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Quantifying Landscape Pattern

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Title: Quantifying Landscape Pattern


1
Quantifying Landscape Pattern
  • ESM 215
  • Feb 3, 2003

2
Changes in forest cover, Cadiz Township,
Wisconsin (Curtis 1956)
3
Landscapes over time
1972 1984 1991
1972
1984
1991
4
Analysis of Model Outputs
1 ha clearings
30 ha clearings
5
Western boundary, Yellowstone National Park
(Turner et al. Fig 5-2)
6
Why quantify pattern?
  • Investigate pattern ltgt process
  • Landscape monitoring
  • Comparison across landscapes
  • Compare and contrast management strategies
  • Sampling and experimental design

7
Classification and Pattern
Turner et al. 5-5
8
Gustafson (1998)
9
Metrics of landscape composition categorical maps
  • Number of classes (richness, S)
  • Proportion of area occupied by class i (pi)
  • Diversity (evenness)
  • SHEI (-?pi ln (pi) ) / ln (S)
  • Dominance
  • D (ln(S) ?pi ln(pi)) / ln(S)

10
Measures of spatial configuration
  • Adjacency (cell-based)
  • Contagion and Interspersion (cell-based)
  • Perimeter-area (patch)
  • Connectivity (patch)
  • Proximity (patch)
  • Patch size distribution (e.g., area-weighted
    patch size)

11
Contagion
  • C 1 ??? Pij ln (Pij)
  • 2 ln (S)
  • P probability that 2 randomly chosen adjacent
    cells are the same cover type
  • S of cover types

12
Connectivity/Fragmentation
  • Patch cohesion (Schumaker 96)
  • A four-part measure by Bogaert et al. 2000.
    Wildlife Society Bulletin 28875-881.
  • habitat loss, isolation, increased perimeter
    length,increase in patch number

13
Fractals
  • Fractals objects or patterns that have
    non-integer dimensions
  • self-similarity pattern at coarse scales is
    repeated at finer and finer scales
  • scale-dependence

14
Fractal curves
N steps r scale ratio D log N/log r
D ln 4/ln 3 1.2618
D ln 5/ln 3 1.4650
15
Fractal Patches
A (kP)d d ln(A) / (ln(P) ln(k)) kP AD D
1/d
A area P perimeter D fractal dimension K
constant
16
Fractal dimensions of (D) of forest patches near
Natchez Mississippi as a function of patch
size. ( Turner fig 5.11)
17
Box analysis of fractal dimension of lattices
18
Procedure for box analysis
  • map is superimposed with a grid gt the grain
    size. Boxes which contain class of interest (in
    any small amount) are counted.
  • The process is repeated with different box sizes
    until 1-2 orders of magnitude in box size has
    been explored (say boxes of size 1 to 100).
  • the logarithm of the number of occupied boxes of
    each length is regressed against the logarithm of
    box length.
  • The slope of the regression is the exponent in
    the power law
  • N(L) kL-Db

19
1000
2,262
x
boxes
100
x
10
x
x
1
x 32,1
100
1
10
Box size
The slope of the line is the fractal dimension of
the lattice as estimated by the box counting
method
20
Converting box counts to areas
  • A(L) kL2-Db
  • assuming self-similarity at scales below the
    grain size, the scaling relation for area could
    be used to estimate the area of a feature at
    sub-grain scales
  • k lacunarity used to estimate N(L) for box
    size L

21
Multiple metrics
22
Which metrics?
  • types, contagion, fractal dimension, mean patch
    perimeter-area ratio, relative patch area
    (Riiters et al. 1995)
  • Patch shape and edge contrast, patch density,
    patch size (McGarigal and Marks (1995)
  • types, proportion of each type, spatial
    arrangement of patches, patch shape, contrast
    between neighboring patches (Li and Reynolds 1994)

23
Pattern in continuous variables and point
processes
  • Trend surface analysis
  • Spatial autocorrelation
  • Semivariance
  • 1-d and 2-d spectral analysis
  • wavelets

24
Morans I, Gearys CTwo general measures of
spatial autocorrelation
  • wij - weight at distance d, that is, wij1 if
    point j is within distance class d from point i,
    else wij0
  • z's are deviations (i.e., ziyi-ymean for
    variable y),
  • W is the sum of all the weights.
  • The summation is done for all i not equal to j

25
Random landscape
Continuous gradient
Repeating pattern
26
Semivariograms
  • N point (location) pairs
  • f1i value of variable at location 1
  • f2i value of variable at location 2

27
Gustafson (1998)
28
Variograms and correlograms
29
Example application of landscape pattern metrics
  • G. Darrel Jenerette 2001. Analysis and simulation
    of land-use change in the central Arizona
    Phoenix region, USA. Landscape Ecology 16(7)
    611-626.

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