By: GeoTrek

Implementation of a Geodatabase for Freeman

Center

Hunter Krenek: Remote Sensing analyst & GIS analyst

Joe Dowling: Assistant Project Manager & GIS analyst

Peter Vogt: Website Designer & GIS analystAlfredo Perez: Project Manager & GIS

specialist

Team Members

Purpose: Build a functioning geodatabaseCreate a brochure with map atlasLand cover classification of huisacheThis project is designed to enable future

research and assist in the maintenance required to keep up the ranch.

The map atlas and brochure will also be used to help navigate the ranch to visitors, research partners, and stakeholders

Project Description

Freeman CenterFreeman Center is

4,200 acres of land in the Texas hill country

That is owned by Texas State University

Goals are to provide effective stewardship of the center’s ecosystem and infrastructure

Task 1: Building a Geodatabase

Advantages of ArcGIS

Digital copy of filesPerform spatial

analysisOrganizationAttribute tablesFile type Display with multiple

projections

Task 1: Building a Geodatabase

Cultural Features

Roads Boundary Fences Creeks Buildings Pastures Wells Drinkers

Cultural Features

Data Texas Parks and Wildlife Department (TPWD)Texas Natural Resources Information System

(TNRIS) Capital Area Council of Governments

(CAPCOG)Digital Ortographic Quarter Quad (DOQQ) .kmz file from Google EarthGPS data collection

Data & Methodology

Our First step was to convert the .kmz files to shape files that could be used in ArcMap.

Data & Methodology

Ranch Bondries.kmz Ranch Bondries_1.lyr

The newly created shapefiles were then used to populate the geodatabase.

Data & Methodology

Creating TopologyA topology feature class was created to set

rules for feature relationships.

Data & Methodology

Creating TopologyRules

Must not have gapsContains pointsMust be insideMust not have danglesMust not self-intersectMust not overlapMust be properly inside boundary

Data & Methodology

Creating TopologyRun error reports based on rules created in the

topology feature classFix or omit errorsTopology edit tools used:

ExtendTrim

Data & Methodology

Creating Feature Classes A polygon feature class was created to

encompass the pastures and buildings present on the ranch.The pasture feature class was created based of the

fences layer digitized from the original .kmz.The buildings feature class was created by

performing a trace while using the 6’’ satellite imagery as a georeference.

Data & Methodology

Edit Attribute TablesFor the roads, fences, pastures, and creeks

layers the calculate geometry tool was used to convert distance from decimal degrees to meters and acres.

For the fence layer the field calculator was also used to categorize the fences into high fence or low fence.

Data & Methodology

Data & Methodology

The boundary layer was used to extract by mask the DOQQ to limit the extent to our study area.

Data & Methodology

Data & Methodology

6’’ satellite imagery was used to georeference the locations of features such as: Buildings

DrinkersWellsFencesRoads

Website Compilation Data & Methodology

Data & Methodology

Manifold was used to generate a map document that was then exported to a website file.

The Freeman Center is experiencing a huisache encroachment on its property.

The Freeman Center has requested a land cover classification for huisache be created for inclusion in the geodatabase provided by GeoTrek.

Task 2 Abstract

2008 and 2012 1m resolution NAIP Imagery (TNRIS)

Freeman Center Boundary Line Shapefile SSURGO Soil Survey ShapefileHuisache tree GPS points

Data

Obtain the NAIP imagery from 2008 and 2012Create mosaic images using Erdas Imagine

Extract AOI from mosaic imagesThe extracted false-color composite images of the

Freeman Center were then used to perform a landcover classification with the following classes:WaterDevelopedBarrenForest/ShrublandHerbaceous (grassland)

Methodology

Training dataMade using the Grow Region and Polygon Tools

with the Signature Editor in Erdas Imagine Google Earth used to make a visual confirmation

of the landcover type. Due to poor spectral separability in the

training data the Forest and Shrubland classes were merged to yield one class with greatly improved separability.

Methodology

Accuracy Assessment:Accuracy Assessments were performed with an

expected accuracy of 80% and an acceptable error of 10%.

A sample size of 64 reference points was derived using Binomial Probability Theory.N=[22 (80)(20)]/102

Methodology

After completion of the initial landcover classification, the Forest/Shrubland class was extracted to narrow the AOI for the huisache classification.

The GPS point data gathered on site was then used in creating training data. Identified huisache trees in the false-color

composite images

Methodology

Spectral Separability of Huisache vs Mesquite:Positively identifying the preferred soil types of

huisache and mesquiteGPS Data Point Sampling Design:

Training Data and Accuracy AssessmentsDetermining the Best Imagery to Use:

NAIP imagery:Good:

1 meter spatial resolution Publicly available

Bad: First included the NIR band in 2008 2008 images comprised of 3 bands, eschewing the blue band in

exchange for NIR.

Constraints and Limitations

Task 1:Implement functioning geodatabaseAttribute tables with correct measuring units

for each feature I e: pastures = acresMaps

FEMA Q3 Navigation SoilsEcological Systemswebsite with interactive map

Results

Functional Geodatabase

Results

Correct Unit Measurement in Attribute Tables

Results

Correct Unit Measurement in Attribute Tables

Results

Database Composite

Fences

Roads

Drinkers

Wells

Pastures

Buildings

Texas Ecological Systems Map

Remote Sensing analysis produced the following:Classified 2012 Map Image

Training Data with polygons2012 NDVI Map Image2012 Tasseled Cap Transformation Map ImageMethodology Constraints and Advice for Future

Researchers

Results Task 2

2012 Classified Map Image

Accuracy = 85.94%

Kappa statistic = .8187

2012 Tasseled Cap Transformation

Map Image

2012 NDVI Map Image

Interactive web map

Task 1-Before our team started working on FreemanCenter.gdb., The Freeman Center only possessed .kmz files created using GoogleEarth, and had zero spectral data. The attribute fields oflength and area were calculated from degrees into meters andacres respectively. The Freeman Center can now have access toa geodatabase that can be used to make maps and for surface modeling. Task 2-A viable Freeman Center huisache tree classification will be a topic for ongoing research at Texas State University. Although this initial investigation met with setbacks, the project report will contribute to future progress in identifying the spec-tral signature of this species.

Conclusion