Upload
phuong
View
53
Download
1
Tags:
Embed Size (px)
DESCRIPTION
Urban Land-Cover Classification for Mesoscale Atmospheric Modeling. Alexandre Leroux. Objectives. Goal: Provide an urban land-cover database for North-American cities for mesoscale atmospheric modeling, specifically, for the Town Energy Balance model (TEB). - PowerPoint PPT Presentation
Citation preview
Urban Land-Cover Classification for Mesoscale Atmospheric ModelingAlexandre Leroux
Objectives
• Goal:Provide an urban land-cover database for North-American cities for mesoscale atmospheric modeling, specifically, for the Town Energy Balance model (TEB).
• Mean:- Approach #1 (presented last year)Satellite imagery and DEM analysis- Approach #2Vector data processing and DEM analysis
Satellite approach - Workflow
Satellite imagery unsupervised classification
Building height assessment
through SRTM-DEM minus
CDED1 or NEDP
rocessing and analysis
Statistics and fractions at a lower scale
Decision tree
Results readied for
atmospheric modeling
Satellite approach results
• 30 to 40 “simple elements” identified on satellite imagery at a 15-m spatial resolution– e.g. asphalt, concrete, roofs, water, trees, grass &
fields• Results from the decision tree:
– 12 new urban classes generated at 60m– +/- 5 vegetation classes associated to gengeo
• Processing and analysis: ~ 1 week / urban area
Oklahoma City, 60 mOklahoma City, 60 m
Montreal, 60 mMontreal, 60 m
(detail, zoom 2x)(detail, zoom 2x)
Vancouver, 60 mVancouver, 60 m
(detail, zoom 4x)(detail, zoom 4x)
Vector approach - Workflow
National Topographic Data Base
• Vector data with 110 thematic layers– e.g. water, vegetation, golf course, built-up areas,
buildings (points and polygons), roads, bridges, railway, etc
• Most layers with attributes– e.g. a road feature can be ‘highway’, ‘paved’,
‘underground’.• A total of 2474 1:50,000 sheets covering Canada• Available internally within the federal government
Statistics Canada - 2001 Census Data
• Canada-wide coverage• Used to distinguish residential districts
– Population density calculated using this dataset– Includes the number of residences
• Available internally (license purchased by EC)
Statistics Canada – Population density
Topography and Height data
• SRTM-DEM– Top of features (e.g. buildings, vegetation)– Worldwide coverage and free– “Poor” spatial resolution (3 arc-second, ~90m)
• CDED1– Ground elevation– Canada-wide coverage and free– 1:50,000 (mtl: 16 x 23m)
• Subtraction to evaluated building height
“AutoTEB” Spatial Data Processing
• Automated dataset identification• Read/write multiple formats, including ‘.fstd’• On-the-fly reprojection and datum management• Different spatial resolution / scale management• Spatial data cropping, subtraction (cookie
cutting), buffering, rasterizing, SQL queries, multiple layer flattening (merge down), basic spatial queries, LUT value attribution and much more…
Results
• Some results for Montreal and Vancouver– Raster output at 5m spatial resolution, generates rater
data up to 10,000 x 12,000 pixels (Toronto)• Other processed cities
– Calgary, Edmonton, Halifax, Ottawa, Quebec, Regina, Toronto, Victoria, Winnipeg (SRTM-DEM - CDED1 not yet processed for those cities)
• The methodology, processing, analysis and results are well documented
TEB classes
• 46 ‘final’ aggregated classes– Buildings (18 classes)
• 1D & 2D, height, use (i.e. 24/7, industrial-commercial)
– Residential areas, divided by population density– Roads and transportation network– Industrial and other constructions
• e.g. tanks, towers, chimneys
– Mixed covers– Natural covers
Population density Population density classes, Montrealclasses, Montreal
1 km
Population density classes, Population density classes, VancouverVancouver
1 km
1 km
1 km
Transportation network, Transportation network, VancouverVancouver
1 km
Detail of Montreal,Detail of Montreal,
Scaled-down, 46 classesScaled-down, 46 classes
1 km
1 km
Detail of Vancouver,Detail of Vancouver,
Scaled-down, 46 classesScaled-down, 46 classes
1 km
1 km
Main benefits• Canada-wide applicability
– Full data coverage – Approach directly applied anywhere over Canada
• Complete automation– Single command with only one input parameter– One optional exception: SRTM-DEM minus CDED1– Fast! From 3 min to 40 min for the whole processing
• Numerous other advantages identified…– No interpretation and reduced human intervention– Flexible approach, code developed reusable– Spatial resolution of the results
Main limitations
• Up-to-date data– BNDT data based on “old” aerial imagery: missing
some downtown buildings and suburbs• Thematic representation
– No layer corresponding to rural areas and parking lots– Almost no distinction in vegetation types
• Various other minor limitations identified…
The future of the vector approach
• Adaptation to CanVect and other datasets, potentially including US territory datasets
• Use of 3D building models required for CFD modeling within the vector approach
• Various other improvements envisioned…– TEB sensibility analysis to urban LULC databases– Scientific article to be written– much more…