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

Developed by: Host Updated: U4-m16.3-s2 Spatial data formats in GIS  Vector formats  Points  Lines  Polygons  Raster formats  Grid coverages  Image data  Georectified “pictures”

Developed by: Host Updated: U4-m16.3-s3 Point data to show locations Used to identify single locations, such as monitoring sites weather stations, well locations and other point features

Developed by: Host Updated: U4-m16.3-s4 Point data as a sample Points collected as a sample can be used to create continuous interpolated surface

Developed by: Host Updated: U4-m16.3-s5 Point data as a sample A Triangulated Irregular Network (TIN) is a surface created by connecting points

Developed by: Host Updated: U4-m16.3-s6 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.

Developed by: Host Updated: U4-m16.3-s7 Representing linear features: line objects  Streams and rivers  Roads  Railroads  Power lines

Developed by: Host Updated: U4-m16.3-s8 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

Developed by: Host Updated: U4-m16.3-s9 Special characteristics of streams  Streams often “flow through” lakes to maintain continuity of stream object  Left and right banks usually not treated separately

Developed by: Host Updated: U4-m16.3-s10 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

Developed by: Host Updated: U4-m16.3-s11

Developed by: Host Updated: U4-m16.3-s12 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 type  Population density  Soil type  Relational database codes

Developed by: Host Updated: U4-m16.3-s13 Polygon data tables

Developed by: Host Updated: U4-m16.3-s14 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

Developed by: Host Updated: U4-m16.3-s15 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

Developed by: Host Updated: U4-m16.3-s16 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

Developed by: Host Updated: U4-m16.3-s17 Raster data  Pixels range in size over several orders of magnitude SatellitePixel size AVHRR1 km Landsat MSS80 m Landsat TM30 m QuickBird2.4 m QuickBird image of Erie Marsh, showing suspended sediment plumes m pixel resolution

Developed by: Host Updated: U4-m16.3-s18 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

Developed by: Host Updated: U4-m16.3-s19 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

Developed by: Host Updated: U4-m16.3-s20 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

Developed by: Host Updated: U4-m16.3-s21 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

Developed by: Host Updated: U4-m16.3-s22 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

Developed by: Host Updated: U4-m16.3-s23 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

Developed by: Host Updated: U4-m16.3-s24 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