The Joy of GRID: Geomorphology and Hydrology in GIS

The Joy of GRID:Geomorphology and Hydrology in GIS Finn Krogstad UW Forest Engineering http://students.washington.edu

x x0,z0 x1,z1 x2,z2 x4,z4 x3,z3 z Consider Sediment Routing

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

continuous discrete GRID BASICS - Thinking in GRID • GRID-ing the World

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 • Clustering Bands 1,4,7 ‘True’ color

Scatter Plots • Clustering image Scatter-plots

Stand cover Cluster Training • Clustering Image

Stream cover Cluster Training • Clustering Image

Water bodies Cluster Training • Clustering Image

Classification Image Classification Image

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.

E(precip) = a0+ a1longitude + a2elevation Multivariate Analysis • Clustering • Regression • Linear Ey = a0+ a1x1 + a2x2 + a3x3 + ....

Landslide Probability L-0.0018 M-0.0026 H-0.0037 Multivariate Analysis • Clustering • Regression • Linear Ey = a0+ a1x1 + a2x2 + a3x3 + .… • Logistic Ey=1/(1+(exp(-(a0+alxl+a2x2+a3x3+...))) 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

Instructors Finn Krogstad Peter Schiess

Schedule Lecture: Tuesday, 9:30-11:20, in BLD 261 Lab: Thursday, 9:30-11:20, in BLD 261 move?

Readings Cell-based Modeling with Grid Assigned readings to follow

Grading FE423: 50% labs, 50% exam FE523: 33% labs, 33% exam, 33% project

Final Exam 10:30-12:20 p.m. Wednesday, Mar. 15, 2000 open books, open notes, pencil-and-paper solution/discussion of several problems.

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.

FE523 Project A course related project of your choosing. 1/20 Proposal 2/24 Progress Report 3/15 Final Report

The Joy of GRID: Geomorphology and Hydrology in GIS