Next Article in Journal
Public Key Encryption with Keyword Search in Cloud: A Survey
Previous Article in Journal
Image Representation Method Based on Relative Layer Entropy for Insulator Recognition
Previous Article in Special Issue
Use of Entropy in Developing SDG-based Indices for Assessing Regional Sustainable Development: A Provincial Case Study of China
Open AccessArticle

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

Department of Geography, York University, 4700 Keele Street, Toronto, ON M3J 1P3, Canada
Entropy 2020, 22(4), 420; https://doi.org/10.3390/e22040420
Received: 11 March 2020 / Revised: 2 April 2020 / Accepted: 7 April 2020 / Published: 8 April 2020
(This article belongs to the Special Issue Entropy in Landscape Ecology II )
Even with considerable attention in recent decades, measuring and working with patterns remains a complex task due to the underlying dynamic processes that form these patterns, the influence of scales, and the many further implications stemming from their representation. This work scrutinizes binary classes mapped onto regular grids and counts the relative frequencies of all first-order configuration components and then converts these measurements into empirical probabilities of occurrence for either of the two landscape classes. The approach takes into consideration configuration explicitly and composition implicitly (in a common framework), while the construction of a frequency distribution provides a generic model of landscape structure that can be used to simulate structurally similar landscapes or to compare divergence from other landscapes. The technique is first tested on simulated data to characterize a continuum of landscapes across a range of spatial autocorrelations and relative compositions. Subsequent assessments of boundary prominence are explored, where outcomes are known a priori, to demonstrate the utility of this novel method. For a binary map on a regular grid, there are 32 possible configurations of first-order orthogonal neighbours. The goal is to develop a workflow that permits patterns to be characterized in this way and to offer an approach that identifies how relatively divergent observed patterns are, using the well-known Kullback–Leibler divergence. View Full-Text
Keywords: binary; configuration; composition; frequency distribution; variability; spatial pattern; divergence binary; configuration; composition; frequency distribution; variability; spatial pattern; divergence
Show Figures

Figure 1

MDPI and ACS Style

Remmel, T.K. Distributions of Hyper-Local Configuration Elements to Characterize, Compare, and Assess Landscape-Level Spatial Patterns. Entropy 2020, 22, 420.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop