The Joy of GRID: Geomorphology and Hydrology in GIS

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The Joy of GRIDGeomorphology and Hydrology in
GIS

• Finn Krogstad
• UW Forest Engineering
• http//students.washington.edu

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Consider Sediment Routing
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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 BASICS
• 1. GIS Data
• 2. Thinking in GRID
• 3. Programming
• B. HYDROLOGIC PROCESSES
• 1. Local
• 2. Watershed
• C. ANALYSIS
• 1. 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

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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

• Clustering

Bands 1,4,7
True color
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Scatter Plots

• Clustering

image
Scatter-plots
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Cluster Training

• Clustering

Image
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Cluster Training

• Clustering

Image
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Cluster Training

• Clustering

Image
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Image Classification
Image
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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 ....

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Multivariate Analysis

• Clustering
• Regression

• Linear
• Ey a0 a1x1 a2x2 a3x3 .

• Logistic
• Ey1/(1(exp(-(a0alxla2x2a3x3...)))

E(LS)1/(1(exp(-(a0alSMORPH)))
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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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Instructors

• Finn Krogstad
• Peter Schiess

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Schedule

• Lecture
• Tuesday, 930-1120, in BLD 261
• Lab
• Thursday, 930-1120, in BLD 261
• move?

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Readings

• Cell-based Modeling with Grid
• Assigned readings to follow

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Grading

• FE423
• 50 labs, 50 exam
• FE523
• 33 labs, 33 exam, 33 project

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Final Exam

• 1030-1220 p.m. Wednesday, Mar. 15, 2000
• open books, open notes, pencil-and-paper
  solution/discussion of several problems.

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Labs

• Post 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 Project

• A course related project of your choosing.
• 1/20 Proposal
• 2/24 Progress Report
• 3/15 Final Report