Catalog Course Search Details

 Department:    GIS

 Course #:    202

 Credits:    5

 Variable:     No

 IUs:    6

 CIP:    450702

 EPC:    194

 REV:    2024

 Course Description  

Principles and conceptual overview of remote sensing instruments and how data and images are used to monitor and evaluate the condition and distribution of the earth's surface features.

 Prerequisite  

Prerequisite: GIS 102 with a "C" or higher.

Contact Hours (based on 11 week quarter)

Lecture: 33

Lab: 44

Other: 0

Systems: 0

Clinical: 0

Intent: Distribution Requirement(s) Status:  

Vocational Supplementary Elective  

Equivalencies At Other Institutions

Learning Outcomes

After completing this course, the student will be able to:

1. List the principles behind various remotely sensed data types.
2. Utilize multispectral digital imagery available online, such as Landsat and Sentinal data.
3. Extract information from imagery, including drone-sourced data.
4. Perform analyses on digital elevation models and temporal GIS data.
5. Perform various raster analysis techniques such as topographic and landcover classifications, relative elevation modeling, habitat modeling and change detection, including the use of deep learning (AI) tools.
6. Utilize Python code for map automation tasks.
7. Demonstrate in detail how classification of data affects visualization in a map and other cartographic tools.
8. Demonstrate data transformation of GIS data related to coastal and inundatation mapping.
9. Demonstrate project management skills for independent mapping project.

General Education Learning Values & Outcomes

Revised August 2008 and affects outlines for 2008 year 1 and later.

Course Contents

1. Introduction to remote sensing.
2. Using multispectral and drone imagery.
3. Raster analysis techniques; landcover and topographic classifications, relative elevation modeling, habitat modeling, and change detection, including use of deep learning (AI) tools.
4. Python coding for map automation.
5. Advanced cartographic concepts.
6. Vertical datum transformations.
7. Data and project management.