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Ecogeomorphic state variables and phase‐space construction for quantifying the evolution of vegetated aeolian landscapes
Author(s) -
Baas Andreas C. W.,
Nield Joanna M.
Publication year - 2010
Publication title -
earth surface processes and landforms
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.294
H-Index - 127
eISSN - 1096-9837
pISSN - 0197-9337
DOI - 10.1002/esp.1990
Subject(s) - aeolian processes , cellular automaton , field (mathematics) , vegetation (pathology) , geology , ecology , computer science , geomorphology , mathematics , algorithm , medicine , pathology , pure mathematics , biology
Cellular automaton modelling for the simulation of dune field formation and evolution has developed progressively in aeolian geomorphology in the last decade or so. A model that incorporates the effects of vegetation and its interactions with geomorphic landscape development – the Discrete Ecogeomorphic Aeolian Landscapes (DECAL) model – can replicate a number of important visual and qualitative aspects of the complex evolution of aeolian dune landscapes under the influence of vegetation dynamics in coastal environments. A key challenge in this research area is the analysis and comparison of both simulated and real‐world vegetated dune landscapes using objective and quantifiable principles. This study presents a methodological framework or protocol for numerically quantifying various ecogeomorphic attributes, using a suite of mathematically defined landscape metrics, to provide a rigorous and statistical evaluation of vegetated dune field evolution. Within this framework the model parameter space can be systematically explored and simulation outcomes can be methodically compared against real‐world landscapes. Based on a simplified scenario of parabolic dunes developing out of blow‐outs the resulting dune field realizations are investigated as a function of variable growth vigour of two simulated vegetation types (pioneer grass and successional woody shrub) by establishing a typological phase‐diagram of different landscape classes. The set of simulation outcomes furthermore defines a higher‐dimensional phase‐space, whose axes or dimensions can be interpreted by analysing how individual ecogeomorphic landscape metrics, or state variables, contribute to the data distribution. Principal component analysis can reduce this to a visual three‐dimensional (3D) phase‐space where landscape evolution can be plotted as time‐trajectories and where we can investigate the effects of changing environmental conditions partway through a simulation scenario. The use of landscape state variables and the construction of a 3D phase‐space presented here may provide a general template for quantifying many other eco‐geomorphic systems on the Earth's surface. Copyright © 2010 John Wiley & Sons, Ltd.