.80 ÿ0.81 ÿ0.78 ÿ0.29 0.52 ÿ0.61 ÿ0.77 0.28

ÿ0.33 ÿ0.31 0.49

0.45

ÿ0.26 ÿ0.69 ÿ0.63 ÿ0.58 ÿ0.79 0.93 ÿ0.89

0.56 ÿ0.49 0.49 ÿ0.58

ÿ0.31 0.44 ÿ0.51 0.36 0.30 0.71 0.88 0.56

ÿ0.77 0.73 ÿ0.79

a Only signi®cant correlation coef®cients are shown, with bold numbers signi®cant at a ˆ 0:01; others are signi®cant at a ˆ 0:05. Descriptions and names of the abbreviated metrics can be found in Table 1.

represented by one nearest neighbor variable for grassland, GRNNCV. 3.2.2. 1 Km resolution land cover database Only 61 of the original hexagon sampling units quali®ed for use in the analysis based on the conditions explained in the methods section. While the same aspects of landscape structure found at ®ner resolutions also occurred at 1 km, their relative orders of importance differed. The ®rst component represented landscape texture for the same reasons noted

previously-MSIDI, SHDI and LPI were highly loaded on this component (Table 4). The edge metrics ED and GRED, as well as contagion, also loaded moderately highly on this component, but these metrics were somewhat cross-loaded with the third component as well. In contrast to the ®ner resolutions, the second component at 1 km represented the class-speci®c cropland and grassland metrics because GRLPI and GRMPS were most strongly correlated with this component. Also loading highly were the cropland patch shape metrics (CRMSI and CRAWMPFD), and

J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61

53

Table 4 Spearman's rank correlation matrix between seven selected landscape metrics and the original 27 landscape pattern metrics used in the analysis of the 1 km land cover data basea AWMPFD Patch size/density CRMPS CRPD GRMPS MPS PD Texture CONTAG ED GRED GRLPI LPI MSIDI SHDI Patch shape AWMPFD AWMSI CRAWMPFD CRMSI GRAWMPFD Nearest neighbor CRMNN CRNNSD GRMNN GRNNCV MNN NNCV NNSD Interspersion CRIJI GRMECI IJI

CONTAG

ED

GRAWMPFD

IJI

ÿ0.27

0.55 ÿ0.38

ÿ0.64 0.62

0.56 ÿ0.53

ÿ0.84 0.84

0.83 ÿ0.75 0.75 ÿ0.46 0.49

0.26 ÿ0.33 0.37

ÿ0.59 0.77 0.76

ÿ0.67 ÿ0.69

0.34

ÿ0.26 0.54 0.53

0.63 ÿ0.70 ÿ0.57

ÿ0.63 0.81 0.83

ÿ0.39 0.49 0.53 0.80 ÿ0.64 0.62 0.62

0.98 0.64

ÿ0.59 ÿ0.57 ÿ0.48

0.77 0.76 0.43

ÿ0.39

0.49

ÿ0.48 ÿ0.46 ÿ0.40

0.41 0.40 0.33

ÿ0.42 ÿ0.36 ÿ0.63

ÿ0.35

0.54

ÿ0.26

ÿ0.67 0.98

0.34

NNSD

0.28

0.31

ÿ0.27 ÿ0.37 ÿ0.51

ÿ0.47 ÿ0.49

0.29 0.26 ÿ0.59

0.40 0.42

0.28 ÿ0.25 0.29 ÿ0.27

NNCV

ÿ0.36

ÿ0.24

0.52

0.57

0.37

0.89

0.79 ÿ0.45

0.85 0.89

0.37

a

Only signi®cant correlation coef®cients are shown, with bold numbers signi®cant at a ˆ 0:01; others are signi®cant at a ˆ 0:05. Descriptions and names of the abbreviated metrics can be found in Table 1.

the grassland edge contrast metric (GRMECI). The third component was interpreted as patch shape, because the two patch shape metrics (AWMPFD and AWMSI) loaded highly on it. The fourth component was interpreted as a nearest neighbor aspect. Correlations between individual nearest neighbor metrics (NNCV, NNSD and MNN) and this component, however, were much stronger (r > 0:9) than at either of the ®ner resolutions. Finally, the two interspersion variables (IJI, CRIJI) were highly correlated to the ®fth component, allowing its easy labeling.

