Characterizing instability of aeolian environments using analytical reasoning

Abstract

This short communication describes the development and application of analytical reasoning to quantify instability of an aeolian environment using scale-dependent information coupled with conceptual knowledge of process and feedback mechanisms. Specifically, a simple Fuzzy Cognitive Map (FCM) for aeolian landscape instability was developed that represents conceptual knowledge of key biophysical processes and feedbacks. Model inputs include satellite-derived surface biophysical and geomorphometric parameters. FCMs are a knowledge-based artificial intelligence (AI) technique that merges fuzzy logic and neural computing in which knowledge or concepts are structured as a web of relationships that is similar to both human reasoning and the human decision-making process. Given simple process-form relationships, the analytical reasoning model is able to map the influence of land management practices and the geomorphology of the inherited surface on aeolian instability within the South Texas Sand Sheet. Results suggest that FCMs can be used to formalize process-form relationships and information integration analogous to human cognition with future iterations accounting for the spatial interactions and temporal lags across the sand sheets.