ERTH 4530

Enviro. Remote Sensing

Lec 01 and Lab B01 selected

This course introduces students to remote sensing methods and their use in extracting thematic information for a widening suite of disciplines. Course topics incorporate the fundamentals of remote sensing (e.g., EMR, image evaluation, enhancement, classification, accuracy), focusing on contemporary optical multispectral systems. In the lab setting students use image processing techniques to evaluate, classify, and estimate uncertainty in remote sensor data.

Sections

Lecture
Lab

From the syllabus

From the Fall 2026 syllabus by Christopher Greene. Open the PDF

Grading

45%Knowledge tests (2)225 points each
10%In-class exercisesWeekly; complete, partial, or incomplete
17.5%Small lab deliverables175 points total
25%Independent lab projectTwo parts; 250 points total

Textbook

  • Introduction to Remote Sensing (6th ed.), J. B. Campbell, R. H. Wynne, and V. A. Thomas (optional)

Course rules

Category pass
Students must earn at least 275 of 550 theory points and 225 of 450 applied points to pass.
Workshop drops
The lowest structured workshop score is dropped; a missed workshop receives zero.
Late workshops
Workshops have no late penalty, but receive zero after the deadline.
Late projects
Project deliverables lose 20% per calendar day late.
Extensions
Each student starts with five extensions for applied work; notify teaching staff before the deadline to use one.
Missed midterm
Students with an accepted reason may write a makeup exam on November 2, 2026.
Missed final
Students with an accepted reason receive an incomplete and may write a makeup exam in the Winter term.
Reference materials
Tests allow the instructor-provided reference sheet, a scientific calculator, and a ruler or straight edge.
GenAI
Limited use is allowed with attribution; AI cannot create assessment work or provide direct assessment answers.

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