Automated Feature Extraction from Aerial Imagery for Forestry Projects

• Esri UC 2015• UC706

• Tuesday July 21

• Bart Matthews - Photogrammetrist• US Forest Service Southwestern Region

• Brad Weigle – Sr. Program Manager• Quantum Spatial

The Power of 4

Imagery of the Planet –Major Advances over 85 years

USDA B&W – 50cm1930s-1960s

LANDSAT 30m1970s - Present

RGB, NIR Film 1m1960s – 2000s

Digital 4-band 5cm +2000s - Present

LARGE FORMAT DIGITAL AERIAL CAMERAS

ADS 100

ULTRACAM EAGLE

DMC ii

GPS & IMU

Imagery for Topography• The forms of the features of the actual surface of the earth which

is typically referred to as the “Bare Earth”.• There are three primary terms for topography:

- Digital Elevation Model (DEM)- Digital Terrain Model (DTM)- Triangulated Irregular Network (TIN)

Digital Surface Model (DSM)

Similar to DEMs or DTMs, except that they may depict the elevations of the top surfaces of buildings, trees, towers, and other features elevated above the bare earth.

DIGITAL SURFACE MODEL

DIGITAL ELEVATION MODEL

DSM – DEM = Height of features (nDSM or PHASE)

Region 3 Forests with Digital Orthophotography

Recent Imagery: 4 band, 30cm pixels, 60% overlap, 30% sidelap

ERDAS IMAGINE Block Files –- an Aerial Triangulation Solution for

Orthophoto Production & Stereo Analysis- a starting point to produce point clouds

Point Clouds for Automated Imagery Analysis

Product: High resolution 3D point clouds from stereo images – points tagged with spectral info and height above sea level

Software to Create:Image Station, ERDAS,SocetSet, Inpho, …

We have developed analytical models to derive vegetation type, height, & canopy closure … even in areas with poor DEMs

Maximum point densities from pixels:

50cm = 4 pts/m2

30cm = 9 pts/m2

10cm = 100 pts/m2

5cm = 400 pts/m2

R3 Riparian Inventory Pilot • Build on Regional Riparian Mapping Project Report and Pilot Project Protocols - analogous to FIA

• 1ha riparian plots in Cibola and Prescott NFs

• Interpret vegetation, bank and stream characteristics using 5-8cm stereo aerial photos in Stereo Analyst & Modelbuilder

• Automate measurements using derivatives from image point clouds using Modelbuilder

Modelbuilder: densify riparian centerlines & select sample locations

• Centerlines of riparian polygons densified to 1m vertex spacing

• Randomized starting location• Systematic site selection

at 25600m intervals• Additional selection until all riparian types have 10+ sites

Modelbuilder Toolbox for Automated Calculations

Manually digitize R&L banks in 3D using Stereo Analyst

Models:•Divide R&L banks into 50 sample sites•Join R&L banks to create transects•Divide transects into 5 sample points•Measure 3D depth: transect point to bed•Determine flood plain along streambed•Calculate sinuosity & stream type

Determine Species Composition of homogenous polygons

Other Projects:Determining Tree Size (DBH) from Point Clouds

Create Point Clouds from Stereo ImageryUse FIA data to correlate Spp. Height to DBH

Use Modelbuilder to:Determine Pixel Heights above Surface (PHASE = DSM – DEM)Characterize distribution of PHASE for Individual PolygonsClassify polygons with Tree Size and Canopy Cover

Estimating Tree Size Classes using FIA data

R² = 0.823005

10152025303540

0 50 100 150

Ponderosa Pine

R² = 0.64030

10

20

30

40

0 50 100 150

Douglas Fir

R² = 0.58090

10

20

0 50 100

Lodgepole Pine

R² = 0.70950

10

20

30

40

0 50 100 150

Douglas Fir - Ponderosa Pine

FIA plot – TS6 Point cloud – TS3

FIA plot – TS6 Point cloud – TS6

Vegetation Mapping in Oregon

LiDAR / Image Cloud Comparison

LiDAR / Image Cloud Comparison

LiDAR / Image Cloud Comparison

Up to 60 cm offset & 10 cm or less near ground control

In Conclusion• Modelbuilder works extremely well to automate geospatial

processing of point cloud rasters and production of derivative data layers

• Point clouds derived from stereo pairs are acceptable alternatives to LiDAR for determining vegetation types, tree heights and canopy closure

• LiDAR is preferred, but a more costly option, for an accurate DEM & detailed forest structure

• Historic aerial imagery can provide valuable data for change detection and vegetation growth patterns – a virtual time-machine

• Scanning of historic imagery to digital format is ESSENTIAL before film degrades to ensure time-series analysis.

Our Contact Information

Bart Matthews - PhotogrammetristUS Forest Service Southwestern Regionbartmatthews@fs.fed.us(505) 842-3861

Brad Weigle – Sr. Program ManagerQuantum Spatialbweigle@quantumspatial.com(727) 576-9500