Spearman rank correlation coef®cients between the component scores at the different resolutions are shown in Table 7. The table shows signi®cant correlations between all component pairs except those involving the nearest neighbor aspects (component 5). Because this component explained little variance, it is not discussed further. The patch shape/size component (component 2) at 1 km also had relatively low correlation with the ®ner resolution levels. This is logical, due to the aggregation effects at the 1 km level that affect the shapes of patches. The fact that the 30

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J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61

Table 5 Eigenvalues and amount of variance explained from results of the principal components analysis and varimax rotation of the ®rst ®ve factors Factor

1

2

3

4

5

30 m Eigenvalue % of variance explained Cumulative % of variance explained

6.87 25.43

6.64 24.58 50.00

4.62 17.12 67.11

4.57 16.94 84.05

1.36 5.04 89.09

100 m Eigenvalue % of variance explained Cumulative % of variance explained

7.72 28.61

6.22 23.04 51.65

4.72 17.48 69.12

3.86 14.31 83.43

1.26 4.68 88.11

1 Km Eigenvalue % of variance explained Cumulative % of variance explained

7.03 26.05

5.12 19.09 45.14

4.47 16.54 61.69

2.70 10.01 71.70

2.54 9.42 81.12

and 100 m levels were more similar is also logical, since the resolution change between them is threefold compared to the 10- and 30-fold difference between them and the 1 km level. In a sensitivity analysis of landscape metrics to pixel size, Wickham and Riitters (1995) also found that landscape metrics should not be greatly affected by resolution changes between 30 and 80 m. Even though the same dimensions emerged at 1 km, this data set was slightly different, as shown by the correlation analyses and maps of component scores (Figs. 3 and 4). Mapping the component scores for the hexagon sampling units elucidated relationships between the resolution levels and placed the patterns in the context of the regional landscape (Fig. 1). Component scores were mapped into four classes using natural breaks in the histogram of score values to determine class intervals. 4. Discussion 4.1. Maps of component scores The correlation analysis showed that, in general, the behavior of the metrics in describing the Kansas landscape at 30 and 100 m was very similar. Although the metrics were less correlated at 1 km, their behavior was similar to the ®ner resolutions for the components describing landscape texture, patch interspersion, and grassland and cropland class-level metrics. The map of component scores for texture (Fig. 3) shows very

similar patterns at all levels, with eastern Kansas having the highest scores. This region of Kansas is more diverse (having more equable distribution of land cover types), more highly fragmented, and has largest patches that comprise a smaller proportion of the total hexagon area than other locations. The map of texture also shows a north-south gradient, resulting from a more stream-dissected landscape along the Nebraska border to areas further south where patches occur in larger clumps, such as in a relatively homogenous area of cropland in the southern Central Great Plains, and in the vast grasslands of the Southwestern Tablelands (Fig. 2). Western Kansas has the opposite characteristics of landscape texture compared to eastern Kansas. For the component describing shape/size (Fig. 3), there was some pattern difference between the 1 km and ®ner resolution levels, as suggested by the lower correlation values. The map shows higher component values for patch shape in the southern Flint Hills, where there are irregularly-shaped stands of post oak and jack oak forest, and also in areas that have relatively signi®cant portions of both cropland and grassland that create complex patch shapes. Patterns of interspersion were also similar across resolutions, with eastern Kansas and the Flint Hills having very high component scores (Fig. 4). These areas contain agricultural valleys and riparian forest that are interspersed with cropland. A map of the metrics speci®c to the cropland and grassland cover classes shows patterns that are roughly, but not exactly the same at 1 km (Fig. 4). Larger scores indicate more grassland and

Table 6 Rotated component matrices showing factor loadings for all three datasetsa Pattern metric

Component (30 m) 1

2

Texture CONTAG ED GRED GRLPI LPI MSIDI SHDI Patch shape AWMPFD AWMSI CRAWMPFD CRMSI GRAWMPFD Nearest neighbor CRMNN GRMNN CRNNSD MNN GRNNCV NNCV NNSD Interspersion CRIJI GRMECI IJI a

