Computation of the Boltzmann entropy of a landscape pattern: the state of the art

Authors: Peichao Gao*, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University
Topics: Landscape, Land Use and Land Cover Change, Geographic Information Science and Systems
Keywords: Boltzmann entropy, Shannon entropy, Information entropy, Landscape patterns
Session Type: Paper
Day: 4/13/2018
Start / End Time: 5:20 PM / 7:00 PM
Room: Galvez, , Marriott, 5th Floor
Presentation File: No File Uploaded


Entropy is the core of the Second Law of Thermodynamics, which plays a fundamental role in understanding nature and is central to studying landscape ecology. For a long time, the entropy used in landscape ecology has been computed using the equation by Shannon (1948), an early pioneer of information theory. And a number of landscape ecology studies have been conducted accordingly in order to interpret landscape/geographic dynamics based on thermodynamic insights. However, the thermodynamic basis of Shannon’s entropy has been critically questioned (Vranken et al. 2015), as well as the thermodynamic interpretations achieved by using Shannon’s entropy. This finding was described as “astounding for a field that has been so obsessed with measuring and interpreting landscape patterns” (Cushman 2015). As a result, scholars (Cushman 2015, Vranken et al. 2015, Cushman 2016) in landscape ecology suggested to revisit Boltzmann’s entropy and using Boltzmann’s entropy as an alternative to Shannon entropy, but it remains largely at a conceptual level in physics and becomes “quite problematic when the notion of entropy is extended beyond physics” (Bailey 2009). Fortunately, there have been recently some progress in computing the Boltzmann entropy of a landscape pattern represented by using either a gradient model (i.e., a landscape gradient) or a mosaic model (i.e., a landscape mosaic). In particular, a feasible computation method for the Boltzmann entropy of a landscape gradient has been successfully proposed by the presenter and his collaborators (Gao et al. 2017). All of the latest progress will be introduced and discussed in this presentation.

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