Revisiting Topographic Horizons in the Era of Big Data and Parallel Computing

Widely used to calculate illumination geometry for estimates of solar and emitted longwave radiation, and for correcting remotely sensed data for topographic effects, digital elevation models (DEMs) are now extensive globally at 10-30-m spatial resolution and locally at spatial resolutions down to a...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2022-01, Vol.19, p.1-5
Main Author: Dozier, Jeff
Format: Article
Language:English
Subjects:
Angles (geometry)
Azimuth
Big Data
Big data applications
Codes
Computational modeling
Computers
Computing time
Digital Elevation Models
digital elevation models (DEMs)
Elevation
Geosciences (General)
Long wave radiation
Matlab
Microprocessors
Mountains
Multiprocessing
Parallel processing
Program processors
Radiation
Remote sensing
Shading
Spatial discrimination
Spatial resolution
surface topography
Terrain
Topographic effects
Topography
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Summary:Widely used to calculate illumination geometry for estimates of solar and emitted longwave radiation, and for correcting remotely sensed data for topographic effects, digital elevation models (DEMs) are now extensive globally at 10-30-m spatial resolution and locally at spatial resolutions down to a few centimeters. Globally, regionally, or locally, elevation datasets have many grid points. Many software packages calculate gradients over every grid cell or point, but in the mountains, shading by nearby terrain must also be assessed. Terrain may obscure a slope that would otherwise face the Sun. Four decades ago, a fast method to calculate topographic horizons at every point in an elevation grid required computations related only linearly to the size of the grid, but grids now have so many points that parallel computing still provides an advantage. Exploiting parallelism over terrain grids can use alternative strategies: among columns of a rotated grid, or simultaneously at multiple rotation angles, or on different tiles of a grid. On a multi-processor machine, the improvement in computing time approaches 2/3 the number of processors deployed.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2021.3125278