Introduction to Remote Sensing

This is an intermediate-level course that primarily is taken by students in the Earth and Environmental Science Department; however, sometimes there are students in the course from Anthropology, Design and Engineering (up to 30% of the course). The course is usually composed of advanced undergraduate students (typically 60%) and graduate students (typically 40%). The suggested pre-requisites are introductory physics and calculus. Computer programming and GIS are not required.

In the first half of the course I spend most of the time on the basic physics of remote sensing and the range of techniques (aerial photographic techniques; multispectral, hyperspectral, thermal, and other image analysis). I then go over the most widely available sensors, cover all the basic types of analyses (from simple color schemes to vegetation and burn indices to classifications, etc.). In the second half of the semester the students develop, complete and present research projects. In this independent research project the students are expected to use remote sensing tools via Google Earth Engine, and at the end of the semester should have a good understanding and the basic skills of remote sensing. Our workshops, roughly 1 per week, are done in Google Earth Engine. Expectations for the graduate student independent research projects are at the graduate level and can relate to their capstone or Ph.D. thesis research topics.

Content:
Understand the basic principles of remote sensing
Understand the basic characteristics of remote sensors
Understand the broad array of remote sensing applications
Understand remote sensing applications specific to the earth and environmental sciences
Understand how image acquisition/data collection is done
Understand the various types of image resolution, and their pros and cons
Understand when one needs high resolution images for an application, and what type of high resolution
Gain fundamental knowledge of the physics of remote sensing
Understand the connection between electromagnetic energy and remote sensing
Understand the differences of aerial photographic techniques, multispectral, hyperspectral, thermal, and other image analysis.

Skills
Ability to critically assess the strengths and weaknesses of remote sensing instruments and platforms for a variety of application scenarios
Ability to create maps to communicate geographic information
Ability to process remotely sensed data in Google Earth Engine (GEE) in order to make it useful for the interpretation and understanding of multiple earth and environmental science applications.
Ability to perform image enhancement on remotely sensed imagery in GEE
Interpret aerial and satellite images
Ability to extract information from remotely sensed data using a variety of manual and automated techniques within GEE
Be able to read, edit and understand a GEE java script in order to develop multi-step remote sensing workflows to solve problems in a variety of application areas
Ability to understand how to come up with an idea for an independent research project in the earth and environmental sciences that uses remote sensing tools to help further understanding and then apply acquired knowledge and critical thinking skills to further the understanding of an earth or environmental science question or problem with appropriate remote sensing data and processing methods
Ability to work effectively with others
Ability to troubleshoot errors in a computer code
Ability to read and understand technical remote sensing scientific journal articles such that the method used in the research documented in the paper could be applied by the student to answer a similar question.
Ability to clearly and concisely communicate findings from the analysis of remotely sensed data through the written word and graphical products.

1. In the online lectures and quizzes, students learn the basic principles of remote sensing.

2. In the workshops, students work in groups of three, to write, edit and answer questions about Google Earth Engine javascript codes. In these exercises (roughly 14/semester), students learn and demonstrate many of the learning goals, including how to critically assess the strengths and weaknesses of remote sensing instruments and platforms for a variety of application scenarios (as they work through established codes and assess their products); ability to trouble-shoot errors in computer code (as they write new code); ability to work with others, since they work in groups and turn in just one product together; ability to create maps to communicate geographic information, since each workshop involves varying amounts of geographical output. Many of the workshops specifically guide the students to gain the ability to perform specific skills, including image enhancement on remotely sensed imagery in GEE; interpreting aerial and satellite images; and extracting information from remotely sensed data using a variety of manual and automated techniques within GEE. Throughout all of the workshops they gain an increasingly ability to edit and understand a GEE java script in order to develop multi-step remote sensing workflows to solve problems in a variety of application areas, which they eventually put to use in their final projects.

3. While students complete their final projects, amongst other things, they learn and demonstrate their abilities to process remotely sensed data in Google Earth Engine (GEE) in order to make it useful for the interpretation and understanding of multiple earth and environmental science applications. In order to both start and complete the project, they must gain an understanding of how to come up with an idea for an independent research project in the earth and environmental sciences that uses remote sensing tools to help further understanding (we discuss a number of readings and do group work to help them generate these ideas); and then apply acquired knowledge and critical thinking skills to further the understanding of an earth or environmental science question or problem with appropriate remote sensing data and processing methods.

