Transcript GIS and Chemical Oceanographic Research Miles Logsdon mlog

Spatial and Physical Models
Related to
Processes across the Landscape
Miles Logsdon
[email protected]
OR
How will GIS and RS help in Salmon Modeling
OR
Why is Miles Talking about Fish?
“Our” agenda
What is GIS?
What is the difference between a Spatial model
and a Spatial Explicit Model
What is a theoretical basis for the application of
GIS and spatial data analysis in modeling?
What model “methods” or “tools” directly apply to
Landscape processes?
Questions
For Miles:
How can these central landscape features be described
and linked to a fish-habitat model? - with a lot of work
What are the 2 (or 3 or 4) biggest sources of uncertainty
in making predictions about how Spatial Data Analysis
affects salmon - me
What 2 (or 3 or 4) alternative scenarios of current or
future conditions would you suggest should be explored
to make our model predictions about the effects of
habitat change on salmon more robust to uncertainties?
– full funding of my research.
See final slide for more complete responses
My agenda
Show you pretty pictures
Talk about “stuff” I enjoy
Justify Spatial analysis
as a field of study
Spatial Information
Technologies
GIS - GPS – Remote Sensing
http://boto.ocean.washington.edu/oc_gis_rs
Spatial Information Technologies
Geographic Information Systems – GIS
Global Positioning System – GPS
Remote Sensing and Image Processing - RS
Technologies to help answer:
What is “here”? … give a position
What is “next” to “this”? … given some description
Where are all of the “???” … detecting or finding
What is the spatial pattern of “???”
When “X” occurs here, does “Y” also occur?
GIS
Geographic Information System
GIS - A system of hardware, software, data, people,
organizations and institutional arrangements for collecting,
storing, analyzing, and disseminating information about areas
of the earth. (Dueker and Kjerne 1989, pp. 7-8)
GIS - The organized activity by which people
•Measure aspects of geographic phenomena and processes;
•Represent these measurements, usually in a computer database;
•Operate upon these representations; and
•Transform these representations. (Adapted from Chrisman, 1997)
A KEY POINT: Geo-referenced Data
RS: Remote Sensing
Remote Sensing is a technology for sampling radiation and force
fields to acquire and interpret geospatial data to develop
information about features, objects, and classes on Earth's land
surface, oceans, and atmosphere (and, where applicable, on the
exterior's of other bodies in the solar system).
Remote Sensing is detecting and measuring of electromagnetic
energy (usually photons) emanating from distant objects made of
various materials, so that we can identify and categorize these
object by class or type, substance, and spatial distribution.
Suggested Reading
Chrisman, Nicholas, 1997, “Exploring Geographic Information Systems”, John Wiley & Sons,
Burrough, P. A., 1986, “Principles of Geographical Information Systems for Land Resources
Assessment”, Monographs on Soil and Resources Servey #12, Oxford Science Publications
Miller, Roberta Balstad, 1996, "Information Technology for Public Policy", in GIS and
Environmental Modeling: Progress and Research Issues, editors, Michael F. Goodchild, Louis T.
Steyaert, Bradley O. Parks, Carol Johnston, David Maidment, Michael Crane, and Sandi
Glendinning, GIS World Books.
Goodchild, Michael F., "The Spatial Data Infrastructure of Environmental Modeling", in GIS and
Environmental Modeling: (see above).
Faber, G. Brenda, William W. Wallace, Raymond M. P. Miller, "Collaborative Modeling for
Environmental Decision Making", proceedings of the GIS'96 Tenth Annual Symposium on
Geographic Information Systems, Vancouver, B.C., March 1996.
The larger context
(Chrisman, 1997)
Integrated
System
Model
System
Models
Process
Models
PRISM
MM5 DHSVM POM CRYSTAL UrbanSim
Slope Satiability
Land Cover Change
Population Growth
…….
……..
Data
Models
Soil Texture
Geology
Elevation
Stream Network
Temperature
Rainfall
Evapotranporation
Water Supply & Demand
.ETC
…….
…..
Population
Land Cover
Water usage
Stream Flow
Salinity
land ownership
Integrated
System
Model
Integrated
System
Model
Integrated
System
Model
System
Models
System
Models
System
Models
Process
Models
Process
Models
Process
Models
Data
Models
Data
Models
elevation
wind
elevation
wind
landcover
landcover
TIME – Understanding?
Data
Models
elevation
wind
landcover
Biophysical Data Layers
Land Use
Energy Balance
Soils
Temperature
Precipitation
Vegetation
Concept
of Spatial
Objects
POINT
0 0
0
0 1 0
0 0 0
5 5 3
AREA
Raster Data Encoding
LINE
1 0 0
0 1 0
0 0 1
POINTS
LINES
AREA
Vector Data Encoding
1 3 3
1 1 2
VECTOR Data Model
Data Relationships are invariant
to translation and rotation
Vector - Topology
Descriptive
Spatial
Object
VAR1 VAR2
1
2
3
1
x1,y1
x2,y2
x3,y3
1
2
3
Fnode Tnode x1y1, x2y2
3
2
2
1
2
153
1 124
10
5
11
1
2
1
2
2
3
xxyy, xxyy
xxyy,xxyy
VAR1 VAR2
1
2
VAR1 VAR2
1
2
10, 11, 12, 15
10, …….
