1. U.S. Department of the Interior U.S. Geological Survey 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) Resolution (m)NAWQAGIS Weasel 3033423.834885.8 6033702.535089.2 12034076.235493.1 21034631.735986.1 24034859.536241.9 48036426.237739.5 96039444.540458.2 192045711.446418.9

13. 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

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

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

16. AGNPS Data Generator

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

18. 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

19. Input Parameter Generation

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

21. Details on Generation of Parameters Slope Shape Factor

22. 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

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

24. 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

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

26. AGNPS Output

28. 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

29. Creating AGNPS Output Images

30. Creating AGNPS Images

31. 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

32. Elevation

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

35. Regression Results n 3 m to 30 m comparison n Elevations -- R 2 of 0.81 n Land cover – McFadden’s pseudo R 2 of 0.139, meaning little correlation n Derived parameters, e.g., slope, problematic because of degraded data source

36. Results Resampling effects

37. 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?

39. 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

40. 30 m DEM120 m DEM120 m slope 60 m slope 30 m DEM30 m slope60 m slope 30 m DEM60 m DEM 30 m DEM30 m slope120 m slope Sample of Slope Generation Approaches compute aggregate compute

41. Results - DEM

43. Image Results -- DEM 30-480 m Pixels210-480 m Pixels

44. 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: Constant0.2762 Std Err of Y Est1.1626 R Squared0.7690 No. of Observations500 Degrees of Freedom498 X Coefficient(s)0.8860 Std Err of Coef.0.0218

45. 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

46. Slope Image Comparison 30 m to 480 m pixels210 m to 480 m pixels

47. Sample of Land Cover Aggregation Approaches 30 m LC210 m LC480 m LC 210m LC 30 m LC60 m LC120 m LC 30 m LC120 m LC 30 m LC960 m LC1920 m LC aggregate

48. Results - Land Cover -- 120 M Pixels

49. Results - Land Cover -- 210 m Pixels

50. Results - Land Cover -- 480 m Pixels

51. Results-Land Cover -- 960 m Pixels

52. Image Results - Land Cover 30-480 m Pixels240-480 m Pixels

53. Image Results - Land Cover 30-210 m Pixels120-210 m Pixels

54. Statistical Testing Selected 500 random points over the watershed Compared elevation, slope, and land cover values at the 500 points Computed R 2 and pseudo R 2 between resolutions Plotted R 2 and pseudo R 2 against resampled resolutions from 30 m data

59. Conclusions Automatic generation of AGNPS parameters from elevation, land cover, and soils Resolution affects results  Elevation and derivatives (slope) hold values well because of averaging methods of resampling  Land cover (categorical data) is inconsistent across resolutions because of nearest neighbor resampling

60. Conclusions Resampling retains values better with even multiples of original pixel sizes Aggregation directly from higher resolution to lower retains values better than multiple intermediate resampling

61. U.S. Department of the Interior U.S. Geological Survey Resolution and Resampling Effects of GIS Databases for Watershed Models E. Lynn Usery U.S. Geological Survey University of Georgia Michael P. Finn U.S. Geological Survey