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Distributions of Hyper-Local Configuration Elements to Characterize, Compare, and Assess Landscape-Level Spatial

Tarmo K Remmel1

  • 1Department of Geography, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

This study presents a new method to measure landscape patterns by analyzing configuration and composition. It uses probabilities and Kullback-Leibler divergence to quantify pattern differences, aiding in landscape simulation and comparison.

Keywords:
binarycompositionconfigurationdivergencefrequency distributionspatial patternvariability

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Area of Science:

  • Geographic Information Science
  • Spatial Analysis
  • Landscape Ecology

Background:

  • Measuring and analyzing spatial patterns is complex due to dynamic processes, scale influences, and representation challenges.
  • Existing methods often struggle to explicitly account for pattern configuration and composition simultaneously.

Purpose of the Study:

  • To develop a novel workflow for characterizing binary landscape patterns on regular grids.
  • To quantify pattern divergence using empirical probabilities and Kullback-Leibler divergence.

Main Methods:

  • Scrutinizing binary classes on regular grids by counting relative frequencies of first-order configuration components.
  • Converting frequency counts into empirical probabilities of occurrence for landscape classes.
  • Utilizing a frequency distribution to create a generic model for landscape structure analysis.

Main Results:

  • The method successfully characterizes a continuum of landscapes with varying spatial autocorrelation and composition.
  • It demonstrates utility in assessing boundary prominence where outcomes are known.
  • A framework is established to quantify pattern divergence using 32 possible first-order neighbor configurations.

Conclusions:

  • This approach provides a robust method for explicit pattern configuration analysis in landscape studies.
  • It enables the simulation of structurally similar landscapes and comparison of pattern divergence.
  • The workflow offers a novel way to assess landscape pattern characteristics and their deviations.