ÿ0.84 0.70 0.71 ÿ0.90 0.93 0.86

3

ÿ0.40 ÿ0.76

4 0.48 0.71

ÿ0.77

ÿ0.42

ÿ0.34 0.45 0.49 0.40

ÿ0.39 0.48 0.41

0.41 ÿ0.42 ÿ0.38

0.33

0.84

0.84

ÿ0.79

0.47

0.54

ÿ0.49 ÿ0.81 ÿ0.43 ÿ0.76 0.35

0.77

ÿ0.59

ÿ0.40 ÿ0.48 ÿ0.60 0.93 ÿ0.66 0.86

ÿ0.39 0.50 0.32

0.74 0.58 ÿ0.35

ÿ0.52

1

2

3

ÿ0.57

4

0.64 0.66 ÿ0.55 ÿ0.39 ÿ0.49

0.73

ÿ0.37

ÿ0.88 0.79 0.80

ÿ0.38 ÿ0.41

ÿ0.36 0.43 0.35

0.67

ÿ0.92 0.96 0.90

0.38 0.97 0.92

0.68 0.57

5

Component (1 Km)

0.54

0.53 0.66

ÿ0.96 ÿ0.90 ÿ0.39 0.68

0.66 0.76 0.53 0.70

ÿ0.45

0.49

ÿ0.34

0.82

2

ÿ0.66 0.78 0.76

0.91

3 0.64

ÿ0.55 0.74

0.94

4

0.39 0.54 ÿ0.31

5

0.31

ÿ0.65 ÿ0.33 0.58 0.60

0.96 0.91

ÿ0.74 0.69

0.42 0.34 0.40

0.55

0.59

ÿ0.47

0.63

ÿ0.70 ÿ0.72 0.57 0.71 ÿ0.34 0.58

0.81

ÿ0.76 ÿ0.60 0.86 ÿ0.71 0.86

1 0.53 0.70 ÿ0.62

0.33

ÿ0.50 ÿ0.34 ÿ0.56

ÿ0.31 0.35

5

ÿ0.46

0.75 0.80

ÿ0.54 ÿ0.68 ÿ0.53 0.34

ÿ0.31 ÿ0.37

0.83 0.89 0.99

ÿ0.68 0.30

0.31 0.41

J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61

Patch size and density CRPD 0.59 PD 0.55 CRMPS ÿ0.48 GRMPS ÿ0.36 MPS ÿ0.38

Component (100 m)

0.87 ÿ0.38 0.88

To make the table easier to read, loadings from ÿ0.3 to ‡0.3 are not shown. Numbers in bold show factor loadings above 0.7, or above 0.5 if not cross-loaded. 55

56

J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61

Table 7 Spearman rank correlation coef®cients between the component scores at the different resolution levelsa 30 m

100 m

Component 1 30 m 100 m 1 km

0.95 0.81

0.86

Component 2 30 m 100 m 1 km

ÿ0.84 0.32

ÿ0.35

Component 3 30 m 100 m 1 km

0.93 0.70

0.70

Component 4 30 m 100 m 1 km

0.95 0.75

0.80

Component 5 30 m 100 m 1 km

0.11 (ns)b 0.10 (ns)b

ÿ0.08 (ns)b

a For the 1 km data set, we used the particular component whose interpreted structure was the same as the ®ner resolution levels. For example, the third component for the 1 km data set (patch shape/size) is shown above in the component 2 correlations, which were interpreted as patch shape/size at the 30 and 100 m resolution levels. b ns: not signi®cant; all signi®cant correlations were signi®cant at a ˆ 0:01 except for the 1 km±30 m correlation for component 2, which was signi®cant at a ˆ 0:05.

with a high largest patch index of grassland. These areas occur in the Flint Hills, in areas of western Kansas that contain river valleys bordered by extensive grassland, and in the grasslands of the Southwestern Tablelands. Examining the mapped component scores showed that the behavior of the landscape texture component, in particular, was very robust across the levels. 4.2. Comparing PCA results across resolution levels Results of the PCA showed that the underlying structure of landscape pattern across Kansas emerging at each resolution was generally consistent. Four main dimensions of landscape pattern emerged: (1) overall

