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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 [email protected](505) 842-3861
Brad Weigle – Sr. Program ManagerQuantum [email protected](727) 576-9500