This course is taught as a "Structured Active In-class Learning" (SAIL) class, meaning (for this course) that there are modules on Canvas to guide the students individually through the basics of the material at home before "class" (the building blocks of the Knowledge and Comprehension in the Bloom's Taxonomy of Learning), and then in class, students do workshops in groups to get at higher order learning objectives (continuing the Comprehension, but also moving on to Application, Analysis, Evaluation and Synthesis).

The idea is that students get the basics on their own from reading and watching my module videos, but it is much more useful and fun to tackle the higher order learning objectives together in class.

Assessment takes place in three primary ways:
1. Module Quizzes (both during and post, for a total of 16/ semester: Multiple choice and long-answer questions.
2. Workshops (roughly 14/semester) are graded with the following questions that make up the assignment rubric:
A. Was this assignment turned in on time? Students lose 1 point for each day late. 2 points
B. Did the student answer all questions on the assignment/workshop thoroughly, correctly, clearly and thoughtfully? For each minor incorrect and/or unclear and/or incomplete answer, student will lose 0.25-1 pts. Major incomplete/unclear/incomplete answers will result in 1-3 points off each. 16.0 pts
C. Did the group work well together, collaboratively, in class? 2.0 points

3. The final paper is graded with the following rubric (there is a separate graded presentation as well, graded by peers and the instructor):
A. Abstract: 1-2 paragraphs, define the topic, state major conclusions, and state why it is important. This should read as a very succinct summary of the topic, findings, and why the work is important. Student should fully identify the problem or research question. Purpose; Design/methodology/approach; Findings (mandatory); Research limitations/implications; Practical implications; Social implications (if applicable); Originality/Value.
B. Introduction: Student should put the research project in the big picture. Summarize what societal need, scientific question, priority, problem, etc., is addressed by your project and then narrate recent attempts to address it with research in your field, leading to your area, your topic, and your project. This can be a bit broad and will differ in length depending on the topic in your thesis, but don't teach about areas that will never recur later in the document.
C. Background: This is essentially a very short literature review for your research topic. You should give necessary background information for your research question, leading to why it is important and therefore why the work was done. While summarizing, you should also re-organize the information that has been published based on its relevance to the thesis topic.
D. Approach/Methods: A description of the procedure you followed in your preliminary data analysis. Your methods section should include information to allow the reader to assess the believability of your results.
E. Results: This should include at least 4 figures, generated by you, with clear figure captions, etc. Student should fully describe their results. Are the figures clear, with good captions? Are the results fully described in the text? This should read as a systematic exposition of the author's findings.
F. Conclusion and Discussion: Here you will make your argument that your work should be continued and why, based on your preliminary results. You will outline your conclusion, based on the preliminary results. You will also include questions or problems that remain, or that have been identified as a result of the work. Somewhere in your discussion and/or conclusion sections you will also give an assessment of the value or importance of the conclusions. The student should discuss the work in a critical manner with clear articulation of analytical questions and key terms.An analysis and assessment of the information gathered. The student should discuss the work in a critical manner with clear articulation of analytical questions and key terms.
G. Clarity and Mechanics of the Paper: Is proper spelling, punctuation, grammar and construction used in the writing?
H. Does the writing motivate interest in the reader?
I. Does the student employ a range of primary sources appropriate to informing the work and are the sources cited correctly? Does the student critically analyze sources with understanding?
J. Understanding of the fundamentals of the work: Does the student demonstrate clear understanding of the papers cited, the research done and the conclusions made?
K. Future Work: In this section you should describe what further analysis would need to be completed in order to fully answer your research question.

Prospectus (Acrobat (PDF) 36kB Nov5 20)

iGETT Remote Sensing Education concept module videos: Students are asked to watch these, as well as videos made by me, to help prepare for in-class work.
Readings are also assigned from a few chapters in Introduction to Remote Sensing, by Campbell and Wynne, as well as online sources such as: http://www2.bren.ucsb.edu/~dturney/WebResources_13/RemoteSensing/TheLightHandbook.pdf and other online sources are also assigned.