Course:FRE527/Syllabus

COURSE INFORMATION

Session and term: 2025W1 Class location: MCML 154

Class times: Mon/Wed 12:30-2:00pm Lab times: NA

Course duration: February 24 – April 16 Credits: 1.5

COURSE DESCRIPTION

This course introduces you to core environmental datasets spanning weather, ecology, and satellite imagery data sources, which are routinely used to support environmental metrics and decision-making in the food and resource sector. It provides hands-on experience with geospatial data extraction, processing and analysis techniques, as well as visualization tools, which are particularly adapted to dealing with the complexities of each environmental data type. The main programming language used is R, along with Python and JavaScript for Google Earth Engine.

INSTRUCTOR

Instructor: Joséphine Gantois Office location: MCML 237

Email: josephine.gantois@ubc.ca Office hours: TBD

LEARNING OUTCOMES

By the end of this course, students will be able to:

Explain the current capacities and limitations for measuring a suite of core environmental variables
Exercise critical thinking when engaging with evidence based on environmental data
Identify appropriate sources of data given a particular analysis or visualization goal
Source multiple publicly available environmental datasets
Write reusable R, Python, and Javascript scripts to extract, process, analyze, and visualize publicly available environmental data
Manipulate large geospatial datasets using Google Earth Engine

ASSESSMENTS


Assignments	3 Assignments	45%
Group Project	Question & data plan In-class presentation Final submission	5% 15% 15%
Weekly quizzes	View schedule online	10%
In-class participation		10%
TOTAL		100%

COURSE POLICIES

Assignments

Unless otherwise indicated, assignments are due at 11:59 pm via Canvas on the due date. For each assignment, specifications will be provided of what to include in the final product. The grade received by the student for the assignment will be based on how well the student met the stated specifications, as well as any additional insight the student brings to the assignment. Therefore, for each assignment, it is important for each student to make sure that they understand the objectives and specifications, and ask questions if clarification is needed. This is the same approach that each student should take in their professional life. It is the responsibility of the student to fully understand assignment and course expectations.

Assignments are designated as Individual Assignments and must be submitted individually. Discussion and collaboration among students are strongly encouraged. However, for Individual Assignments, each student must do their own work and submit their original work. Identical submissions are a form of academic dishonesty and immediately receive a grade of zero and possibly negative affect your academic record.

Late Assignments

Assignments handed in within 24 hours of the deadline will receive a 5% discount. Assignments handed in later than that will receive zero credits. Quizzes handed in later than the deadline will receive zero credits. The quiz with the lowest mark will be dropped from the grade calculation, to allow flexibility in case a quiz submission is missed.

Missed Assignments

If you miss an assignment or need to reschedule an assignment, you must discuss this with your Instructor. If you have not discussed with your Instructor prior to the submission date, you will receive a grade of 0.

Writing Exams

All exams will be in-person and will follow MFRE exam protocol (See Student Portal). Exams may be online, e.g., in Canvas, but students must be physically present, use the lock-down browser, and invigilated. If a student is unable to write an exam, they must have a verifiable doctor’s note and must contact the Course Instructor, Course Assistant, and MFRE Program Coordinator before the scheduled exam date/time. If the documentation is considered legitimate, the Course Instructor will let you know how to proceed. If you miss an exam and you have not previously discussed this with the Instructor, you will receive a grade of zero. Notification after the exam date is not acceptable and will result in a grade of zero. Calculators may be utilized in class, on assignments, and during the exams.

Generative Artificial Intelligence (AI) Use

The use of Generative AI tools at UBC is a course or program-level decision. Students are permitted to use AI tools for formative work such as gathering information or brainstorming but may NOT use it on any assessed work or final submission. Students are ultimately accountable for the work they submit, and any content therein. Note: AI is a developing area and guidelines of its use may change. Students are encouraged to learn the material and produce their own output, rather than AI generated output.

Attendance and Missing Classes/Labs

Students are expected to attend all classes, labs, or workshops. If you cannot make it to a class, lab, or workshop due to a medical or personal emergency, email your Instructor, your Course Assistant, and Olivier Ntwali, MFRE Program Coordinator ahead of time to let them know.

Recordings

There is no required distribution of recordings of class. Recording will be provided based upon on the decision of the course instructor. Classes are designed as and are intended to be in‐person.

Copyright

All materials of this course (course handouts, lecture slides, assessments, course readings, etc.) are the intellectual property of the instructor or licensed to be used in this course by the copyright owner. Redistribution of these materials by any means without permission of the copyright holder(s) constitutes a breach of copyright and may lead to academic discipline and could be subject to legal action. Further, audio or video recording of classes are not permitted without the prior consent of the instructor.

Respectfulness in the Classroom

Students are expected to be respectful of their colleagues at all times, including faculty, staff and peers. This means being attentive and conscious of words and actions and their impact on others, listening to people with an open mind, treating all MFRE community members equally and understanding diversity.

