# Presentation: 676156

## 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) | NAWQA | GIS Weasel |
| --- | --- | --- |
|  |  |  |
| 30 | 33423.8 | 34885.8 |
| 60 | 33702.5  | 35089.2 |
| 120 | 34076.2 | 35493.1 |
| 210 | 34631.7 | 35986.1 |
| 240 | 34859.5 | 36241.9 |
| 480 | 36426.2 | 37739.5 |
| 960 | 39444.5 | 40458.2 |
| 1920 | 45711.4 | 46418.9 |

## Slide 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**

## 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

## 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

## Slide 33

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

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

## Regression Results

**3 m to 30 m comparison**
**Elevations -- R****2**** of 0.81 **
**Land cover – McFadden’s pseudo R****2**** of 0.139, meaning little correlation**
**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?**

## Slide 38

## 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

- 30 m DEM

- 120 m DEM

- 120 m slope

- 60 m slope

- 30 m DEM

- 30 m slope

- 60 m slope

- 30 m DEM

- 60 m DEM

- 30 m DEM

- 30 m slope

- 120 m slope

**Sample of Slope Generation Approaches**

- compute

- aggregate

- aggregate

- aggregate

- aggregate

- compute

- compute

- compute

## Results - DEM

## Results - DEM

## Image Results -- DEM

- 30-480 m Pixels

- 210-480 m Pixels

## 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

## 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

**Slope Image Comparison**

- 30 m to 480 m pixels

- 210 m to 480 m pixels

## Sample of Land Cover Aggregation Approaches

- 30 m LC

- 210 m LC

- 480 m LC

- 210m LC

- 30 m LC

- 60 m LC

- 120 m LC

- 30 m LC

- 120 m LC

- 30 m LC

- 960 m LC

- 1920 m LC

- aggregate

- aggregate

- aggregate

- aggregate

- aggregate

- aggregate

- aggregate

- aggregate

## 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

- 30-480 m Pixels

- 240-480 m Pixels

## Image Results - Land Cover

- 30-210 m Pixels

- 120-210 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**

## Slide 55

## Slide 56

## Slide 57

## Slide 58

## 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**

## 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**