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Transcript of 2004 - Minnesota ASPRS Workshop 1 Integrating Imagery Remote Sensing for GIS Project Managers...
2004 - Minnesota ASPRS Workshop
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Integrating ImageryRemote Sensing for
GIS Project Managers
Timothy L. Haithcoat
University of Missouri
GRC/MSDIS/ICREST
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What is Remote Sensing?
• The science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with it.
• Remote sensing is a tool - not an end in itself
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GENERALLY
• Question on what the problem ‘is’ comes from detailed ground observation
• Remote sensing comes in at where, how much, and how severe the problem is.
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Considerations
• Photograph scale is a function of terrain elevation - hence ortho-rectification needed
• Geometry - ground control
• Finer scales = higher costs & more photos
• Photo-interpreter - hard to maintain consistency– Mental acuity + visual perception
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Reference DataGROUND TRUTH
• Collecting measurements or observations about the features being sensed
• Two types - time critical / time stable
• Three uses– Aid in analysis and interpretation of data– Calibrate sensor– Verify information extracted from image data
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Raster Model
• Divides the entire study area into a regular grid of cells in specific sequence– The conventional sequence is row by row from the top left corner– Each cell ( or picture element - PIXEL) contains a single value– Is space-filling since every location in the study area corresponds to a cell
in the raster– One set of cells and associated values is a layer
• There may be many layers in a database • Examples: soil type, elevation, land use, land cover
• Tells what occurs everywhere - at each place in the area
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Creating a Raster
• Consider laying a grid over a land cover map– Create a raster by coding each cell with a value that represents the land cover type
which appears in the majority of that cells area
– When finished, every cell will have a coded value
water
grass
forest urban
W W W
W W W
W W W
G G
G G
G G
G G G G G
G G U U
UUGF
F
F
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Influence of Spatial Resolution
• Consider laying a coarser grid over our land cover map– Problem of mixed pixels or cells
– Implications when landscape is broken up into fine pieces
water
grass
forest urban
W G
W G G
UF
G
U
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Influence of Spatial Resolution
• Consider laying a finer grid over our land cover map– Resolution needed to discriminate the smallest object to be mapped
– Implications on file size and access times
water
grass
forest urban
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Zoom Scale Change of a 1”=400’ Scale Features
Scale 1”=400’
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Zoom Scale Change of a 1”=400’ Scale Features
Scale 1”=200’
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Zoom Scale Change of a 1”=400’ Scale Features
Scale 1”=100’
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Zoom Scale Change of a 1”=400’ Scale Features
Scale 1”=50’
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Zooming an Image...
• Does not Change the Accuracy
• Does not Change the Resolution
• You merely enlarge or reduce your view of the images original Pixels
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Having Said All that...
What IS the Impact of Resolution?
Same Scale Image Viewed with Different Resolutions...
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Resolution 0.5’/pixel
Scale 1”=50’
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Resolution 1’/pixel
Scale 1”=50’
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Resolution 2’/pixel
Scale 1”=50’
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Resolution 4’/pixel
Scale 1”=50’
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Impact of Resolution
• Spatial resolution at which the imagery is actually acquired plays a key role in determining what you can use this imagery for.
• You can zoom in all you want but it can not change the resolution at which it was acquired!
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Landsat 7 ETM+
15 m
SPOT 10 m
Indian Remote Sensing (IRS)
5 m
IKONOS 1 m
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Positive Systems 0.7 m
IKONOS4 m
Indian Remote Sensing 20 m
Landsat MSS 60 m
Landsat ETM + 30 m
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Other Resolution Concepts
• Spatial– Smallest resolution element– Areal coverage
• Radiometric– Number of brightness values detected
• Spectral– Number of bands– Bandwidth– Location of bands within the spectrum
• Temporal– Frequency of revisit– Time of day
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IKONOS 1M Pan vs DOQQ 1M Radiometric Resolution Comparison
IKONOS DOQQ
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1 meter Pan image
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4 meter Multi-spectral image
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Data Fusion: Pan and MS
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Sidewalks in pan image
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Imagery as a Central Data SourceImagery as a Central Data Source
• In the past, imagery and spatial data was often separate
GIS Guysvs.
