Fall 2024 - ENVS 473: Remote Sensing in GIS

Course Instructor: Dr. Bingqing Liu, Assistant Professor, School of Geosciences

Office Number: Hamilton Hall 335

E-Mail: bingqing.liu@louisiana.edu

Course Time:  Mondays and Wednesdays, 10:00-11:15 am

Location: Hamilton Hall 113 (passcode: 329113)

Office Hours: Mondays, Wednesdays 1:15-2:15 pm, or arrange an appointment by email.

Description

This course is designed to provide a foundational understanding of remote sensing techniques and instrumentation in environmental sciences. It will cover the physical principles of remote sensing, including an exploration of the characteristics of electromagnetic radiation and its interactions with the natural environment. Both active and passive techniques will be explored.

The course also includes computer laboratory components. A significant focus of the laboratory work will be on the practical application of Python in processing and interpreting remotely sensed data, such as active remote sensing (e.g., RADAR from Sentinel-1 and LIDAR from ICESat-2) and passive remote sensing data, such as, those popular multispectral remote sensing data (e.g., Sentinel-2 MSI), more advanced hyperspectral remote sensing data (e.g., PACE and EMIT), as well as thermal remote sensing data (e.g., ECOSTRESS). Throughout the course, students will participate in hands-on labs and final projects that reinforce the concepts learned in lectures. This hands-on approach aims to equip students with the skills necessary to apply Python programming effectively in environmental remote sensing.

By the end of the semester, students will be familiar with the underlying mechanisms of remote sensing techniques, the diversity of remote sensing data, and have a strong understanding of Python programming and its applications in the geospatial domain.

Materials

Text (While this book will serve as a guide throughout the course, acquiring it is optional. The lecture slides have been crafted to comprehensively cover the content of this book, ensuring a thorough understanding of the material):

Remote Sensing of the Environment: An Earth Resource Perspective 2nd Edition by John R.

Jenson, Pearson Prentice Hall, 2007.

Software Tools

• Miniconda or Anaconda
• Visual Studio Code
• Git
• SeaDAS
• SNAP

Cloud Computing Platforms

• Google Earth Engine
• Google Colab

Course Resources

• Course Website: https://envs473.bingqingliu.com/

1. Syllabus
2. Jupyter Notebooks
3. Lab assignments
4. Video recordings

• Moodle: https://moodle.louisiana.edu/course/view.php?id=43030

1. Announcements
2. Lab submissions
3. Check grades

• GitHub: https://github.com/bingqing-liu/ENVS473-Remote-Sensing

Backup Device

Ensure you have a flash drive or another backup device with at least 8 GB of space. This will be indispensable for transferring remote sensing data between computer labs

Course Communications

Please try to use your @louisiana.edu addresses. Emails of a personal or private nature sent to the instructor should include “ENVS473” in the email subject line. The instructor will strive to respond to questions within one business day, i.e., 24 hours excluding weekends.

Expectations:

• Class attendance: Students are required to attend all weekly lectures and laboratory sessions. If you anticipate missing a class, please inform me in advance with a valid excuse.

• Homework/Lab Assignment: Homework/Lab assignments will be assigned periodically throughout the semester. All assignments will be due in class on the date specified. Late submissions will only be considered if arrangements are made in advance with me due to exceptional circumstances. Please note that your assignments must be clear, well-written (always include your name, title, assignment number, etc). Please include screen shots of your work in your lab write-ups if required.

• Final Project: Each student is expected to investigate a remote sensing topic of his or her choice that uses satellite data in combination with python. Data analysis with some literature reviews are considered typical class projects.

• Exams: There will be one midterm and one final exam.

Grading:

• Homework and Quizzes 40%
• Exams 40% (20 and 20% each for midterm and final)
• Final Project 15%
• Participation 5%

Grading Scale:

• A = 90-100
• B = 80-89
• C = 70-79
• D = 60-69
• F = Below 60

Note: Syllabus Changes: The instructor reserves the right to make changes as necessary to this syllabus. If changes are necessitated during the term of the course, the instructor will immediately notify students of such changes by email.