GIS and Landscapes
Lisa A. Schulte
Forest Ecology and Management
Vegetation y
Distribution
Climate
x1
Soils
x2
Topography
x3
Model
= f(c, s, t)
Probability of
What are geographical/spatial data?
What are geographical/spatial data?
 Any data that can be mapped
 Have x- and y-coordinate
Types of spatial data?
Types of spatial data?

Topographic maps

Hydrographic maps

Political/Administrative/Property boundaries

Road networks

Remote Sensing (aerial photography, satellite)

Data on people: census data, land use, marketing
surveys

Data on natural resources: climate, geology,
hydrology, soil, natural hazards, biological activity

Data on utilities
Why use GIS?
Limitations of a map:

2-D representation of 3-D

Limited to a single scale

Snapshot in time

Difficult to manipulate data
GIS Overcomes
These!!!
What is a GIS?

A set of computer tools for collecting, storing,
retrieving, transforming, and displaying spatial data
from the real world (Burrough and McDonnell 1998).

Many functions = may parts.
Scanner
Network
Digitizing
Table
Computer
Screen
CD
CD
Printer
FTP
Core parts of a GIS:
1)
User interface/GIS tools
• Responsible for capturing, storing, retrieving,
displaying, customizing, and sharing data
2)
Spatial Database
• Responsible for storing and querying data
Scanner
Network
Digitizing
Table
Computer
CD
Spatial
Database
Printer
CD
FTP
Screen
How do we represent the real world
digitally?
Physical
reality
Actual phenomena:
-Properties
-Connections
Real world
model
Entity:
-Type
-Attributes
-Relationships
From: Bernhardsen 1999
Data
model
Object:
-Type
-Attributes
-Relationships
-Geometry
-Quality
Database
Object:
-Type
-Attributes
-Relationships
-Geometry
-Quality
Maps/
reports
Spatial Data Components
Spatial Data
Geometric
Component
Point
Line
Area (polygon/cell)
Attribute
Component
Qualitative
Quantitative
Categorical
Ordinal
Interval
Ratio
Spatial Data Components
Spatial Data
Geometric
Component
Point
Line
Area (polygon/cell)
Attribute
Component
Qualitative
Quantitative
Categorical
Ordinal
Interval
Ratio
Geometric Representation
Point: 0-D object that specifies geometric location
specified through a set of coordinates.
Line segment (vector): 1-D object that is a direct line
between 2 endpoints.
String: a sequence of line segments.
Polygon: 2-D object bounded by at least 3 1-D line
segments.
Raster cell/pixel: 2-D that represents an element of
regular tesselation of a surface.
Vector Data Model
Raster Data Model
Raster Data Model
TIN Data Model
TIN
Raster
Data Model
Vector vs. Raster

Very important choice!

Advantages of vector:
•
Good representation of entity data models
•
Space efficient storage of data
•
Topology can be described explicitly and be
easily manipulated
•

Efficient query operation
Advantages of raster:
•
Simple data structure
•
Efficient representation of highly variable data
•
Mathematical modeling easier because all
entities have simple, regular shape
Georeferencing:

Matching up spatial database with earth coordinate
system

Coordinate systems
• Latitude/Longitude – distortion near poles
• Universal Transverse Mercator
– divide globe up into strips
– good for large datasets
• State Plane
– each state has own
– most accurate for at this scale
How do we represent the real world
digitally?

Selecting applicable scale

Through simplification!

Two basic components associated with spatial data:
1. Geometric component
2. Attribute component
Data Model
Classification
Who produces spatial data?
Who produces spatial data?

National agencies (USGS, USFS, NOAA, DNR)

Military organizations

Remote sensing companies (aerial photography, satellite)

Utility companies

Climatologists, geologists, hydrologists, ecologists,
geographers, oceanographers, etc.

Grad students!
Data Acquisition:
Scanner
Network
Digitizing
Table
Computer
Screen
CD
CD
Printer
FTP
Data Acquisition:

Field surveys

Digitizing

•
Trace lines on map
•
Labor intensive
Scanning
•
Scan map
•
Edit data

Remote sensing

Deriving from existing GIS data layers

Downloading
Web Sources of GIS Data:




USGS
•
Remotely sensed, DEMs, Soils, Hydrographies
•
http://www.usgs.gov
NOAA - National Climatic Data Center
•
Climate
•
http://www.ncdc.noaa.gov/ol/about/ncdcnoaa.html
US Census Bureau
•
Demographic
•
http://www.census.gov/geo/tigerline/tl_1998.html
Wisconsin State Cartographer’s Office – Wisconsin Land
Information Clearinghouse
•
Various
•
http://wisclinc.state.wi.us/
GIS software:
Scanner
Network
Digitizing
Table
Computer
Screen
CD
CD
Printer
FTP
GIS software:

Arc/Info
•

ArcView
•

Clark Labs (http://www.clarklabs.org/)
GRASS
•

ESRI (http://www.esri.com/)
IDRISI
•

ESRI (http://www.esri.com/)
Baylor University (http://www.baylor.edu/~grass/)
Imagine
•
ERDAS (http://www.erdas.com/products/product.html)
GIS functionality

Spatial queries
• Site analysis
• Trend analysis
• Pattern analysis

Spatial overlay

Spatial modeling

Network operations

Interpolation

Digital terrain analysis

Statistical analysis
Who uses spatial data?
Who uses spatial data?

Agriculture

Archaeology

Demographers

Environmental scientists and managers

Epidemiology and health scientists

Emergency services

Land planners

Marketing agencies

Naviation

Real estate

Tourism

Utilities
Uncertainty…
From: Lunetta et al. 1991
Spatial data in landscape ecology…
Resolution?
Data model?
Attribute representation?
Trustworthiness?
From: Bernhardsen 1999
Nine factors to consider when embarking
on spatial analysis with GIS:
1.
Real world phenomena simple/complex?
2.
Data used to describe real world phenomena detailed/generalized?
3.
What data types are used to describe the phenomena?
4.
Can phenomena be represented in a database exactly/vaguely?
5.
Do database entities represent discrete/continuous real world entities?
6.
Were the attributes of database entities obtained by complete
enumeration or by sampling?
7.
Will the database be used for descriptive/administrative/analytical
purposes?
8.
Will the database be used to make inferences about the real world?
9.
Is the process under consideration static/dynamic?
(Burroughs and MacDonnell 1998)
References

Bernhardtsen, T. 1999. Geographic information systems: an
introduction, 2nd edition. John Wiley and Sons, New York, New
York, USA.

Burrough, P. A., and R. A. McDonnell. 1998. Principles of
geographic information systems. Oxford University Press, Inc.,
New York, New York, USA.

Johnston, C.A. 1998. Geographic information systems in
ecology. Blackwell Science, Oxford, UK.

Lunetta, R.S., R.G. Congalton, L.K. Fenstermaker, J.R. Jensen,
K.C. McGwire, and L.R. Tinney. 1991. Remote sensing and
geographic information system data integration: error sources
and research issues. Photogrammetric Engineering and
Remote Sensing 57:677-687.