GEO-SCI426/626 Remote Sensing and Image Interpretation

Department of Earth, Geographic, and Climate Sciences

University of Massachusetts - Amherst

4 cr, Fall 2023

Class schedule table        

Notes, lab and assignments will be updated on the Canvas.

Lecture†††††††††††††††† TuTh 1-2:15 am    †††††††††††† 225 Morrill Sci. Ctr.

Lab††††††††††††††††††††††† Tu 2:30-4:30 pm 212 Morrill IV South (UMass IT lab) (No lab on 9/5. First lab on 9/12)

Instructor†††††††††††† Prof. Qian Yu, Morrill 267,, Office hour: Mon 1:30-2:30, Thu 10-11 or by appt

TA††††††††††††††††††††††††† Hutch Tyree, Morrill 264,, Office hour: Tues Wed TBA or by appt


Course description

This course introduces the fundamentals of remote sensing. Class lectures will focus on a range of concepts and techniques key to understanding how remote sensing data are acquired, displayed, restored, enhanced, and analyzed. Topics include remote sensing principles, aerial photography, image interpretation, major remote sensing systems, image display and enhancement, information extraction, accuracy assessment, and remote sensing in environmental research and applications. First half of the semester focuses on theory of remote sensing. In the second half, we have several hands-on computer labs to gain experience using the image processing software ENVI. We will also explore a range of practical issues related to the application of remote sensing to solve real world problems. Overall, this class emphasizes remote sensing theory and knowledge.


Course objectives

To provide you with an introduction to the principles and practices of photo interpretation and digital remote sensing for use in environmental monitoring, measurements of structural parameters, and natural resource management.

This class will ensure students have knowledge on these aspects:

1.the properties and characteristics of aerial photographs.

2.remote sensing systems: a) how to define the type of remote sensing needed to fulfill the userís stated objectives, b) where existing remote sensing data which fulfills his/her objectives may be located, and c) how to obtain new aerial photography, if necessary. image processing: a) basic concepts on non-photographic remote sensing, b) general principles of digital image processing for remote sensing applications, and c) future applications of remote sensing to natural resource management and related fields.

4.remote sensing information extraction: a) which characteristics of land cover types can be mapped/measured from remote sensing, b) different techniques available for mapping and measuring these land cover types, and c) how accurately these land cover characteristics can be mapped from remote sensing.

Prerequisites:  High school Geometry, Algebra 2, and precalculus.

Required textbook for lectures 

1.    Jensen, John R., 2016, Introductory Digital Image Processing: A Remote Sensing Perspective, Pearson, 4th ed. ISBN 978-0-13-405816-0.

* Digital, hardcopy, any format is fine. The book is on reserve in the library.

Reference book for lectures

2.    Jensen, John R., 2007, Remote Sensing of the Environment: An Earth Resource Perspective, Prentice Hall: Upper Saddle River, NJ. 2nd ed. ISBN 0-13-188950-8

3.    Lillesand, Kiefer and Chipman, Remote Sensing and Image Interpretation, Wiley.

4.    Sabins, Floyd and James Ellis. Remote Sensing: Principles, Interpretation, and Applications, Waveland Press.

* Reference books are not part of the reading. They are listed as courtesy as some of my lecture figures are from those sources.

Lab software access

A professional remote sensing software ENVI will be used for most of the labs. ENVI is installed in 212 Morrill. You can also access ENVI through UMass Azure Virtual Desktop. You can find the instruction on how to access AVD here.

Grading and evaluation

Exercises and assignments

Mid-Term Exam

Final Project


Class participation& attendance






-          All written assignments must be handed in on time.

-          Mid-exams will cover key concepts from lecture, readings and remote sensing theories.

-          Presentation: 426 students will present a satellite sensor.

626 students will present a journal paper about remote sensing application in your field.

-          Attendance will be accessed by class participation, taking rolls at random dates and pop quiz.

Policy on attendance and due dates for assignments:

      The class is taught in person with zoom recording for lectures!

426 students must take the course in-person. For most students taking the class in person, attendance to both lecture and lab session is required in the normal circumstances and forms a portion of your grade. Lectures and some lab tutorials will be recorded for the convenience of review. This does not mean you are free to skip lectures or labs. For absences due to health reasons, family illness, religious observance or other extenuating circumstances, students should inform the instructor in a timely fashion. Verification document is requested for alternate arrangements to be made. It is the responsibility of the student to review the lecture recording in the event the student cannot attend class. A google form is created to record excused absences.

The class is also open to 626 students who enrolled in GIST Masters program online and need to take the course by zoom live stream or asynchronously. Please fill out the pre-class survey and get approval from the instructor at the beginning of the semester. We also want to know how tohelp your remote learning.

      Assignment late submission policy Both due date and late due date are noted in each assignment. The late due date is normally 7 days after the due date. For the assignments right before mid-term and near the end of the semester, the late due date could be shortened (e.g., 3 days late due) or no late due allowed. The purpose is for me to give out the assignment answers earlier for mid-term preparation. In all, late due date printed on the assignment should be followed.

All exercises must be turned in by the due date for full credit unless arranged with the instructor before the deadline. Each student has one chance of late submission (before late due date) with no point deduction during the semester. Other late submissions before late due date without advance permission by the instructor will cause a grade deduction. No lab assignment will be accepted after late due date. By that time, the assignment answers will be released.

Late submission

1-3 days

4-7 days

>7 days or after late due date

Point deduction



Not accepted, the assignment answers will be released

The instructor could grant extension for excused late submission. Please email the instructor to request an extension.

