The Joy of GRID: Geomorphology and Hydrology in GIS
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Transcript of The Joy of GRID: Geomorphology and Hydrology in GIS
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The Joy of GRID:Geomorphology and Hydrology in GIS
Finn Krogstad
UW Forest Engineering
http://students.washington.edu
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Consider Sediment Routing
x0,z0
zx
z
x
dt
dVi 1
dt
dVidt
dz
x4,z4x3,z3
x2,z2x1,z1
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Times Change
• Spatial problems used to require lots of programming.
• With modern spreadsheets, we could assign it as an undergraduate homework problem.
• GRID offers the same spreadsheet simplicity and functionality,but handles spatial issues for you.
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OUTLINE
A. GRID BASICS1. GIS Data2. Thinking in GRID3. Programming
B. HYDROLOGIC PROCESSES1. Local2. Watershed
C. ANALYSIS1. Classification 2. Regression
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GRID BASICS - GIS Data
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GRID BASICS - GIS Data
• Points
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GRID BASICS - GIS Data
• Points
• Arcs
• Polygons
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GRID BASICS - GIS Data
• Points
• Arcs
• Polygons
• Attribute Tables
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GRID BASICS - GIS Data
• Points
• Arcs
• Polygons
• Attribute Tables
• Data Sources
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GRID BASICS - Thinking in GRID
• GRID-ing the World
continuous
discrete
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GRID BASICS - Thinking in GRID
• GRID-ing the World
• Grid Algebra
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GRID BASICS - Thinking in GRID
• GRID-ing the World
• Grid Algebra
• Spatial Spreadsheet
- not mysterious
- intuitiveness
- flexible
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GRID BASICS - Programming
• Command Line– just like you type it
• Flow Control– if, do, while
• User Interface– for GIS novices, e.g. SEDMODL
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Hydrologic Processes• Local
– Slope, Aspect, Curvature
Z = Ax2y2 + Bx2y + Cxy2 + Dx2 + Ey2 + Fxy + Gx + Hy + I
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Hydrologic Processes• Local
– Slope, Aspect, Curvature– Hillshade
• Display Topography
• Radiant Energy
• Other things
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Hydrologic Processes• Local
– Slope, Aspect, Curvature– Hillshade
• Watershed
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Hydrologic Processes• Local
• Watershed– Flow direction
• Lowest Neighbor
• Gradient
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Hydrologic Processes• Local
• Watershed– Flow direction– Flow accumulation
• Upslope Area
• Streams
• Watersheds
• Variable Inputs
• Cumulative Impact
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Hydrologic Processes• Local
• Watershed– Flow direction– Flow accumulation– Flow length
• distance to stream
• transport ‘friction’
• delivery to streams
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Multivariate Analysis
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Multivariate Analysis
• ClusteringClustering
‘True’ color Bands 1,4,7
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Scatter Plots
• ClusteringClustering
image Scatter-plots
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Cluster Training
• ClusteringClustering
Image Stand cover
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Cluster Training
• ClusteringClustering
Image Stream cover
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Cluster Training
• ClusteringClustering
Image Water bodies
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Image Classification
Image Classification
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Classification vs. End Member
Classification - We can classify a cell according to which class gives a higher likelihood.
End Member - The fraction of each end member can be approximated by saving the normalized likelihoods.
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Multivariate Analysis
• Clustering
• Regression• Linear
Ey = a0+ a1x1 + a2x2 + a3x3 + ....
E(precip) = a0+ a1longitude + a2elevation
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Multivariate Analysis
• Clustering
• Regression• Linear
Ey = a0+ a1x1 + a2x2 + a3x3 + .…
• Logistic
Ey=1/(1+(exp(-(a0+alxl+a2x2+a3x3+...)))
L-0.0018M-0.0026H-0.0037
LandslideProbability
E(LS)=1/(1+(exp(-(a0+alSMORPH)))
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Conclusions• GRID should be used like Excel
• Get yourself a wonk
• Keep up on data sources
• Use models to predict results
• Use observations to improve models
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InstructorsFinn Krogstad
Peter Schiess
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ScheduleLecture:
Tuesday, 9:30-11:20, in BLD 261
Lab:
Thursday, 9:30-11:20, in BLD 261
move?
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ReadingsCell-based Modeling with Grid
Assigned readings to follow
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GradingFE423:
50% labs, 50% exam
FE523:
33% labs, 33% exam, 33% project
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Final Exam10:30-12:20 p.m. Wednesday, Mar. 15, 2000
open books, open notes, pencil-and-paper solution/discussion of several problems.
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LabsPost lab reports on their web site
Grading will be based on communication
Finished and posted one week after assigned.
Late work will be accepted with half the points deduced for each week it is late.
Revise and resubmit each lab.
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FE523 ProjectA course related project of your choosing.
1/20 Proposal
2/24 Progress Report
3/15 Final Report