GEOG 481 - Topographic Mapping with Lidar
This is a sample syllabus.
This sample syllabus is a representative example of the information and materials included in this course. Information about course assignments, materials, and dates listed here is subject to change at any time. Definitive course details and materials will be available in the official course syllabus, in Canvas, when the course begins.
Overview
This is an introduction to the capabilities of lidar sensors and platforms, data processing systems, and derived digital data products. Students in this course will master the basic skills needed to leverage commercial lidar data sources and information products in a broad range of applications, including topographic mapping, flood inundation studies, vegetation analysis, and 3D modeling of urban infrastructure.
Lidar (Light Detection and Ranging) is an optical remote sensing technology that uses laser pulses to determine the distance between the sensor and a surface or object. In recent years, lidar has emerged as one of the most important sources of data for topographic mapping, vegetation analysis, and 3D modeling of urban infrastructure. Federal, state, and local government agencies are acquiring lidar data and derived products for use in floodplain mapping, transportation planning, and design, resource and environmental management, law enforcement, and emergency response. Much of this data is freely available to the public, and new uses for the data are emerging at a rapid pace. A thorough understanding of lidar technology and its application in GIS is part of the essential body of knowledge for today’s geospatial professional.
GEOG 481 cultivates students’ knowledge of the capabilities and limitations of lidar instruments and processing systems. The course also introduces fundamental concepts of accuracy assessment and appropriate use of lidar-derived data products. It helps students master the basic skills needed to leverage these data sources and information products in the context of application domains, such as topographic mapping, floodplain mapping, forestry, urban and regional planning, transportation systems design, and emergency response.
Throughout the course, students confront realistic problem scenarios that incorporate such skills and concepts as the definition of data needs, metadata content standards, data formats and types, analysis methods, and spatial accuracy requirements.
Objectives
Students who excel in this course are able to:
- summarize the basic operational characteristics of lidar instruments and platforms used for topographic mapping and geospatial applications;
- describe the basic principles of calibrating, georeferencing, and processing of lidar data;
- describe quantitative and qualitative methods used in industry standards for quality assurance and accuracy assessment of lidar-derived data products;
- critically assess the strengths and weaknesses of various lidar platforms and instruments for a broad range of application scenarios;
- apply acquired knowledge and critical thinking skills to solve a real-world problem with appropriate lidar data processing and analysis methods.
Required Materials
The materials listed here represent those that may be included in this course. Students will find a definitive list in the course syllabus, in Canvas, when the course begins.
Required Text
Renslow, Michael, ed. 2012. Airborne Topographic Lidar Manual. Bethesda, MD. American Society for Photogrammetry and Remote Sensing. ISBN 1-57083-097-5.
Required Software
- ArcGIS, Esri. All students in the Online Geospatial Program receive a student license of ArcGIS valid for one year.
- QCoherent, LP360
- 7-Zip. 7-Zip can be downloaded for free.
- Screen Capture Utility. Students are free to use any screen capture software of their choosing.
Prerequisites
GEOG 480: Exploring Imagery and Elevation Data in GIS Applications (or equivalent professional experience). It is expected that students are conversant in fundamental concepts of GIS and have hands-on experience with ArcGIS Pro. The following bullets are examples of knowledge and skills you should have before starting this course.
- explain the concept of map scale
- explain the concept of a map projection
- describe the difference between a vector and a raster data set
- explain the difference between an Esri SHP file and a feature class
- explain the difference between a 2D and 3D SHP file or feature class
- manage GIS data files in the Esri interface
- access data management, data conversion, and data analysis tools in the Esri interface
- add a vector data layer to a project file
- add a raster data layer to a project file
- create a new SHP file or feature class
- edit a SHP file or feature class using the Editor toolbar
- change symbols for a SHP file or feature dataset using Symbology Properties
- view and edit the attribute table for a SHP file, feature class, or raster layer
Expectations
We have worked hard to make this the most effective and convenient educational experience possible. How much and how well you learn is dependent on your attitude, diligence, and willingness to ask for clarifications or help when you need them. We are here to help you succeed. Please keep up with the class schedule and take advantage of opportunities to communicate with us and with your fellow students. You can expect to spend an average of 12 – 15 hours per week on class work.
Major Assignments
Students earn grades that reflect the extent to which they achieve the learning objectives listed above. Opportunities to demonstrate learning include:
- 7 online quizzes (17% of grade)
- 8 hands-on laboratory activities (45% of grade)
- 6 discussion/survey activities (4% of grade)
- 6 final project building activities (31% of grade)
Course Schedule
| Week | Topic | Assignment |
|---|---|---|
| 0 | Orientation |
|
| 1 | Lidar Sensors and Data |
|
| 2 | Lidar Systems and Calibration |
|
| 3 | Lidar Data Processing, Part 1 |
|
| 4 | Lidar Data Processing, Part 2 |
|
| 5 | Accuracy Assessment and Quality Control |
|
| 6 | Topographic Mapping |
|
| 7 | Lidar Applications |
|
| 8 - 10 | Final Project: Leveraging Lidar Data to Confront Contemporary Challenges in Geospatial Analysis |
|