This document discusses using supervised classification of aerial imagery to identify methamphetamine lab locations in Denver, Colorado. The researcher obtained 2008 meth lab location data and aerial images. He used ERDAS to perform supervised classification to identify healthy and unhealthy vegetation, then analyzed meth lab sites against control sites. The results showed no statistically significant difference between meth lab and control sites in Denver's urban environment, likely due to low image quality. Better identification may require higher quality images, more samples, and analysis in rural areas with open fields.

1. Supervised Classification
Identifying Methamphetamine Locations in Denver Colorado
By: Chad Yowler

2. Overview
• Meth – “Methamphetamine
(Also Called Meth, Crystal,
Chalk, And Ice, Among Other
Terms) Is An Extremely
Addictive Stimulant Drug That
Is Chemically Similar To
Amphetamine” (National
Institute On Drug Abuse, 2014)
• Economic, Social, And
Environmental Effects
• In 2008 – 8810 Meth Lab
Seizures
• Rural Low Income Areas
Have A Higher Incident
Rate
• Chemical Dumping
Damages Vegetation,
Water, And Has Negative
Effects On Wildlife

3. Why Remote Sensing?
• Can Identify Changes In
Landuse/Vegetation The
Human Eye Cant Detect
• Aerial Imagery As A
Supplement For Ground Truth
• Quick Analysis Of Many Areas
Through One Signature
• Potential Predictive
Capabilities

4. Method Used In Other Research
• Jasper County Missouri
• Highly Rural
• Highest Percentage Meth Lab
Seizures Per Capita
• Used Supervised Classification To
Identify Healthy and Unhealthy
Vegetation Percentages
• Compared Meth Sites To Control
Sites
• Statistically Significant Results
Source: Hall, 2014

5. Applying The Method to an Urban Setting
• Denver-low amount of
incidents
• 23rd Largest City by Population
• Highly Urban
• Easy Access to Data and
Statistics
• 4 Meth Lab Locations in 2008
• Good Study Area
Source: HomeFacts.com, 2015

6. Research Plan
• Locate 2008 Data
• Meth Lab locations
• ShapeFiles
• Images
• Process Data
• Locate/Develop Study Regions
• ERDAS Image Processing
• Created Signature
• Performed Supervised Classification
• Subset Control And Study Regions
• Performed Visual Analysis
• Performed Statistical Analysis
Source: Montana State University, 2014

7. Data Acquisition
• Denver Open Data Catalog
• DEA: Drug Enforcement Agency
• Yourmapper
• Allowed For Export Into Google
Earth
• Earth Explorer
• USGS: United States Geological
Survey

8. Geoprocessing
• Points Were Put Into KML Format
From Google Earth
• ArcMap Tool To Create Shapefile
From A KML
• Used Select By Attribute To Select Only
2008 Locations
• Created New “2008methlab.Shp File”

9. Study Regions
Region BRegion A

10. Create Signature
• Create New AOI Layer
• Raster->Supervised->Signature
Layer
• Select Pixels From Map->Merge-
>Name->Assign Color
Healthy(Green) Unhealthy(Red)

11. Clip Study Regions/Perform Supervised
Classification
• Create New AOI->Draw Polygon->Select
Raster->Subset & Chip->From AOI
• Supervised Classification->Maximum
Likelihood
Region A Region B

12. Visual Analysis
• Meth Labs • Not Meth Labs
A
B B
A

13. Statistics
Meth Lab
A
Healthy%:
46.67
Standard
Deviation(s):
.514
P-Value:
.314
Meth Lab
B
Healthy%:
32.17
Standard
Deviation(s):
1.597
P-Value:
.085
• Based On The Supervised
Classification Of The Meth Lab
Locations There Was Not A
Statistically Significant Identified
Change In Healthy Vegetation
Percentage
• Lab B Was Better Identified Using
The Signature
• Nearly No Difference In Meth Lab
A Compared To Control Sites
• At 90% Confidence Interval Meth
Lab B Would Meet Threshold Of
Statistical Significance
One Tailed T-Test
Confidence Interval α=.05
σ = 13.367
x̄ = 53.527

14. Conclusion
• A Lot Of Assumptions Made
• Selection Bias
• Could Be Improved With Random
Sampling
• Image Quality Too Low To Identify
Great Change In Vegetation At A
Parcel Level
• Works Better With Open Fields (Rural
Areas)
• Supervised Classification Can Identify
Healthy And Unhealthy Vegetation
• Difficult To Use For Identifying Meth
Lab Locations In An Urban Setting
• Study Could Be Improved With More
Samples
• Better Classification Can Be Done
With More Time, Better Images, And
A Larger Sample

15. Work Cited
• (n.d.). Retrieved December 8, 2015, from http://forensic-applications.com/meth/DEA_Druglab_list.pdf
• City Mayors: Largest 100 US cities. (n.d.). Retrieved December 8, 2015, from
http://www.citymayors.com/gratis/uscities_100.html
• Denver, CO Former Drug Labs Information and Locations. (n.d.). Retrieved December 8, 2015, from
http://www.homefacts.com/methlabs/Colorado/Denver-County/Denver.html
• Hall, M. (2014). EVALUATING THE POTENTIAL OF USING MULTISPECTRAL IMAGERY TO IDENTIFY AREAS OF
METHAMPHETAMINE PRODUCTION IN JASPER COUNTY, MISSOURI.
• Home. (n.d.). Retrieved December 8, 2015, from http://www.fs.fed.us/lei/dangers-meth-labs.php
• Methamphetamine. (n.d.). Retrieved December 8, 2015, from
http://www.drugabuse.gov/publications/drugfacts/methamphetamine
• Spectral Imager. (n.d.). Retrieved December 8, 2015, from
http://www.coe.montana.edu/ee/seniordesign/archive/FL10/tethered_balloon/index.html