landscape texture, (2) patch shape and size, (3) classspeci®c cropland and grassland variables, and (4) patch interspersion. Additionally, nearest neighbor measures were moderately correlated with the ®fth component, and strongly correlated with the fourth component at 1 km. While the dimensions interpreted from the components were relatively consistent, their relative order of importance changed at 1 km. Although the ®rst component was interpreted identically at all three resolutions, the second component was interpreted at 30 and 100 m to represent patch shape/size, but at 1 km patch shape/size was represented by the third component (Table 6). The third component at both 30 and 100 m was identi®ed as patch interspersion, whereas at 1 km, interspersion was associated with the ®fth component. The loss of rarer land cover types such as forest or other smaller patches may play a role in its decreased importance at 1 km. Interspersion is based on edge amounts associated with patch type adjacencies; studies have demonstrated that rarer cover types become even less frequent and dominant cover types become more dominant with aggregation (Turner et al., 1989). The fourth component at 30 and 100 m was identi®ed as the cropland or grassland class-speci®c metrics, while at 1 km, these were associated with the second component (Table 6). As cropland and grassland are the two dominant cover types, they become even more dominant during aggregation. Besides the emergence of the same landscape structure at each resolution, the robustness of these dimensions is also shown in that, for nearly all components, the landscape pattern metrics loading most strongly on each were also consistent (Table 8). Some frequently used measures were found redundant for some dimensions. Although contagion is often used as a measure of fragmentation or texture, the diversity indices (SHDI, MSIDI) were more strongly correlated to the texture component in this landscape. Contagion has been one of the most frequently used pattern metrics in landscape ecology studies, yet what it actually measures is debated (Hargis et al., 1998; Frohn, 1998). The factor loading on the texture component for contagion was relatively high at 30 and 100 m, but less so at 1 km, perhaps due in part to its reliance on internal cell adjacencies (Riitters et al., 1995). Hargis et al. (1998) found contagion sensitive to patch size/shape as opposed

J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61

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Fig. 3. Maps of the component scores for each hexagon based on the results of the PCA of 27 landscape metrics. On the left is the component interpreted as landscape texture, and on the right is patch shape/size. Higher values for landscape texture indicate a landscape that is more diverse (with proportions of cover types more balanced), more fragmented with greater edge, and with largest patches that comprise smaller proportions of the total hexagon area than other locations. Higher values for shape/size indicate more complex-shaped patches with smaller mean patch sizes (overall and for cropland), and lower distance between nearest neighbors (in the overall landscape, and for the grassland cover class). For the 100 m resolution level, the directions of the signs for the component scores were reversed to match the other resolution levels.

to spatial distribution, and Frohn (1998) found the behavior of contagion unstable across resolutions. The one dimension that was not stable with respect to the most highly loading metrics was the nearest neighbor component. Although it is of note that these metrics emerged as a component, they explained little variance. More investigation is needed to document behavior of these metrics across resolution levels. Another example of redundancy is that many of the metrics were correlated with landscape texture, including edge density. Edge density is a frequently used metric in wildlife studies to describe habitat and landscapes, yet ED at all levels was correlated with most other metrics. In this study, edge was another representation of overall landscape texture as opposed to being a metric providing unique information. In other studies, edge has been associated with both texture and patch shape/size (Riitters et al., 1995;

Hargis et al., 1998). This outcome is logical because edge depends on both patch shape and size. A less frequently used metric is edge contrast. The edge contrast index for grassland (GRMECI) was associated with the interspersion component at 30 and 100 m, but was correlated with the component representing grassland±cropland class-level metrics at 1 km. The edge contrast weights used here were arbitrary, but even so, GRMECI emerged with a strong loading on the third component at 100 m. Edge contrast should be studied further, especially since Li and Reynolds (1995) submit contrast as a main component of spatial heterogeneity. 4.3. Comparison of results with past PCA studies In general, results of this study con®rm several ®ndings from previous studies of landscape pattern

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J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61

Fig. 4. Maps of the component scores for each hexagon based on the results of the PCA of 27 landscape metrics. This ®gure shows the components interpreted as patch interspersion (left) and the cropland/grassland class-speci®c metrics (right). Higher values for interspersion indicate a landscape where more patches are interspersed with a more balanced proportion of edge touching other cover types. Higher values for the cropland/grassland metrics indicate a landscape with higher grassland mean patch sizes and largest patch index of grasslands, and lower mean patch size of cropland, and more complex shaped cropland patches.