Image Processing & Photogrammetry Guys
• Recent developments in technology have moved these much closer and they will increasingly be closer.
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Trends in Remote Sensing Systems
• Continuity of established programs (Landsat, SPOT)
• Higher spatial resolution• Wide-field monitoring sensors• Hyperspectral sensors
(dozens to hundreds of bands)
• Radar and Lidar• More commercial systems
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What is Needed to Estimate Project Costs?
Estimates of Project Area in Square Miles
Estimates of Image Costs per Square Mile
A Set of Business-based Assumptions
Image Specifications
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Mixing Alternate Scales
• You can reduce the project costs by changing the projects scale requirements or by mixing scales.
• This concept matches the appropriate scale to a corresponding subject area.
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Basic Issues to Integration
• What follows in the next few slides are examples of simple imagery integration issues that the GIS Project Manager will face.
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DOQQ 1MShift Differential
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IKONOS 1M PanShift Differential
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Example of Control Point Selected from IKONOS Imagery
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DOQQ Match
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Histogram Matched DOQQ’s
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Spatial Resolution: Limitations
Less area covered: with minimum strip of 11km x 11km
Requires lots of time for processing: ex. Mosaicing
Columbia metro area covered in two images (necessarily collected on different dates)
Tones do not match properly because imagery was taken at different times
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Shadow Effects
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Cloud cover
Frequent revisit helps in easy update and more chances for acquiring cloud free data.
Minimum cloud cover is 20 %. You have to pay extra money.
PE: It can take many months to get cloud free data.
Suggestion: Ordering the data between known cloud-free dates would help
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Azimuth
IKONOS satellite allows to specify the specific image acquisition angle
But, it will be treated as a nonstandard order and may result in a longer delivery time frame and additional surcharge
352
Walnut
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The next series of slides will present a tool used to integrate legacy GIS vector
information with newer and more accurate imagery data
More Involved Integration Issues
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Integrating ImageryThe Local Problem
• Vector GIS data lineage may preclude direct integration with image data sets– Mapping pre-dates computers– Stand-alone system organized by tiles– Integration with other data – GPS– Huge investments in GIS data
• Imagery can provide the accurate base map materials to meet these needs
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GIS Vector Linework
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Imagery Acquired
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The Pervasive Problem
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Creating Image to Vector Linkages
• Extracting the nodes from the image based road centerlines file
• Building or acquiring a centerline vector file from within the current local GIS and building a node file from this source
• Conducting a local-area search to establish the positional relationships between these two sets of nodes.
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Example of LinkageGIS Vector to Image
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X-Shift Surface Depicted as Tin
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Y-Shift Surface Depicted as Tin
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Resulting AdjustmentParcel Data Layer
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Resulting Imagery Overlay
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Resulting Options
• From these spatial relationships two surfaces are created to allow:– Consistent positional recalculation of vector
points, lines, and polygons based on imagery – Visualization of the variation in error
magnitude across ‘old’ vector database– Prioritization of resurvey work by local
jurisdictions– Pathway for all associated databases built on
the vector base
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The next series of slides will show what newer technologies associated
with LIDAR data and Extraction can derive from imagery data
LIDAR Data Analysis
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1 m Laser DEMSpringfield, MO
Elevation (m)
360.0
670.0
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Building Extraction
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Comparing Hipped (L) and Gabled (R) Buildings
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Then there is always Policy Issues
• Data Ownership– public - free access– private - limited license
• Privatization– ownership of launch vehicles, satellites,
sensors, and distribution rights
• Cost of data– cost of filling user requests– partial government subsidy– full cost recovery
• Data archives• National Security
– spatial resolution limits– shutter control
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• Overall Benefits Include:– Imagery/Basemaps for use in GIS systems
– New information product(s) not available previously
– Improved accuracy/utility over existing products
– Increased speed of access for updating baseline information
– Personnel time savings in workflow
– Cost effective solutions
– Improved planning/decision making processes!!
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Conclusions
Unique, Timely, Cost Effective Solutions to Positively Impact Planning, Management, and
Decision Making Processes in Local Government
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Thank You
Questions, comments, or suggestions:
Tim Haithcoat
104 Stewart Hall – Univ. of Missouri
Columbia, MO 65211
E-mail: [email protected]