mapping solar potential obstructions using lidar data · /16 insolation, in kwh/m2/day 3 0 2.0 4.0...
TRANSCRIPT
Mapping solar potential obstructions
using LiDAR dataKrista Amolins
David Coleman, Yun Zhang, Peter DareUniversity of New Brunswick, Fredericton, NB
ASPRS 2011
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Sunlight in Milwaukee
2
Sunrise, sunset, dawn and dusk times Sun path
Dec 21
Jun 21
May 1
Image Source: http://www.gaisma.com
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Insolation, in kWh/m2/day
3
0
2.0
4.0
6.0
8.0
J F M A M J J A S O N D
Fredericton Iqaluit Milwaukee
Source: NASA Langley Research Center Atmospheric Science Data Center, via http://www.gaisma.com
If could capture and convert 100% of the solar radiation hitting a 1 m2 area on May 3:- power 5 100 W light bulbs for 10 hours- make 25 pots of coffee- dry 1 load of laundry
BUT high efficiency solar panels convert only 18%
http://geonb.snb.ca/geonb/N
Sun angles December 21
rise 124°set 236°
max elev 20.7°
Limited Solar Potential
Fredericton, NB, Canada
Worst CaseBest Case:
Obstructions...
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LiDARpoint cloud
solid surface
worst case obstructions
solid object points
best case obstructions
Workflow for Mapping Obstructions
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LiDARpoint cloud
Refinements...
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LiDAR Point Cloud
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50 m
Data collected November 2007 ➙ leaf-offUp to four returns per pulse; most last returns from ground
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Solid Surface
750 m
Grid resolution 1 mSimple filtering: last returns.Contains ground, buildings, evergreen trees and hedges, cars, some artefacts from deciduous trees.
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• Worst case means: on the shortest day, when the sun does not reach a high elevation, will sunlight reach the surface?
• Compare LiDAR points to surface cells: are objects obstructing the sun at any time?
Worst Case Obstructions
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Worst Case Obstructions
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LiDAR Point Cloud Solid Surface
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Worst Case Obstructions
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LiDAR Point Cloud Solid Surface
Sun angles December 21
rise 124°set 236°
max elev 20.7°
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Worst Case Obstructions
9
LiDAR Point Cloud Solid Surface
Sun angles December 21
rise 124°set 236°
max elev 20.7°
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LiDARpoint cloud
solid surface
worst case obstructions
solid object points
best case obstructions
Workflow for Mapping Obstructions
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solid object points
Refinements...
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Solid Object Points
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Data collected May 2006 ➙ leaf-onPoints classified using intensity, local density, and local variation in height
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Solid Object Points
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Non-solid Objects
Data collected May 2006 ➙ leaf-onPoints classified using intensity, local density, and local variation in height
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• Best case means: (on the shortest day)are any of the obstructions not solid objects?
• Compare only solid object LiDAR points to surface cells: are objects obstructing the sun at any time?
Best Case Obstructions
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Best Case Obstructions
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Greyscale: Solid surfacePink: Non-solid Object Points
Sun angles December 21
rise 124°set 236°
max elev 20.7°
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Greyscale: Solid surfacePink: Non-solid Object Points
Best Case Obstructions
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Best Case Obstructions
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Non-solid Objects Included
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Best Case Obstructions
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Non-solid Objects Included Non-solid Objects Excluded
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LiDARpoint cloud
solid surface
worst case obstructions
solid object points
best case obstructions
Workflow for Mapping Obstructions
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Refinements...
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Refinements - Directional
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Greyscale: Solid surfaceGreen: Non-solid Object Points
East
South
West
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Refinements - Directional
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Greyscale: Solid surfaceGreen: Non-solid Object Points
East
South
West
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Refinements - Directional
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Greyscale: Solid surface
East
South
West
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Refinements - Directional
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Greyscale: Solid surface
East
South
West
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Recap
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• Renewable energy sources, e.g., solar
• Low insolation in winter
• Filter LiDAR data: ground (solid surface) and obstructions (everything)
• Classify LiDAR data: solid objects (not trees, power lines...)
• Analyze obstruction direction, seasonal solar variations to refine results
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Further Work• Apply better filtering methods
• Smooth solid surfaces
• Quantitative assessment of results
• Improve classification
• Separate trees from power lines
• Locate building edges
• Apply to vertical surfaces19
Thank You!