1
2
RASTER Data Model
Raster Topology
Map Algebra
In a raster GIS, cartographic modeling
is also named Map Algebra.
Mathematical combinations of raster layers
several types of functions:
• Local functions – do not consult the 8 neighbors
• Focal functions – function on the “kernel” of neighboring cells
• Zonal functions – function on cells that test true in a different layer
• Global functions – based upon the distribution of “all” cells
Functions can be applied to one or multiple layers
Focal Function: Examples
•Focal Sum (sum all values in a neighborhood)
2
2
0
3
1
0
1
4
2
1
1 2
2 3
3 2
(3x3)
=
12 13
17 19
•Focal Mean (moving average all values in a neighborhood)
2
2
0
3
1
0
1
4
4
2
2
3
1 1
3 2
(3x3)
=
1.8 1.3 1.5 1.5
2.2 2.0 1.8 1.8
2.2 2.0 2.2 2.3
2.0 2.2 2.3 2.5
Digital Elevation Model – Raster Data Model
Thanks to David Maidment: http://www.ce.utexas.edu/prof/maidment
D8 – Determine the Direction of flow
Assign a value to indicate the direction
of flow. Then for each cell determine
the number of cell “upstream”
”
Set a threshold for the minimum
value of flow accumulation which
defines a stream
Data Modeling Issues for hydrology
Spatial and temporal scale
Irrigation
Diversions
Impoundment
Urban water use
Other urbanization effects
Temporal Averaging:
Example: 1-month rainfall
Evaporation and discharge modeled as functions
of soil moisture content
How to handle long-interval (1-month) RF?
Constant (drizzle) or One Big Event
Drizzle: ET too high, Discharge too low
Big Event: ET too low, Discharge too high
Urbanization Effects
Water Use: How much outdoor use?
Waster Water: How disposed?
Urban Hydrology
Reduced infiltration
Concentration of water
Reduced ET
Satellite Remote
Sensing
June 27, 2001
Remote Sensing
in brief
Thanks to Robin Weeks
The “PIXEL”
Ground Truth
Classified Product
DOES PATTERN MATTER?
Evaluating the Impact of Landscape Pattern on
Watershed Hydrology
Urban I (10-30% developed)
Urban II (30-60%)
Urban III (> 60%)
Short Grass
Tall grass
Crop/mixed
Irrigated Crop
Mixed Woodland
Bog or Marsh
Evergreen Shrub
Coniferous I
Coniferous II
Coniferous III
Coniferous IV
Deciduous Broadleaf
Non-forested (Altered-unknown)
Non-forested (Altered-shrub)
Ice cap / Glacier
Water
Prism ‘98 Classified
Landcover Snoqualmie
Drainage Basin
4 landscapes with different patterns
Classified
“real”
Random
Same composition
Patchy
12% more
Forests
Patches
Smooth
12% less
Forests
Patches
Patchy – 1998
more water
Random - 1998
Smooth – 1998
Less water
Accumulated Sum Difference (1990 – 1991):
The Difference in the total amount of water flowing past
the mouth of the basin between the “real” landscape
(1998 classified) and the “simulated”pattern – Random,
Patchy, and Smooth
A 12% change in the forest
composition, impacts the
total accumulated flow to a
greater degree then does a
change in the pattern of the
landscape with the same
composition.
Change in Landcover
Through an Increase in
Impervious Surfaces
1991
LANDCOVER CHANGE
1998
Maplewood Creek – an Urban Watershed
Maplewood Creek, of the Lower Cedar River
140
cfs (91)
cfs (98)
cfs (Hist)
120
1998
1991
Discharge (cfs)
100
Historical
80
60
Assuming the same rainfall record
we experienced between 1989 –
1991, The amount of Discharge at
peak flow increased ~67% over
historical conditions, and ~11%
between 1991 - 1998
40
20
0
0
20
40
60
Recurrence interval (years)
80
100
Spatial Data Analysis
SUMMARY POINTS
The accurate description of data related to a process
operating in space, the exploration of patterns and
relationships in such data, and the search for
explanation of such patterns and relationships
Spatial Analysis vs. Spatial Data Analysis
Spatial Analysis = what is here, and where are all the X’s ???
Spatial Data Analysis = observation data for a process operating in
space and methods are used to describe or explain the behavior, and/or
relationship with other phenomena.
Questions
For Miles:
How can these central landscape features be described and
linked to a fish-habitat model? – spatially explicit definition of
objects and processes that are consistent with spatially reference
data models
What are the 2 (or 3 or 4) biggest sources of uncertainty in
making predictions about how Spatial Data Analysis affects
salmon – data definition and/or data resolution
What 2 (or 3 or 4) alternative scenarios of current or future
conditions would you suggest should be explored to make our
model predictions about the effects of habitat change on salmon
more robust to uncertainties? – Does Pattern Matter? Does a change in
configuration of landcover produce a change in function of the landscape for a
give process.