Abstract
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 data sets. We propose an approach for computing slope and curvature 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 our approach, to which we will refer as Iterative Aggregation of Regression Terms (IART), aggregates for fitting a quadratic function are computed iteratively from the DEM data in a process that scales logarithmically with the window size. We show that the IART algorithm produces results of much higher-quality than the two-step process of applying neighborhood operations like focal statistics followed by small-window topographic computations, at comparable computational cost.
Accepted Version
Published Version
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