Respect for Equity, Diversity, and Inclusion

The MFRE Program strives to promote an intellectual community that is enhanced by diversity along various dimensions including status as a First Nation, Métis, Inuit, or Indigenous person, race, ethnicity, gender identity, sexual orientation, religion, political beliefs, social class, and/or disability. It is expected that all students and members of our community conduct themselves with empathy and respect for others.

Centre for Accessibility

The Centre for Accessibility (CfA) facilitates disability‐related accommodations and programming initiatives designed to remove barriers for students with disabilities and ongoing medical conditions. If you are registered with the CfA and are eligible for exam accommodations, it is your responsibility to let Olivier Ntwali, Academic Program Coordinator, and each of your Course Instructors know. You should book your exam writing with the CFA using its exam reservation system: for midterm exams or quizzes, at least 7 days in advance; and final exams, 7 days before the start of the formal exam period.

ACADEMIC HONESTY

Plagiarism and Academic Dishonesty

Academic dishonesty and plagiarism are taken very seriously in the MFRE program. All incidences of plagiarism will be escalated to the MFRE Academic Director with penalties ranging from a mark of zero on the assignment, exam or course to being required to withdraw from the program. Note: If a student needs to extend his/her program due to a failed course or unsatisfactory progress, they will have to pay the full MFRE tuition fees for that term/s.

Academic misconduct that is subject to disciplinary measures includes, but is not limited, to the following:

Plagiarism, which is intellectual theft, occurs where an individual submits or presents the oral or written work of another person as his or her own. In many UBC courses, you will be required to submit material in electronic form. The electronic material will be submitted to a service which UBC subscribes, called TurnItIn. This service checks textual material for originality. It is increasingly used in North American universities. For more information, review TurnItIn website online.
Using Generative Artificial Intelligence (AI) tools like ChatGPT, Bard, or other Generative AI models to generate content or conduct analysis for evaluations, without proper citation and or if asked not to use AI, is considered plagiarism and academic misconduct. If students use AI in their submissions, they must cite the AI generator using citations consistent with the UBC Academic Honesty Standards.
Cheating, which may include, but is not limited to falsification of any material subject to academic evaluation, unauthorized collaborative work; or use of unauthorized means to complete an examination.
Working with Others on an Assignment: You are encouraged to work with other students, but you must turn in your own individual assignment. If you have an answer that is too close to another student’s answer, this will be considered academic dishonesty and this will be handled according to the MFRE and UBC policies.
Resubmission of Material, submitting the same, or substantially the same, essay, presentation, or assignment more than once (whether the earlier submission was at this or another institution) unless prior approval has been obtained from the instructor(s) to whom the assignment is to be submitted.
Use of academic ghostwriting services, including hiring of writing or research services and submitting papers or assignments as his or her own.

Student Responsibility: Students are responsible for informing themselves of the guidelines of acceptable and non‐acceptable conduct for examinations and graded assignments as presented via MFRE Code of Conduct; MFRE Turn it in, Course Syllabus, MFRE Instructors; Canvas and UBC academic misconduct policies.

Penalties for Academic Dishonesty: Penalties for academic dishonesty are applied at the discretion of the MFRE program. Incidences of academic misconduct may result in a mark of zero on the assignment, examination, or course, required withdrawal from the program, and/or the matter being is referred to UBC Graduate Studies.

