Spatial information surveyed by photogrammetry, airborne LiDAR and Mobile Measurement System (MMS) above ground level can be analyzed by scientists using standard geostatistical methodologies such as ordinary Kriging and sequential Gaussian simulation to interpolate heterogeneities of profiles from sparse sample data. Proven effective by researchers, the Kriging algorithm model is used by commercial data analysis packages for instant interpolation. However, meaningful and reliable results only come with a comprehensive understanding of the variogram associated with valid mathematical functions. To capture spatial landscape variations from massive sample grids of satellite images, this paper presents a cloud computing-based automation approach to improve topography interpolation by taking advantage of rapid computation speed through an open-source cross platform to enrich internet applications. The research team conducted a pilot test on sand beaches, developed the Kriging Cloud Computing Framework, streamlined the Kriging algorithm, developed Kriging Variogram Data Bank and Parameter Management System, derived cross validation procedures and built in Application Programming Interface, API. This new technology can benefit end users around the world in acquiring of ground profiles and production of Digital Elevation Models (DEMs) while requiring only minimal knowledge of the Kriging Method. This cloud computing system facilitates user data input, parameter selection, fast data analysis and model output. The application of this new framework improves remote sensing technology and GIS applications in a variety of unreachable terrains, such as deserts, swamps, and dense forests.

Included in

Engineering Commons