Geographic Information Systems in Water Science Unit 4: Module 16, Lecture 3 – Fundamental GIS...
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Transcript of Geographic Information Systems in Water Science Unit 4: Module 16, Lecture 3 – Fundamental GIS...
Geographic Information Systems in Water Science
Unit 4: Module 16, Lecture 3 – Fundamental GIS data types
Developed by: Host Updated: 10.04.04 U4-m16.3-s2
Spatial data formats in GIS
Vector formats Points Lines Polygons
Raster formats Grid coverages
Image data Georectified “pictures”
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Point data to show locations
Used to identify single locations, such as monitoring sitesweather stations, well locations and other point features
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Point data as a sample
Points collected as a sample can be used to create continuous interpolated surface
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Point data as a sample
A Triangulated Irregular Network (TIN) is a surface created by connecting points
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Point data as a sample
Filling in the triangular faces of a TIN creates a surface, which can then be coded by elevation. Aspects are used to create shading.
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Representing linear features: line objects
Streams and rivers
Roads Railroads Power lines
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Representing linear features: line objects
Lines consist of nodes and vertices Node – endpoints of line segments Vertices – intermediate points along line To show flow direction, lines may have a “From
Node” and a “To Node”
•From Node
Vertices
•To Node•Direction of flow
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Special characteristics of streams
Streams often “flow through” lakes to maintain continuity of stream object
Left and right banks usually not treated separately
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Polygons: mapping areal data
A polygon map layer consists of irregularly shaped areas
Boundaries are continuously curved lines Digitally represented as polylines – an ordered
sequence of points connected by straight lines Denser points = more accurate areas
Every point lies in exactly one polygon Polygons do not overlap Polygons “tesselate” the space
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Polygon data tables
Each polygon is associated with a line in a data table, which contain “attributes” of the feature: Area Perimeter Polygon ID User supplied data
Land use typePopulation densitySoil typeRelational database codes
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Polygon data tables
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Attributes of polygon data
The value for each polygon is an average, total or some other aggregate property Representation is complete All variation within areas is lost
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Common polygon data sets
Watersheds (at many scales) Land cover
Land use/land cover Natural features
Forest types Soil series or classes Geologic features
Socio-economic data Political or administrative boundaries Census data Land ownership
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Raster data sets
Points, lines and polygons are called “vector data”
Other data sets are better represented in grid or raster format Basic map unit is a pixel –
a square cell containing information, organized in rows and columns
Remotely-sensed data from satellites are typically in raster format
National Land Cover Data (NLCD) from Green Bay Wisconsin – 30 m pixel resolution
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Raster data
Pixels range in size over several orders of magnitude
Satellite Pixel size
AVHRR 1 km
Landsat MSS 80 m
Landsat TM 30 m
QuickBird 2.4 m QuickBird image of Erie Marsh, showing suspended sediment plumes - 2.4 m pixel resolution
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Raster data
Raster data are particularly well-suited to computer analyses Image classification
Raw data to land use Hydrologic modeling
Flow length, distance Watershed delineation Neighborhood
analyses Using a Digital Elevation Model to calculate flow length for each cell (pixel) within a watershed
Nemadji River Basin Western Lake Superior
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Common raster data sets
Raw remote sensing imagery Landsat, AVHRR, SeaWIFS
Classified remote sensing imagery National Land Cover Database C-CAP change analysis database
Digital Elevation Models (DEMs) Most GIS programs can readily convert
between polygon and raster data
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Image data
Pictures used as backdrops to other data sets Must be georeferenced to allow spatially accurate
overlays (e.g. GeoTIFF file format) Not useful for analytical purposes
Not associated with database
Georectified color infra-red photograph of portion of Miller Creek watershed, Duluth, MN
Roads and stream line coverages are superimposed on image
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Image data: Digital raster graphics (DRG)
Georectified topographic maps Often ‘seamless’
Edges removed, edgematched
Same location as previous slide, but with DRG
Miller Creek watershed, Duluth, MN
Roads and stream line coverages are superimposed on image
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Image data: Digital Ortho Quads (DOQs)
Digital Orthorectified Quarter-Quadrangles Aka DOQQs
Aerial photographs with distortions removed
Typically gray-scaled, high resolution images ~1 m resolution
Large file sizes! Many state agencies
have these available for download
Same location as previous slide, but with DRG
Miller Creek watershed, Duluth, MN
Roads and stream line coverages are superimposed on image
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Image data: Hyperspectral imagery
Fine spatial resolution (1.5 to 3 m)
A large number of spectral bands (30-100s)
Capable of discriminating very fine differences in color (reflectance)
Used to map aquatic veg, Chlorophyll content, turbidity, many other attributes
Hyperspectral image of Kingsbury Creek – image acquired by Nebraska Space Grant for WOW
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Summary
Most data sets you encounter will be in in vector (point, line, polygon) or raster format
The types of analyses possible differ by data type (WOW Module 19)
Image data are not typically used in analysis, but are very useful for conveying information to the public