GIS 202 - Introduction to Remote Sensing

Credits: 5
Variable Credit Course: No

Lecture Hours: 33
Lab Hours: 44
Worksite/Clinical Hours: 0
Other Hours (LIA/Internships): 0

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: GIS 102 with a C or higher.
Meets FQE Requirement: No
Integrative Experience Requirement: No

Student Learning Outcomes

List the principles behind various remotely sensed data types.
Utilize multispectral digital imagery available online, such as Landsat and Sentinal data.
Extract information from imagery, including drone-sourced data.
Perform analyses on digital elevation models and temporal GIS data.
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.
Utilize Python code for map automation tasks.
Examine in detail how classification of data affects visualization in a map and other cartographic tools.
Apply data transformation of GIS data related to coastal and inundation mapping.
Apply project management skills for the design and implementation of an independent mapping project.

Course Contents

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

Instructional Units: 6