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Landscape Pattern Metrics

Also known as: landscape pattern indices, FRAGSTATS metrics, fragmentation indices, peyzaj metrikleri

OriginatorR. V. O'Neill et al.; McGarigal & Marks (FRAGSTATS)Year1988Sources2Related methods5

Landscape metrics are quantitative indices that describe the composition and spatial configuration of a categorical map — typically land cover — at the patch, class, and whole-landscape levels. Developed in landscape ecology (O'Neill and colleagues, 1988) and made widely usable by the FRAGSTATS software, they turn maps into numbers like patch density, edge density, fragmentation, diversity, and connectivity for ecological, planning, and change analysis.

Key highlights

  • Turn categorical maps into comparable numbers for pattern and fragmentation.
  • Capture both composition (amount/diversity) and configuration (arrangement).
  • Multi-level (patch/class/landscape) and widely standardized via FRAGSTATS.
  • Enable change detection and links between spatial pattern and ecological process.

Intuition

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How it works

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When to use it

Use landscape metrics to quantify and compare spatial pattern in categorical maps — measuring habitat fragmentation and connectivity, tracking land-cover change over time, relating pattern to ecological responses (biodiversity, species movement), and informing conservation and land-use planning. They are the standard descriptive language of landscape ecology and pair naturally with land-change models (CA-Markov) and image classification (OBIA). Key cautions: many metrics are strongly correlated (so a small, non-redundant set should be chosen), highly sensitive to spatial resolution and to the classification scheme, and descriptive rather than inferential — they summarize pattern but do not by themselves establish ecological cause. Comparisons are only meaningful at a common grain, extent, and class definition.

Strengths & limitations

Strengths
  • Turn categorical maps into comparable numbers for pattern and fragmentation.
  • Capture both composition (amount/diversity) and configuration (arrangement).
  • Multi-level (patch/class/landscape) and widely standardized via FRAGSTATS.
  • Enable change detection and links between spatial pattern and ecological process.
Limitations
  • Highly sensitive to spatial resolution (grain) and classification scheme.
  • Many metrics are strongly intercorrelated and redundant.
  • Descriptive, not inferential — they do not establish causation.
  • Comparisons require a common grain, extent, and class definition.

Common pitfalls

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Applications

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Frequently asked

What is the difference between composition and configuration metrics?

Composition metrics describe how much of each class is present and how diverse the landscape is, ignoring arrangement (e.g., class proportions, Shannon diversity). Configuration metrics describe the spatial arrangement — patch size, shape, edge, contagion, connectivity. Both are needed because landscapes with identical composition can have very different configurations.

Why are landscape metrics scale-sensitive?

Most metrics change with grain (cell size) and extent: coarser resolution merges patches and smooths edges, altering counts, densities, and shape indices. Therefore metrics are only comparable across maps at the same resolution, extent, and classification scheme.

How do I avoid redundant metrics?

Many indices are strongly correlated, so reporting dozens is misleading. Select a small set capturing distinct components of pattern (e.g., one each for amount, subdivision, shape, and connectivity), often guided by correlation or factor analysis on the metric set for your data.

Sources

  1. 1.
    O'Neill, R. V., et al. (1988). Indices of landscape pattern. Landscape Ecology, 1(3), 153–162.
    DOI 10.1007/BF00162741
  2. 2.
    McGarigal, K., & Marks, B. J. (1995). FRAGSTATS: spatial pattern analysis program for quantifying landscape structure. USDA Forest Service General Technical Report PNW-GTR-351.
    https://www.fs.usda.gov/research/treesearch/3064

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ScholarGate. (2026, June 2). Landscape Metrics. ScholarGate. https://scholargate.app/spatial-analysis/landscape-metrics