Geographic Information Systems in Water Science Unit 4: Module 16, Lecture 3 – Fundamental GIS data types

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”


Point data to show locations

Used to identify single locations, such as monitoring sitesweather stations, well locations and other point features


Point data as a sample

Points collected as a sample can be used to create continuous interpolated surface



A Triangulated Irregular Network (TIN) is a surface created by connecting points



Filling in the triangular faces of a TIN creates a surface, which can then be coded by elevation. Aspects are used to create shading.


Representing linear features: line objects

Streams and rivers

Roads Railroads Power lines


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


Special characteristics of streams

Streams often “flow through” lakes to maintain continuity of stream object

Left and right banks usually not treated separately


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



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


Polygon data tables


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


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


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


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


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


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


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


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



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



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


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

Documents

Geographic Information Systems in Water Science Unit 4: Module 16, Lecture 3 – Fundamental GIS data types