      Assignments should be submitted through Canvas. Any submission made by email attachment or link to your shared document will not be considered for grading and meeting deadline.

      Mid-term is close book and the schedule on syllabus is only tentative. No make-up exams will be given unless PRIOR arrangements have been made with instructor and documentation of excused must be provided.

      Missing mid-term exam, presentations, final project presentation, project report, or >=3 homework will result in an F for the course regardless of the final grade.

Disability Accommodations

The University of Massachusetts Amherst is committed to providing an equal educational opportunity for all students. If you have a documented physical, psychological, or learning disability on file with Disability Services (DS), you may be eligible for reasonable academic accommodations to help you succeed in this course. For disability accommodations, please register with Disability Services as early as possible. Meanwhile please notify the instructor through the pre-class survey or email within the first two weeks of the semester so that we may make appropriate arrangements. DS accommodation request must be filed each semester. Do not expect the request will be automatically carried over.

Policy on class materials and recordings

Students can only use the lecture notes and the notes they take from class for their own personal use, and not share (sell) these notes via an outside vendor or entity without the faculty/instructorís permission.This pertains to in-class recordings as well.Usage of the notes or in-class recordings in this way without the faculty memberís permission is a violation of the faculty memberís copyright protection.

This does not pertain to accommodations under the Americans with Disabilities Act (ADA), although recordings or sharing of notes for ADA accommodations should not pertain to distribution beyond the students in the class receiving the accommodations.

Academic Honesty Policy

Since the integrity of the academic enterprise of any institution of higher education requires honesty in scholarship and research, academic honesty is required of all students at the University of Massachusetts Amherst. Academic dishonesty is prohibited in all programs of the University. Academic dishonesty includes but is not limited to: cheating, fabrication, plagiarism, and facilitating dishonesty. Appropriate sanctions may be imposed on any student who has committed an act of academic dishonesty. (

      All work submitted must be your own in presentation. You may discuss homework and final project with other students (you are encouraged to do so), but the homework and project write-up must be your work.

      Copying homework is not allowed, and collaboration so close that it looks like copying is not allowed. Violation will be reported to the UMass Academic Honesty Board.

      In write-up, if you make use of a printed or on-line source as reference, you need to cite it and mention it.

Class Schedule (subject to change according to the progress)

This table will be updated through the semester. Schedule could change according to our progress and reading will be filled in. You can direct access by the following URL. This URL is also posted in Canvas.







2 Sept 4


Lecture 0

Introduction and syllabus




No lab



Lecture 1.1

Physic basis of remote sensing (1):

Electromagnetic radiation principles

Jensenís ch6 p185-191.

ParticleModel p191-196 is optional.

3 Sept 11


Lecture 1.2

Physic basis of remote sensing (2):

Atmospheric Energy-Matter interaction

Terrain Energy-Matter interaction

Path radiance

Jensenís ch6


p208-215 (end before Atm Transmittance)


Lab 1

Hyperspectral curve, spectroradiometer

Jensenís ch 1, page 1- 3 In situ data collection


Lecture 2.1

Aerial photography: vantage point, cameras (1)

PDF in Canvas.

4 Sept 18


Lecture 2.2

Aerial photography: color theory, filter (2)



Lab 2

Aerial Photographic System



Lecture 3.1

Multispectral remote sensing systems: concepts: digital images, resolution, orbits, platform, types of system

Jensenís ch 2 p37-38 (end before Microdensitometer) and p44-48, Ch 1 p3-17 (end before Polarization Information)

5 Sept 25


Lecture 1.3

Atmosphere correction (3)

p220-223 (start from Absolute Atm Correction, end before Relative Radiometric Corr)

Lab 3

Image display and multispectral remote sensing System



Lecture 3.2

Multispectral remote sensing systems: Land viewing sensors: Landsat and SPOT

Jensenís ch 2 p44-63 and p73-82

6 Oct 2


Lecture 3.3

Multispectral remote sensing systems: Ocean viewing sensors: AVHRR, EOS, ESA sensors

Jensenís ch 2 p63-73, p88-101 (end before Airborne Digital Cameras)


Lab 3b

Access public remote sensing data and preprocessing



Lecture 3.4

Multispectral remote sensing systems: VHR, hyperspectral sensors, SmallSats/CubeSats


7 Oct 9



No class, Monday schedule




No class, Monday schedule



Lecture 4.1

Thermal infrared remote sensing

Online reading

8 Oct 16


Lecture 4.2

Thermal infrared remote sensing


Lab 4

Thermal infrared remote sensing interpretation

PDF in Canvas


Lecture 5.1

Image enhancement: Radiometric enhancement and Spatial enhancement

Jensenís ch 8 p273-301

9 Oct 23


Lecture 5.2

Image enhancement: Spectral enhancement and vegetation indices

Jensenís ch8 p308-340


Lab 5

Image enhancement



Lecture 6.1

Information extraction:  supervised classification

Jensenís ch9 p361-402

10 Oct 30



Mid-term (10/31)



Mid-term continue, no lab



Lecture 6.2

Information extraction:  unsupervised classification

Jensenís ch9 p402-411

11 Nov 6



Lecture 6.3

Information extraction:  object-based classification

Jensenís ch9 p413-423


Lab 6





Guest lecture: microwave remote sensing


12 Nov 13






Lec & Lab 7

Change detection

 Jensenís ch12



Working on final project


13 Nov 20


Lab 8

Online RS platform: Google earth engine




Working on final project or lab




Thanksgiving recess


13 Nov 27



Working on final project



No lab. Tu runs Th schedule.




Working on final project


14 Dec 4



Working on final project





Final project presentation





Final project report due on Dec 4