Several landforms are known to exhibit topographic anisotropy, defined as a directional inequality in elevation. The quantitative analysis of topographic anisotropy has largely focused on measurements taken from specific landforms, ignoring the surrounding landscape. Recent research has made progress in measuring topographic anisotropy as a distributed field in natural landscapes. However, current methods are computationally inefficient, as they require specialized hardware and computing environments, or have a limited selection of scales that undermines the feasibility and quality of multiscale analyses by introducing bias. By necessity, current methods operate with a limited set of scales, rather than the full distribution of possible landscapes. Therefore, we present a method for measuring topographic anisotropy in the landscape that has the computational efficiency required for hyperscale analysis by using the integral image filtering approach to compute oriented local topographic position (LTP) measurements, coupled with a root-mean-square deviation (RMSD) model that compares directional samples to an omnidirectional sample. Two tools were developed: One to output a scale signature for a single cell, and the other to output a raster containing the maximum anisotropy value across a range of scales. The performances of both algorithms were tested using two data sets containing repetitive, similarly sized and oriented anisotropic landforms, including a dune field and a drumlin field. The results demonstrated that the method presented has the robustness and sensitivity to identify complex hyperscale anisotropy such as nested features (e.g., a drumlin located within a valley).
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited