Spatial Analysis and Modeling in Geomorphology

Abstract

Rapid advancements in remote sensing and geospatial information technologies provide Earth scientists new opportunities to utilize high-resolution geospatial datasets for investigating and mapping landforms and geomorphological systems. Advances in spatial analysis and modeling algorithms, approaches and parameterization schemes also enable scientists to better characterize landscape biophysical and structural conditions, morphometric properties of the topography, process mechanics and system dynamics. This article describes the fundamentals of spatial analysis and modeling techniques and approaches, in the context of current capabilities and research opportunities. Issues associated with analysis and modeling efforts are also highlighted. Challenges include concept representation and formalization, empiricism, spatial information synthesis, data volume and computational issues. Nevertheless, rigorous spatial analysis and modeling efforts that quantitatively formalize spatial, temporal and geomorphic concepts can significantly improve our understanding of the characteristics of geomorphological systems and the complex nature of topographic evolution.