Lecture presentation - Forest Landscape Ecology Lab
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Transcript Lecture presentation - Forest Landscape Ecology Lab
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.