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

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

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).

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

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

Tree Canopy Assessment

• Top Down Approach Remote Sensing and GIS

• Proprietary and Free and Open Source (FOSS)

ArcGIS

Feature Analyst

SAGA

QGIS

R

Python

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

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

FunctionGIS

Data

Target Geographies

Final Land Cover

Aerial Imagery

Object Based Image

Analysis (OBIA)

Land Cover (Raster/Vector

Digital Surface Model

Pixel Counts

LiDAR (LAS files)

Key

Data

FunctionGIS

Data

Target Geographies

Final Land Cover

FOSS Tools

Count Pixels

(R)

Create DSM

(Python,SAGA)

Sum Totals(Python)

QA/QC(R, QGIS)

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

• Use Python Scripts to create DSM from LiDAR

• Calls to functions in SAGA

LiDAR Data Processing

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

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)

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!)

Output Data

• R Script

• Count the pixels for

each land cover type

in each target

geography

Calculate MetricsStep 1

• Python Script

• Calculate totals (pixels * conversion factor = area)

and percents for each landuse type in each target

geography

Calculate MetricsStep 2

Aspen Community Forest

• GIS Data

Aspen Community Forest

Aspen Community Forest

• 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

Questions?