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Analysis of Resolution and Resampling on GIS Data Values

Analysis of Resolution and Resampling on GIS Data Values. E. Lynn Usery U.S. Geological Survey University of Georgia Michael P. Finn U.S. Geological Survey. The People Who Did the Work. Michael P. Finn, Computer Specialist Douglas Scheidt, Student Programmer

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Analysis of Resolution and Resampling on GIS Data Values

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  1. Analysis of Resolution and Resampling on GIS Data Values E. Lynn Usery U.S. Geological Survey University of Georgia Michael P. Finn U.S. Geological Survey

  2. The People Who Did the Work • Michael P. Finn, Computer Specialist • Douglas Scheidt, Student Programmer • Gregory Jaromack, Student Programmer • Thomas Beard, Cartographic Technician • Sheila Ruhl, Cartographic Technician • Morgan Bearden, Cartographic Technician • John D. Cox, Cartographic Technician

  3. Outline • Introduction and Objectives • Study Areas • GIS Databases for Parameter Extraction • AGNPS Parameter Generation • Resolution Effects • Resampling Effects • Conclusions

  4. Objectives • Develop GIS databases as input to Agricultural Non-Point Source (AGNPS) Pollution Model • Create a tool for generating input, executing the model, and analyzing output • Determine effects of resolution and resampling

  5. Introduction -- AGNPS • Operates on a cell basis and is a distributed parameter, event-based model • Requires 22 input parameters • Elevation, land cover, and soils data are the base for extraction of input parameters

  6. Study Areas • Four Watersheds • Little River, GA • Piscola Creek, GA • Sugar Creek, IN • EL68D Wasteway, WA

  7. Georgia Watersheds Agricultural areas with some woodland, wetlands, and small urban areas

  8. Indiana Watershed Agricultural area with primarily corn and soybean crops

  9. Washington Watershed Agricultural watershed with a variety of row crops and small grains

  10. Watershed Boundaries • NAWQA Boundary • Defined by USGS WRD personnel from contour maps • GIS Weasel • Automatically computed from DEM data

  11. Comparison of Watershed Areas (hectares)

  12. GIS Databases for Parameter Extraction • National Elevation Dataset (30-m) • National Land Characteristics Data (30 m) • Augmented with recent Landsat TM data • Soils databases from USDA soil surveys • Scanned separates, rectified, vectorized, tagged • Resampled the 30-m data to 60, 120, 210, 240, 480, 960, and 1920 meters • 210-m roughly matches 10 acre grid size

  13. AGNPS Parameter Generation • AGNPS Data Generator • Input parameter generation • Details on generation of parameters • Extraction methods

  14. AGNPS Data Generator • Created to provide interface between GIS software (Imagine) and AGNPS • Developed interface for Imagine 8.4, running on WinNT/2000

  15. AGNPS Data Generator

  16. Input Parameter Generation • 22 parameters; varying degrees of computational development • Simple, straightforward, complex

  17. Creating AGNPS Input • Input Data File Creation • Format generated parameters into AGNPS input file • Use a “stacked” image file to create AGNPS data file (“.dat”) -- ASCII

  18. Input Parameter Generation

  19. Details on Generation of Parameters • Cell Number • Receiving Cell Number • SCS Curve Number • Uses both soil and land cover to resolve curve number

  20. Details on Generation of Parameters • Slope Shape Factor

  21. Details on Generation of Parameters • Slope Length • A concern; max value should be 300 ft. • Parameters 10, 11, 12, 14, 15, 16, and 17 • Uses Spatial Modeler to lookup attributes from soils or land cover • Parameters 13, 18, 19, 20, and 21 • Hard coded on advice from experts

  22. Details on Generation of Parameters • Type of Channel • Uses TARDEM program • Creates a Strahler steam order

  23. Extraction Methods • Used object-oriented programming and macro languages • C/ C++ and EML • Manipulated the raster GIS databases with Imagine • Extracted parameters for each resolution for both boundaries using AGNPS Data Generator

  24. Creating AGNPS Output • AGNPS creates a nonpoint source (“.nps”) file • ASCII file like the input; tabular, numerical form

  25. AGNPS Output

  26. AGNPS Output

  27. Creating AGNPS Output Images • Output Image Creation • Combined “.nps” file with Parameter 1 to create multidimensional images • Users can graphically display AGNPS output • Process: create image with “x” layers, fill layers with AGNPS output data, set projection and stats for image • Multi-layered (bands) images per model event

  28. Creating AGNPS Output Images

  29. Creating AGNPS Images

  30. Results • Resolution effects • Tested with two independent collections • Elevation at 3 m and 30 m resolution • Land cover at 3 m and 30 m resolution • Comparison of values

  31. Elevation

  32. Sampling of Points for Land Cover and Elevation Comparisons for Little River, GA

  33. Regression Results • 3 m to 30 m comparison • Elevations -- R2 of 0.81 • Land cover – McFadden’s pseudo R2 of 0.139, meaning little correlation • Derived parameters, e.g., slope, problematic because of degraded data source

  34. Results • Resampling effects

  35. Experimental Approach • Analysis requires DEM, slope, and land cover at 30, 60, 120, 210, 240, 480, 960, 1920 m cells • Starting point is 30 m DEM and land cover • Calculate slope at 30 m cell size from DEM • Resample land cover • How to generate slope at 60 m and larger cell sizes? How to aggregate land cover?

  36. Method of Calculation • Slope calculated from DEM • 30, 60, 120, 210, 240, 480, 960, 1920 m cells • Compute slope from 30 DEM • Aggregate DEM from 30 m to each lower resolution • Compute slope from aggregated elevation data

  37. Sample of Slope Generation Approaches compute aggregate 30 m DEM 30 m slope 60 m slope aggregate compute 60 m slope 30 m DEM 60 m DEM aggregate compute 30 m DEM 120 m DEM 120 m slope 30 m DEM compute 30 m slope aggregate 120 m slope

  38. Results - DEM

  39. Results - DEM

  40. Image Results -- DEM 30-480 m Pixels 210-480 m Pixels

  41. Results -- Slope Slope % 30 to 480m Pixels 7.8816 7.8232 7.5870 7.8251 8.1604 8.5415 8.2065 7.9530 7.7434 7.7092 Slope % 210 to 480m Pixels 7.9514 7.8969 7.6244 7.7855 8.1263 8.5087 8.2157 7.8606 7.6390 7.6081 Regression Output: Constant 0.2762 Std Err of Y Est 1.1626 R Squared 0.7690 No. of Observations 500 Degrees of Freedom 498 X Coefficient(s) 0.8860 Std Err of Coef. 0.0218

  42. Results -- Slope • Slope • Method of calculation affects results • Higher resolution aggregation directly to large pixel sizes yields better results than multistage aggregation (e.g., 30 m to 960 m is better than 30 m to 60 m to 120 m to 240 m to 480 m to 960 m) • Even multiples of pixels hold results while odd pixel sizes introduce error

  43. Slope Image Comparison 30 m to 480 m pixels 210 m to 480 m pixels

  44. Sample of Land Cover Aggregation Approaches aggregate aggregate 30 m LC 60 m LC 120 m LC aggregate aggregate 210m LC 30 m LC 120 m LC aggregate aggregate 30 m LC 210 m LC 480 m LC aggregate aggregate 30 m LC 960 m LC 1920 m LC

  45. Results - Land Cover -- 120 M Pixels

  46. Results - Land Cover -- 210 m Pixels

  47. Results - Land Cover -- 480 m Pixels

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