Subsampling large-scale digital elevation models to expedite geospatial analyses in coastal regions

Kelly Ann Murphy; David G. Zawada; Kimberly K. Yates

doi:10.2112/jcoastres-d-22-00015.1

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Subsampling large-scale digital elevation models to expedite geospatial analyses in coastal regions

Kelly Ann Murphy·David G. Zawada·Kimberly K. Yates

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Abstract

Large-area, high-resolution digital elevation models (DEMs) created from light detection and ranging (LIDAR) and/or multibeam echosounder data sets are commonly used in many scientific disciplines. These DEMs can span thousands of square kilometers, typically with a spatial resolution of 1 m or finer, and can be difficult to process and analyze without specialized computers and software. Such DEMs often can be subsampled to expedite analysis with negligible impact on results for large-scale geospatial analyses. Subsampling can be achieved by creating a grid of points that specify the locations from which to extract elevation values from the DEM. This paper presents a method that can be used to accurately perform subsampling of large-scale, high-resolution DEMs using GIS software. This subsampling method was applied to two LIDAR-derived DEMs encompassing 242 km 2 of the northern Florida Reef Tract as an example application and to test subsampling accuracy. Results indicate that subsampling 1-m-resolution DEMs using a 2-m-spaced grid results in no significant difference in mean elevation or other basic statistics for analyses performed over multiple spatial scales ranging from 1 km 2 to 242 km 2 .

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Kelly Ann Murphy, David G. Zawada, Kimberly K. Yates. 2022. Subsampling large-scale digital elevation models to expedite geospatial analyses in coastal regions. https://doi.org/10.2112/jcoastres-d-22-00015.1

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