The city of Aspen has a diverse and extensive community forest comprised of natural forested areas, street and park trees, yard trees, and riparian corridors. Trees are a key asset to experiencing downtown Aspen. In this study, we utilized several open source GIS software to analyze the tree canopy extent as well as new tree planting areas. Several land cover metrics were calculated using geoprocessing routines across a variety of spatial planning scales including city limits, parcels, and zoning categories. The data informs planning and development, stormwater modeling, education/outreach, and natural areas monitoring. Methods, tools, and results will be presented.

1. Analyzing Aspen's
Community Forest
with LiDAR, Object-Based Image Analysis,
& Open Source GIS Software
Andrea Santoro, Senior GIS Analyst
Laura Atkinson, GIS Analyst & Jr. Software Developer

2. Company Overview
• Plan-It Geo was established in 2012 (7 full time staff)
• Focus is on Urban Forestry and Ecosystem Services
• Utilize proprietary and open source technologies for GIS,
Remote Sensing, and Web/Mobile/Desktop applications
In this presentation:
 Why trees?
 How we integrate open source geospatial
technology into our canopy mapping process

3. Why Trees?
Air quality:
Trees absorb, trap, offset, and hold
pollutants such as particulates,
ozone, sulfur dioxide, carbon
monoxide, and CO2.
Water quality:
Soil aeration, evapotranspiration,
and rainfall interception by trees
improves water quality and
helps manage run-off.
Erosion control:
Tree roots hold soil together along
stream banks and slopes.
Wildlife habitat:
Trees promote urban biodiversity.
Property value:
Each 10% increase in tree cover
increases home prices by
$1,300+ (Sander, Polasky, &
Haight, 2010).
Energy conservation:
Trees lower energy demand
through summer shade and
winter wind block, offsetting
power plant emissions.
Stormwater mitigation:
Urban forests intercept
stormwater, reducing the need
for costly gray infrastructure.
Public health:
Trees diminish asthma
symptoms and reduce UV-B
exposure by about 50% (Shade:
Healthy Trees, Healthy Cities,
Healthy People, 2004).
Crime and domestic
violence:
Urban forests help build
stronger communities. Nature
and trees provide settings in
which relationships grow
stronger and violence is
reduced.
Noise pollution:
Trees act as a buffer, absorbing
up to 50% of urban noise (U.S.
Department of Energy).

4. Trees Take Effort
+ Planting, Management, Policy, Money, Water
- Development, Pests, Diseases, Storms
Ash Tree Lined Street: Belvedere Drive, Toledo, OH
Before and After Emerald Ash Borer Infestation (2006-2009)
Image credit: US Forest Service

5. Data, Data, Data!
• How much tree canopy exists?
• Where are we lacking trees?
• Where can we plant more trees?
• What species of trees are where?
Image credit: http://bestutopiaever.wikispaces.com/
 Quantify
 Measure
 Track
 Map
 Analyze

6. Tree Canopy Assessment
• Top Down Approach  Remote Sensing and GIS
• Proprietary and Free and Open Source (FOSS)
 ArcGIS
 Feature Analyst
 SAGA
 QGIS
 R
 Python

7. Case Study: Aspen, CO
• Map Aspen’s urban tree canopy (community forest) and
possible planting areas
• Generate metrics at various geographic scales:
• Citywide
• Zoning / Land Use
• Parcels
• Right-of-Way

8. Aspen Process Overview
Aerial
Imagery
Object
Based Image
Analysis
(OBIA)
Land Cover
(Raster/
Vector
Count
Pixels
Digital
Surface
Model
Pixel
Counts
Create
DSM
Sum Totals
QA/QC
LiDAR
(LAS files)
Key
Data
Function
GIS
Data
Target
Geographies
Final
Land Cover

9. Aerial
Imagery
Object
Based Image
Analysis
(OBIA)
Land Cover
(Raster/
Vector
Digital
Surface
Model
Pixel
Counts
LiDAR
(LAS files)
Key
Data
Function
GIS
Data
Target
Geographies
Final
Land Cover
FOSS Tools
Count
Pixels
(R)
Create
DSM
(Python,
SAGA)
Sum Totals
(Python)
QA/QC
(R, QGIS)

10. LiDAR Data Processing
• SAGA GIS: System for Automated Geoscientific Analysis
• Free and Open Source Software (FOSS)
• View and process raw LAS files and interpolate to surface models

11. • Use Python Scripts to create DSM from LiDAR
• Calls to functions in SAGA
LiDAR Data Processing

12. 3 band DSMNAIP
4 band
• High resolution aerial imagery – 3 band SID
• Aerial imagery from USDA’s National Aerial Imagery Program
(NAIP) – 4 band
• LiDAR derived Digital Surface Model (DSM)
Input Data

13. 3 band DSMNAIP
4 band
Input Data
• High resolution aerial imagery – 3 band SID
• Aerial imagery from USDA’s National Aerial Imagery Program
(NAIP) – 4 band
• LiDAR derived Digital Surface Model (DSM)

14. Output Data
DSM Tree
Canopy
NAIP
4 band
• Feature Analyst Extension (proprietary)
• Object Based Image Analysis (Remote Sensing)
• Derive Tree Canopy and Other Land Use Classes
• Run accuracy scripts (and repeat!)

15. Output Data

16. • R Script
• Count the pixels for
each land cover type
in each target
geography
Calculate Metrics
Step 1

17. • Python Script
• Calculate totals (pixels * conversion factor = area)
and percents for each landuse type in each target
geography
Calculate Metrics
Step 2

18. Aspen Community Forest
• GIS Data

19. Aspen Community Forest

20. Aspen Community Forest

21. • Assessment Metrics by Zone Class
Zone Class Description
Total
Acres
Land
Acres
Canopy
(acres)
Canopy
(%)
Dist. Of
Canopy
Plant.
Space
(acres)
Plant.
Space
(%)
Dist. Of
Plant.
Space
Multi-Family
Residential 116 116 26 22% 7% 32 28% 7%
Residential 346 346 147 42% 40% 105 30% 24%
Open Space 496 496 133 27% 36% 237 48% 53%
Commercial 83 83 20 24% 5% 23 28% 5%
Lodging/Recreation 56 56 14 25% 4% 17 31% 4%
Right of Way 131 131 32 25% 9% 28 21% 6%
OVERALL 1,229 1,229 372 30% 100% 442 36% 100%
Distribution of Tree Canopy Distribution of Plantable Space
Aspen Community Forest

22. Questions?