Table 8 The two landscape pattern metrics most highly correlated with each principal component at each spatial resolution levela Component

1

2

3

4

5

30 m 100 m 1 Km

MSIDI LPI MSIDI LPI MSIDI LPI

AWMPFD AWMSI AWMPFD AWMSI GRLPI GRMPS

CRIJI IJI IJI CRIJI AWMSI AWMPFD

GRMPS GRLPI GRLPI GRMPS NNSD NNCV

NNCV CRMSI GRNNCV CRMSI IJI CRIJI

a

The consistency across resolutions shows the general robustness of the components. MSIDI: modi®ed Simpson's diversity index, LPI: largest patch index, AWMPFD: area-weighted mean patch fractal dimension, AWMSI: area-weighted mean shape index, IJI: interspersion and juxtaposition index, CRIJI: cropland interspersion and juxtaposition index, GRMPS: grassland mean patch size, GRLPI: grassland largest patch index, NNCV: nearest neighbor coef®cient of variation, NNSD: nearest neighbor standard deviation, CRMSI: cropland mean shape index, GRNNCV: grassland nearest neighbor coef®cient of variation.

J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61

metrics. The number of dimensions emerging as important components (four), and the amount of variation explained by the components (81±87%), generally correspond with other studies (Riitters et al., 1995; Cain et al., 1997; Rogers, 1993). Five dimensions found from LUDA maps included patch compaction, image texture, average patch shape, patch perimeter-area scaling and the number of attribute types (Riitters et al., 1995). These factors explained 87% of the variation among 26 pattern metrics. Cain et al. (1997) used six factors, explaining 92±97% of the variation, and found that texture was consistently the ®rst factor across datasets. Rogers (1993) found three readily interpretable components. New metrics not studied in previous PCAs were found important in this study. Super®cially, descriptions of IJI appeared similar to contagion, but the calculation of CONTAG relies on cell adjacencies, while IJI relies on whole patch adjacencies and associated edge (McGarigal and Marks, 1995). Results found here suggest the two metrics explain independent information. This ®nding is interesting, because the two variables are moderately correlated (Tables 2± 4), yet appear on different components in the PCA. The fact that class-level statistics in this study emerged as a separate dimension from the same metric applied to the landscape-level is also interesting; in many studies, the focus is either on landscape-level or class-level metrics, without simultaneously addressing both. In other words, the mean patch size or largest patch index of grasslands (GRMPS or GRLPI) explains unique variation compared to the overall landscape mean patch size or largest patch index. This result points out the need to choose metrics carefully based on the particular questions being posed. 5. Conclusion At resolutions of land cover data ranging from 30 m to 1 km across Kansas, the same underlying aspects of landscape structure occurred. This information is important as new satellite sensors with different spatial resolutions are launched and data from them is used to characterize landscapes. That the same main elements of structure emerged at all three scales, however, does not mean the pattern metric values are directly comparable across resolutions. For exam-

59

ple, patch density will obviously be different between ®ne-resolution and coarse-resolution data due to aggregation effects. For general environmental monitoring purposes in this region, however, it appears that different sets of pattern metrics are not needed to monitor landscape pattern at different levels of resolution. More research is needed to determine if thresholds exist between 100 m and 1 km, because new satellite sensors such as the moderate resolution imaging spectrometer (MODIS), which has an intermediate resolution of 500 m, might occupy the general region of some threshold value. This research would be useful because some slight differences in mapped patterns and correlations between component scores occurred at 1 km. More research is also needed on how results would differ with newer high-resolution imagery such as the 4 m multispectral data from the IKONOS satellite. The market for this imagery is considered to be for small-scale uses such as urban planning, however, and large data processing requirements may limit its use in regional landscape ecology studies. Results presented here support focusing on a few metrics for landscape characterization and monitoring. A smaller subset of pattern metrics to monitor similar agricultural landscapes in the midwestern US might include the modi®ed Simpson's diversity index and the largest patch index, the area-weighted mean patch fractal dimension or area-weighted mean shape index, the interspersion and juxtaposition index, and the largest patch index and mean patch size for grasslands. A speci®c landscape metric known to relate to an ecological attribute, however, can still be valuable even though it may provide redundant information. Edge metrics were redundant with both texture and patch shape/size metrics. The use of edge metrics, however, will continue to be valuable in wildlife studies, wherein edge is a habitat element proven to be important for certain organisms. Another group of metrics potentially useful in landscape monitoring are nearest neighbor measures (NNCV, NNSD, and GRNNCV). Hargis et al. (1998) suggested these be added to any minimal subset of metrics because they were uncorrelated with other metrics. Our results were not conclusive on the importance of nearest neighbor metrics. On one hand, our results supported the suggestion of Hargis et al. (1998) in that one of the components at each resolution level had a nearest