COURSE SCHEDULE

Date	Class Topics
Week 1 Weather and Climate Data
MON February 24 12:30-2:00	Topic: Introduction to Weather Data for Economic Applications Includes: climate science refresher Quiz 1 posted Supporting resources Carleton, T. A., & Hsiang, S. M. (2016). Social and economic impacts of climate. Science, 353(6304), aad9837. Hsiang, S., & Kopp, R. E. (2018). An economist’s guide to climate change science. Journal of Economic Perspectives, 32(4), 3-32.
WED February 26 12:30-2:00	Topic: Weather Data Products for Economic Applications Includes: introduction to geospatial data In-class tutorial in R Quiz 1 due; Quiz 2 posted Assignment 1 posted Supporting resources Auffhammer, M., Hsiang, S. M., Schlenker, W., & Sobel, A. (2013). Using weather data and climate model output in economic analyses of climate change. Review of Environmental Economics and Policy.
Week 2
MON March 3 12:30-2:00	Topic: Weather and Climate Data Access and Wrangling In-class tutorial in R Quiz 2 due; Quiz 3 posted Supporting resources Supplementary material of D'Agostino, A. L., & Schlenker, W. (2016). Recent weather fluctuations and agricultural yields: implications for climate change. Agricultural economics, 47(S1), 159-171.
WED March 5 12:30-2:00	Topic: Weather Data Use and Climate Model Data for Economic Applications In-class check: application for Google Earth Engine account submitted Quiz 3 due; Quiz 4 posted Assignment 1 due Supporting resources Auffhammer, M., Hsiang, S. M., Schlenker, W., & Sobel, A. (2013). Using weather data and climate model output in economic analyses of climate change. Review of Environmental Economics and Policy.
Week 3 Satellite Data
MON March 10 12:30-2:00	Topic: Introduction to satellite imagery and review of geospatial data fundamentals In-class tutorial: Google Earth Engine basics in the online code editor Quiz 4 due; Quiz 5 posted Supporting resources: Lovelace, R., Nowosad, J., & Muenchow, J. (2019). Geocomputation with R. Chapman and Hall/CRC. chapters 2 (Geographic data in R) and 7 (Reprojecting data). Overview of Coordinate Reference Systems (CRS) in R (pdf document) VanderPlas, J. (2016). Python data science handbook: Essential tools for working with data. " O'Reilly Media, Inc.". 04.13 Geographic Data with Basemap Cardille, J. A., Crowley, M. A., Saah, D., & Clinton, N. E. (Eds.). (2023). Cloud-based remote sensing with google earth engine: fundamentals and applications. Springer Nature. F1.0: JavaScript and the Earth Engine API F1.3: The Remote Sensing Vocabulary Spatial Thoughts End-to-End Google Earth Engine course: introductory videos (introduction to remote sensing, introduction to google earth engine)
WED March 12 12:30-2:00	Topic: Satellite data products for monitoring vegetation and land use In-class tutorial: Google Earth Engine intermediate in the online code editor Quiz 5 due; Quiz 6 posted Assignment 2 posted Supporting resources: Zeng, Y., Hao, D., Huete, A., Dechant, B., Berry, J., Chen, J. M., ... & Chen, M. (2022). Optical vegetation indices for monitoring terrestrial ecosystems globally. Nature Reviews Earth & Environment, 3(7), 477-493.
Week 4
MON March 17 12:30-2:00	Topic: Satellite data products for monitoring vegetation and land use (continued) and Google Earth Engine Python API In-class tutorial: Google Earth Engine Python API Quiz 6 due; Quiz 7 posted
WED March 19 12:30-2:00	Topic: Satellite imagery applications in economics In-class tutorial: manipulating weather data in Google Earth Engine Quiz 7 due; Quiz 8 posted Assignment 2 due Supporting resources: Rolf, E., Proctor, J., Carleton, T., Bolliger, I., Shankar, V., Ishihara, M., ... & Hsiang, S. (2021). A generalizable and accessible approach to machine learning with global satellite imagery. Nature communications, 12(1), 4392. Sherman, L., Proctor, J., Druckenmiller, H., Tapia, H., & Hsiang, S. M. (2023). Global High-Resolution Estimates of the United Nations Human Development Index Using Satellite Imagery and Machine-learning (No. w31044). National Bureau of Economic Research.
Week 5 Ecological Data
MON March 24 12:30-2:00	Topic: Introduction to Ecological Data and Measuring Plant Outcomes In-class tutorial: using Python and API requests to access data Quiz 8 due; Quiz 9 posted Assignment 3 posted Supporting resources: IPBES, W. (2019). Intergovernmental science-policy platform on biodiversity and ecosystem services. Summary for Policy Makers of the Global Assessment Report on Biodiversity and Ecosystem Services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. IPBES Secretariat, Bonn, Germany.
WED March 26 12:30-2:00	Topic: Monitoring Wildlife and Monitoring Plant Phenology Quiz 9 due; Quiz 10 posted Submission of question and data plan for final project due Supporting resources: Trisos, C. H., Auerbach, J., & Katti, M. (2021). Decoloniality and anti-oppressive practices for a more ethical ecology. Nature Ecology & Evolution, 5(9), 1205-1212. Guzman, L. M., Johnson, S. A., Mooers, A. O., & M'Gonigle, L. K. (2021). Using historical data to estimate bumble bee occurrence: Variable trends across species provide little support for community-level declines. Biological Conservation, 257, 109141.
Week 6
MON March 31 12:30-2:00	Topic: Use of Ecological Data in Economics (I) Quiz 10 due; Quiz 11 posted Assignment 3 due Supporting resources: Frank, E., & Sudarshan, A. (2024). The social costs of keystone species collapse: Evidence from the decline of vultures in india. American Economic Review, 114(10), 3007-3040. Frank, E. G. (2024). The economic impacts of ecosystem disruptions: Costs from substituting biological pest control. Science, 385(6713), eadg0344. Madhok, R. (2023). Infrastructure, Institutions, and the Conservation of Biodiversity in India.”. Working paper.
WED April 2 12:30-2:00	Topic: Use of Ecological Data in Economics (II) Quiz 11 due; Quiz 12 posted
Week 7 Closing week
MON April 7 12:30-2:00	Opportunity to review past topics or introduce new ones based on student feedback Quiz 12 due
WED April 9 12:30-2:00	In-class project presentations
Week 8
WED April 16 8 pm	Final project report due