Large-Window Curvature Computations for High-Resolution Digital Elevation Models

With the increasing availability of high-resolution digital elevation model (DEM) data, a need has emerged for new processing techniques. Topographic variables, such as slope and curvature, are relevant on length scales far larger than the pixel resolution of modern DEM datasets. An approach for computing slope and curvature is proposed that uses standard regression coefficients over large windows while generating output on the full resolution of the original data, without adding substantially to the computation time. In the proposed window-aggregation approach, aggregates for fitting a quadratic function are computed iteratively from the DEM data in a process that scales logarithmically with the window size. It is shown that the window-aggregation algorithm produces the results of much higher quality than the two-step process of applying neighborhood operations such as focal statistics followed by small-window topographic computations, at comparable computational cost.