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J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61

neighbor metric correlated to it. However, the nearest neighbor metrics were correlated with a component that explained little variance and were correlated with other components as well. Furthermore, the correlation analysis showed strong correlations between mean nearest neighbor distance (MNN) and both contagion and IJI. A single PCA does not identify all important dimensions of possible aspects of landscape pattern (Riitters et al., 1995). The choice of metrics used in the analysis affects the outcome. Limitations to this study are that it did not include every possible metric, such as patch richness or core area metrics. The number of meaningful components may also be debated. Nonetheless, if similar underlying structures of landscape pattern are found in many situations, con®dence in the ®ndings will be greater (Riitters et al., 1995). The results presented here support the use of pattern metrics that represent overall landscape texture and patch shape/ size as consistent and important measures for characterizing landscape pattern in Kansas. Acknowledgements The authors wish to thank three anonymous reviewers for their reviews and helpful comments. The lead author was supported by a dissertation fellowship from the University of Kansas. References Baker, W., Cai, Y., 1992. The r.le programs for multiscale analysis of landscape structure using the GRASS geographic information system. Landscape Ecol. 7, 291±302. Baskent, E., Jordan, G., 1995. Characterizing spatial structure of forest landscapes. Can. J. For. Res. 25, 1830±1849. Cain, D., Riitters, K., Orvis, K., 1997. A multi-scale analysis of landscape statistics. Landscape Ecol. 12, 199±212. EPA, 1994. Landscape Monitoring and Assessment Research Plan. EPA 620/R-94/009. Environmental Monitoring Systems Laboratory, Las Vegas, NV. Frohn, R., 1998. Remote Sensing for Landscape Ecology. Lewis Publishers, Boca Raton, FL, 99 pp. Gustafson, E., 1998. Quantifying landscape spatial pattern: what is the state-of-the-art? Ecosystems 1, 143±156. Hargis, C., Bissonette, J., David, J., 1998. The behavior of landscape metrics commonly used in the study of habitat fragmentation. Landscape Ecol. 13, 167±186.

Hunsaker, C., O'Neill, R., Jackson, B., Timmins, S., Levine, D., Norton, D., 1994. Sampling to characterize landscape pattern. Landscape Ecol. 9 (3), 207±226. Li, H., Reynolds, J., 1995. On de®nition and quanti®cation of heterogeneity. Oikos 73, 280±284. McGarigal, K., Marks, B., 1995. FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. USDA Forest Service General Technical Report PNW-GTR-351, Paci®c Northwest Research Station, Portland, OR, 122 pp. Mladenoff, D., DeZonia, B., 1999. APACK 2.11 Analysis Software User's Guide. Unpublished document available on-line at ftp:// ¯el.forest.wisc.edu/APACK/VERSION211/. Omernik, J., 1987. Ecoregions of the conterminous United States. Ann. Assoc. Am. Geogr. 77, 118±125. O'Neill, R., Hunsaker, C., Timmins, T., Jackson, B., Jones, K., Riitters, K., Wickham, J., 1996. Scale problems in reporting landscape pattern at the regional scale. Landscape Ecol. 113, 169±180. O'Neill, R., Krummel, J., Gardner, R., Sugihara, G., Jackson, B., DeAngelis, D., Milne, B., Zygmunt, B., Christensen, S., Dale, V., Graham, R., 1988. Indices of landscape pattern. Landscape Ecol. 1, 153±162. Reed, B., 1986. Vegetation mapping using a spectral/textural hierarchical classi®cation. Master's Thesis, Department of Geography, University of Kansas. Riitters, K., O'Neill, R., Hunsaker, C., Wickham, J., Yankee, D., Timmins, S., Jones, K., Jackson, B., 1995. A factor analysis of landscape pattern and structure metrics. Landscape Ecol. 101, 23±39. Rogers, C., 1993. Describing landscapes: indices of structure. Master's Thesis, Department of Natural Resources Management, Simon Fraser University. Sharma, S., 1996. Applied Multivariate Statistics. Wiley, New York, 493 pp. Stevens, J., 1996. Applied Multivariate Statistics for the Social Sciences, 2nd Edition. Lawrence Erlbaum, Hillsdale, NJ, 629 pp. Tinker, D., Resor, C., Beauvis, G., Kipfmueller, K., Fernandez, C., Baker, W., 1998. Watershed analysis of forest fragmentation by clearcuts and roads in a Wyoming forest. Landscape Ecol. 13, 149±165. Turner, M., 1989. Landscape ecology: the effect of pattern on process. Ann. Rev. Ecol. Syst. 20, 171±197. Turner, M., 1990. Spatial and temporal analysis of landscape patterns. Landscape Ecol. 41, 21±30. Turner, M., Carpenter, S., 1998. At last: a journal devoted to ecosystems. Ecosystems 11, 1±4. Turner, M., O'Neill, R., Gardner, R., Milne, B., 1989. Effects of changing spatial scale on the analysis of landscape pattern. Landscape Ecol. 33 (4), 153±162. White, D., Kimerling, A., Overton, W., 1992. Cartographic and geometric components of a global sampling design for environmental monitoring. 19 (1) 5±22. Whistler, J., Egbert, S., Jakubauskas, M., Martinko, E., Baumgartner, D., Lee, R., 1995. The Kansas state land cover mapping project: regional scale land use/land cover mapping using Landsat Thematic Mapper data. In: Proceedings of the ACSM/

