Behavioral characteristics of landscape structure metrics in neutral landscapes
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BEHAVIORAL CHARACTERISTICS OF LANDSCAPE STRUCTURE METRICS IN NEUTRAL LANDSCAPES. FRAGSTATS Workshop 18, July 2003 IALE World Congress Darwin, Australia. Increasing area (P). 95%. Step 1: Generate binary neutral landscapes using the computer program RULE ( Gardner 1999 ).

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Behavioral characteristics of landscape structure metrics in neutral landscapes l.jpg
BEHAVIORAL CHARACTERISTICS OF LANDSCAPE STRUCTURE METRICS IN NEUTRAL LANDSCAPES

FRAGSTATS Workshop18, July 2003IALE World Congress Darwin, Australia


Slide2 l.jpg

Increasing IN NEUTRAL LANDSCAPES

area (P)

95%

Step 1: Generate binary neutral landscapes using the computer program RULE (Gardner 1999).

Increasing aggregation (H)

0

1

5%

256 x 256 cell grids

Factorial Design

H (n = 21) x P (n = 19)

100 replicates of each of 399 H x P combinations


Fragstats specifications l.jpg

Step 2: Calculate 55 applicable class-level metrics on all 39,900 neutral landscapes using FRAGSTATS.

FRAGSTATS Specifications:

  • 30 m cell size

  • 90 m edge depth

  • 500 m search radius

  • 8 cell neighbor rule

  • No border

  • No background

  • Boundary not included as edge


Conceptual metric classification l.jpg
Conceptual Metric Classification 39,900 neutral landscapes using FRAGSTATS.

Isolation/

Proximity

Proximity Index*

Similarity Index*

Euclidean Nearest Neighbor*

Shape

Perimeter Area Fractal

Perimeter Area Ratio*

Shape Index*

Fractal Dimension Index*

Core Area

Total Core Area

Core Percent of Landscape

# Disjunct Core Areas

Disjunct Core Area Density

Core Area

Disjunct Core Area*

Core Area Index*

Area/Edge/

Density

Class Area

Percent of Landscape

Patch Density

Edge Density

Landscape Shape Index

Largest Patch Index

Normalized Shape Index

Patch Area*

Radius of Gyration*

Contagion/

Interspersion

Percent Like Adjacencies

Clumpiness Index

Aggregation Index

Intersperson and Juxtapostion Index

Landscape Division

Splitting Index

Effective Mesh Size

Contrast

Contrast Weighted Edge Density

Total Edge Contrast Index

Edge Contrast*

Connectivity

Patch Cohesion Index


Conceptual metric classification5 l.jpg
Conceptual Metric Classification 39,900 neutral landscapes using FRAGSTATS.

Isolation/

Proximity

PROX*

SIMI*

ENN*

Shape

PAFRAC

PARA*

SHAPE*

FRAC*

Core Area

TCA

CPLAND

NDCA

DCAD

CORE*

DCORE*

CAI*

Area/Edge/

Density

CA

PLAND

PD

ED

LSI

LPI

nLSI

AREA*

GYRATE*

Contagion/

Interspersion

PLADJ

CLUMPY

AI

IJI

DIVISION

SPLIT

MESH

Contrast

CWED

TECI

ECON*

Connectivity

COHESION


Metric behavior l.jpg
Metric Behavior 39,900 neutral landscapes using FRAGSTATS.

H

P


Slide7 l.jpg

Step 4: Plot the range of the H x P space that real landscapes occupy

  • Calculate metrics in landscapes from three geographically distinct regions in the United States:

    • Idaho (221 landscapes, 5 classes)

    • Western Massachusetts (155 landscapes, 7 classes)

    • Colorado (152 landscapes, 4 classes)

  • Superimpose values from real landscapes onto values from neutral landscapes.


Slide9 l.jpg

Metric Behavior landscapes occupy

H

P


Slide12 l.jpg

Step 5: Evaluate patterns of class-level metric behavior in using mean metric values for 48 metrics.

  • Use cluster analysis to classify metrics based on behavior along P and H gradients.

  • Graphically compare behavior of metrics.


Primarily a function of p l.jpg
Primarily a Function of P using mean metric values for 48 metrics.

LPI

AREA_AM

AREA_SD

GYRATE_AM

GYRATE_SD

CORE_AM

CORE_SD

TCA

DCORE_AM

DCORE_SD

PROX_MN

PROX_CV

PROX_SD

DIVISION

MESH

H

P


Primarily a function of h strongly related to h l.jpg
Primarily a Function of H: using mean metric values for 48 metrics.Strongly Related to H

PAFRAC

nLSI

PARA_SD

FRAC_CV

FRAC_SD

CAI_SD

CLUMPY


Slide15 l.jpg

CLUMPY using mean metric values for 48 metrics.


Related to interaction of p and h parabolic response along p l.jpg
Related to Interaction of P and H using mean metric values for 48 metrics.Parabolic Response Along P

LSI

PD

GYRATE_CV

FRAC_AM

SHAPE_AM

SHAPE_CV

SHAPE_SD

PROX_AM

DCORE_CV

DCAD

ED


Slide17 l.jpg

FRAC_AM using mean metric values for 48 metrics.

DCAD

SHAPE_SD


Related to interaction of p and h l.jpg
Related to Interaction of P and H using mean metric values for 48 metrics.

GYRATE_MN

PARA_AM

CORE_MN

DCORE_MN

CAI_AM

CAI_MN

SPLIT

PLADJ

AI

COHESION

ENN_AM

ENN_MN

ENN_CV

ENN_SD

AREA_MN


Slide19 l.jpg

ENN_AM using mean metric values for 48 metrics.

GYRATE_MN

PARA_AM

COHESION


Differential metric sensitivity l.jpg

P = 5% using mean metric values for 48 metrics.

P = 50%

P = 95%

H = 0

P = 5%

P = 50%

P = 95%

H = 1

Differential Metric Sensitivity

AREA_MN

AREA_AM


Main points l.jpg
Main Points using mean metric values for 48 metrics.

**Limitations**

  • Results are based on

    • binary neutral landscapes.

    • at one scale.

    • only one configuration gradient (H). Varying shape, inter-patch distance, etc. would yield different behavior.

  • Identified 7 behavioral groups with varying relationships with P and H.

  • Conceptual similarity ≠ behavioral similarity.

  • Many metrics have non-linear behavior and lack of sensitivity in at least part of the H x P space.

    • Problematic conditions do not always exist in real landscapes.

  • Very few metrics measure configuration independent of area – most confound P & H.


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