The Joy of GRID: Geomorphology and Hydrology in GIS Finn Krogstad UW Forest Engineering

Consider Sediment Routing x 0,z 0 zz x x 4,z 4 x 3,z 3 x 2,z 2 x 1,z 1

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

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 = Ax 2 y 2 + Bx 2 y + Cxy 2 + Dx 2 + Ey 2 + 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

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 = a 0 + a 1 x 1 + a 2 x 2 + a 3 x E(precip) = a 0 + a 1 longitude + a 2 elevation

Multivariate Analysis Clustering Regression Linear Ey = a 0 + a 1 x 1 + a 2 x 2 + a 3 x 3 +.… Logistic Ey=1/(1+(exp(-(a 0 +a l x l +a 2 x 2 +a 3 x ))) L M H Landslide Probability E(LS)=1/(1+(exp(-(a 0 +a l SMORPH)))

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