J.A. Griffith et al. / Landscape and Urban Planning 52 (2000) 45±61 ASPRS `95 Annual Convention and Exposition, Charlotte, North Carolina, 27 February±2 March. Wickham, J., Riitters, K., 1995. The in¯uence of pixel size on Landscape metrics. Int. J. Rem. Sens. 16 (18), 3385±3594. Jerry A. Griffith is a PhD candidate in the Department of Geography at the University of Kansas a graduate research assistant at the Kansas Applied Remote Sensing Program. Jerry has served as lab instructor for introductory/advanced remote sensing and physical geography. With 10‡ years of environmental experience in wetland ecology, GIS/RS, landscape ecology, and ecological monitoring, Jerry has held positions in a private consulting firms, county government, Oak Ridge National Laboratory, and the National Biological Service. His dissertation research focuses on landscape ecology and vegetation condition of the Great Plains and its relation to stream conditions. Jerry's remote sensing research includes cropland classification from multi-temporal TM imagery, and incorporation of NDVI temporal metrics into his landscape ecology studies. Edward A. Martinko is the State Biologist and Director of the Kansas Biological Survey and Director of the Kansas Applied Remote Sensing (KARS) Program. Dr. Martinko also served in Washington DC for 3 years (1992±1994) as the national Director of the US EPA's Environmental Monitoring and Assessment Program (EMAP), a

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multidisciplinary research and development program. Trained as a terrestrial ecologist, Dr. Martinko is a tenured Professor in the Department of Ecology and Evolutionary Biology at the University of Kansas. His research interests include community and ecosystems ecology; basic research in insect community ecology; natural and anthropogenic changes in communities; studies of the spatial and temporal dimensions of change as an intellectual framework for the analysis of applied problems; human impacts on the environment and natural biological diversity as they relate to habitat quality, land use change, and natural resource management; interdisciplinary research; remote sensing and geographic information systems technology as tools for natural resources inventory and analysis. Kevin P. Price is an Associate Professor of Geography and the Associate Director of the KARS Program at the University of Kansas. Dr. Price has 17 years of remote sensing/GIS training and research experience based on disciplinary foundations in geography and rangeland ecology. His research interests are in biogeography, landscape ecology, and agro- and natural ecosystems monitoring and modeling using remotely sensed measurements and geographic information systems (GIS). His past research includes studies in crop monitoring and modeling, plant ecology, biodiversity, grassland productivity, land use discrimination, soil erosion modeling in pinyon-juniper woodlands and agricultural environments, grouse and pheasant habitat modeling, and development of models for predicting patterns of noxious weed invasion. He is currently working on projects in the US, China, Mexico, El Salvador, and Zambia.