Analysis of Resolution and Resampling on gis data Values E. Lynn Usery

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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



  • 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

Indiana Watershed

Washington Watershed

Watershed Boundaries

  • NAWQA Boundary

    • Defined by USGS WRD personnel from contour maps
  • GIS Weasel

    • Automatically computed from DEM data

Comparison of Watershed Areas (hectares)

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

AGNPS Output

  • 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


  • 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

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


  • 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

Sample of Slope Generation Approaches

Results - DEM

Results - DEM

Image Results -- DEM

Results -- Slope

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

Sample of Land Cover Aggregation Approaches

Results - Land Cover -- 120 M Pixels

Results - Land Cover -- 210 m Pixels

Results - Land Cover -- 480 m Pixels

Results-Land Cover -- 960 m Pixels

Image Results - Land Cover

Image Results - Land Cover

Statistical Testing

  • Selected 500 random points over the watershed

  • Compared elevation, slope, and land cover values at the 500 points

  • Computed R2 and pseudo R2 between resolutions

  • Plotted R2 and pseudo R2 against resampled resolutions from 30 m data


  • 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


  • 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

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

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