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

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

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

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

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

Study Areas Four Watersheds  Little River, GA  Piscola Creek, GA  Sugar Creek, IN  EL68D Wasteway, WA

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

Indiana Watershed Agricultural area with primarily corn and soybean crops

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

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

Comparison of Watershed Areas (hectares) Resolution (m)NAWQAGIS Weasel

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

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

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

AGNPS Data Generator

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

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

Input Parameter Generation

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

Details on Generation of Parameters Slope Shape Factor

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

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

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

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

AGNPS Output

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

Creating AGNPS Output Images

Creating AGNPS Images

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

Elevation

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

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

Results Resampling effects

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?

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

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

Results - DEM

Image Results -- DEM m Pixels m Pixels

Results -- Slope Slope % 30 to 480m Pixels Slope % 210 to 480m Pixels Regression Output: Constant Std Err of Y Est R Squared No. of Observations500 Degrees of Freedom498 X Coefficient(s) Std Err of Coef

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

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

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

Results - Land Cover M Pixels

Results - Land Cover m Pixels

Results - Land Cover m Pixels

Results-Land Cover m Pixels

Image Results - Land Cover m Pixels m Pixels

Image Results - Land Cover m Pixels m Pixels

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

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

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

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