The Joy of GRID:Geomorphology and Hydrology in GIS

Finn Krogstad

UW Forest Engineering

http://students.washington.edu

Consider Sediment Routing

x0,z0

zx

z

x

dt

dVi 1

dt

dVidt

dz

x4,z4x3,z3

x2,z2x1,z1

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.

OUTLINE

A. GRID BASICS1. GIS Data2. Thinking in GRID3. Programming

B. HYDROLOGIC PROCESSES1. Local2. Watershed

C. ANALYSIS1. Classification 2. Regression

GRID BASICS - GIS Data

GRID BASICS - GIS Data

• Points

GRID BASICS - GIS Data

• Points

• Arcs

• Polygons

GRID BASICS - GIS Data

• Points

• Arcs

• Polygons

• Attribute Tables

GRID BASICS - GIS Data

• Points

• Arcs

• Polygons

• Attribute Tables

• Data Sources

GRID BASICS - Thinking in GRID

• GRID-ing the World

continuous

discrete

GRID BASICS - Thinking in GRID

• GRID-ing the World

• Grid Algebra

GRID BASICS - Thinking in GRID

• GRID-ing the World

• Grid Algebra

• Spatial Spreadsheet

- not mysterious

- intuitiveness

- flexible

GRID BASICS - Programming

• Command Line– just like you type it

• Flow Control– if, do, while

• User Interface– for GIS novices, e.g. SEDMODL

Hydrologic Processes• Local

– Slope, Aspect, Curvature

Z = Ax2y2 + Bx2y + Cxy2 + Dx2 + Ey2 + Fxy + Gx + Hy + I

Hydrologic Processes• Local

– Slope, Aspect, Curvature– Hillshade

• Display Topography

• Radiant Energy

• Other things

Hydrologic Processes• Local

– Slope, Aspect, Curvature– Hillshade

• Watershed

Hydrologic Processes• Local

• Watershed– Flow direction

• Lowest Neighbor

• Gradient

Hydrologic Processes• Local

• Watershed– Flow direction– Flow accumulation

• Upslope Area

• Streams

• Watersheds

• Variable Inputs

• Cumulative Impact

Hydrologic Processes• Local

• Watershed– Flow direction– Flow accumulation– Flow length

• distance to stream

• transport ‘friction’

• delivery to streams

Multivariate Analysis

Multivariate Analysis

• ClusteringClustering

‘True’ color Bands 1,4,7

Scatter Plots

• ClusteringClustering

image Scatter-plots

Cluster Training

• ClusteringClustering

Image Stand cover

Cluster Training

• ClusteringClustering

Image Stream cover

Cluster Training

• ClusteringClustering

Image Water bodies

Image Classification

Image Classification

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.

Multivariate Analysis

• Clustering

• Regression• Linear

Ey = a0+ a1x1 + a2x2 + a3x3 + ....

E(precip) = a0+ a1longitude + a2elevation

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)))

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

InstructorsFinn Krogstad

Peter Schiess

ScheduleLecture:

Tuesday, 9:30-11:20, in BLD 261

Lab:

Thursday, 9:30-11:20, in BLD 261

move?

ReadingsCell-based Modeling with Grid

Assigned readings to follow

GradingFE423:

50% labs, 50% exam

FE523:

33% labs, 33% exam, 33% project

Final Exam10:30-12:20 p.m. Wednesday, Mar. 15, 2000

open books, open notes, pencil-and-paper solution/discussion of several problems.

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.

FE523 ProjectA course related project of your choosing.

1/20 Proposal

2/24 Progress Report

3/15 Final Report