- Spring 2001 -

| Instructor: |
|
|
|
Dept. of Forest Sciences, SALRM University of Alaska Fairbanks |
||
| Teaching Assistant: |
|
Course Objectives:
1) To build upon GIS concepts covered
in NRM 338 (or similar course) in order to develop a broader understanding
of the types of questions and objectives that GIS can address.
"What is it good for and why do I care?"
2) To provide hands-on experience
in conceiving, structuring and performing such analyses with various types
of GIS data.
"O.K., so show me some examples of how to get
it done."
Hardware/Software:
We'll be using ARC/INFO and ArcView on WindowsN/T. While experience with ARC/INFO and ArcView is not required (though helpful), general computer literacy is required.
What the course will and won't do . . .
The fall term GIS class, NRM338, teaches students how to create base information within a GIS. This means taking information from a variety of sources - field sampling, paper maps, GPS coordinates etc etc and putting them into a computer-based GIS. NRM338 teaches concepts of how information about the world is translated into a GIS format, and provides hands on experience doing it. The spring term class, NRM341, teaches students how to do things with that information once it is already in a GIS format. While NRM341 will review concepts of data structure covered in NRM338, it will not re-teach the process of creating base data. NRM341 will cover concepts, strategies and methods of taking base information from a variety of sources and combining and analyzing them to ask questions about the landscape and provide meaningful answers. Since we will be working with base data that already exists, it will not be directly necessary for students of NRM341 to have taken NRM338.
Basically, learning GIS is a matter of learning the fundamental concepts up front and becoming familiar with how the data and software work. Once that's done, you are prepared to get good at it by trial and error and experimentation over time. It is typical for a GIS "guru" to have learned the basics by taking classes, and then learning the rest on the job. With every new project you learn new things. These classes are designed to get you to the point where you have learned the basics, and are ready to jump into the process of trial-and-error learning on your own.
Lectures: Tuesday/Thursday, 9:45-11:15 Natural Sciences 165
Lab: Tuesday 2-5PM OR Wednesday 6-9PM ONeill 330
Office Hours: Anytime I'm in my office, or by e-mail appointment . . .
Grading:
Based on total points from the following:
4 quizzes 100 points
4 reading assignments 80 points
13 lab assignments 260 points
2 on-line exams 200 points
Total possible points 640. The final grades
will be curved based on total points earned
in the course. To emphasize understanding,
and not memorization, all exams will be
open book.
Spring 2001 Schedule:
|
|
|
|
| Week 1 January 18 | Introduction | none |
| Week 2 January 23 - 25 | Case Studies - GIS in Research | Lab 1
- Looking at Data &
Making Maps |
| Week 3
January 30 - February 1 |
Data Structure
/ Polygon Analysis
reading assigment 1, due 2/1/01
|
Lab 2
- ARC/INFO Intro &
Polygon Analysis |
| Week 4 February 6 - 8 | Grids /
Map Projections
GridAnalaysis |
Lab 3 - Grid Analysis 'Dammit' |
| Week 5 February 13 - 15 | Continuous
Surface Model/
Types of Analyses reading assignment
2, due
|
Lab 4
- Continuous Surface
Model - Critical Habitat Watershed |
| Week 6 February 20 - 22 | Analysis
in ArcView/
Structuring an Analysis quiz 2, 2/22/01 |
Lab 5
- Continuous Surface
Model - Critical Habitat Watershed (ArcView method) |
| Week 7
February 27 - March 1 |
Structuring
an Analysis cont./
Intro to making an Effective Map/ Exam Review |
Lab 6
- Three New Tricks &
Practice Exam 1 |
| Week 8 March 6 - 8 | EXAM 1 | EXAM 1 |
| Week 9 March 13 - 15 | Spring Break | Spring Break |
| Week 10 March 20 - 22 | AMLs
quiz 3, 3/22/01 |
Lab 7
- AML &
Changing Map Projections |
| Week 11 March 27 - 29 | Intro. to Remote Sensing I | Lab 8 - Supervised Classification |
| Week 12 April 3 - 5 | Intro. to
Remote Sensing II
Guest Lecture: Scott Rupp
reading assignment
3, due
|
Lab 9 - Supervised Classifications: Evaluation & Analysis |
| Week 14 April 10 - 12 | Regions
Guest Lecture: Will Putman
|
Lab 10 - Regions: Forest Analyis Using Regions and Supervised Classifications |
| Week 15 April 17 - 19 | Points - Telemetric Data
Guest Lecture: Brad Griffith
reading assignment
4, due
|
Lab 11 - Analysis with telemetric point data |
| Week 16 April 24 - 26 | Dynamic
Segmentation/Network
quiz 4, 4/26/01 |
Lab 12 - Dynamic Segmentation |
| Week 17 May 1 - 3 | Review/Summary/Discussion | Lab 13 - Practice Exam 2 |
| Week 18 May 7 - 10 | EXAM 2 | EXAM 2 |