GEOGXXX: Remote Sensing of Natural Hazards
Abbreviated Syllabus Download full syllabus here
Course Description
This course introduces students to the capabilities of analyzing remotely-sensed data (and other gridded data products) through Google Earth Engine (GEE). With a focus on utilizing these data products to monitor the impacts of natural hazards, the course combines comprehensive remote sensing instruction — including theory, data acquisition, and data analysis — with relevant skill development using available data products to monitor natural hazards and their environmental impacts.
Students will execute analyses in Google Earth Engine, primarily using GEE’s JavaScript API. The curriculum emphasizes practical applications and hands-on learning, culminating in an research project that asks students to conduct their own analyses on a hazard of interest, integrating the skills and knowledge acquired throughout the course.
Course Philosophy
The course has been developed around “deep learning” by intertwining the theoretical foundations of remote sensing with the practical application of these skills to the pressing issue of natural hazard monitoring. This approach fosters a deeper understanding of the subject matter, as students are not only learning the ‘how’ of remote sensing analysis but also the ‘why’, connecting their technical skills to real-world challenges. The emphasis on collaborative learning through pair programming and group projects further enhances the deep learning experience, encouraging students to actively engage with the material, discuss concepts, and problem-solve collectively. By focusing instruction on the impactful context of natural hazards and fostering a collaborative learning environment, this course creates a framework for students to develop a deeper comprehension of remote sensing techniques and their applications.
Over half of the instructional time of this course will be dedicated to “hands-on-the-keyboard” coding skill development. The first half of the semester will introduce students to coding in Google Earth Engine using the pair-programming technique to complete in-class exercises (two people working together on a single computer, with a driver (coding) and a navigator (reviewing)). Hands-on-the-keyboard practice will be supplemented with lectures and active-learning exercises that introduce students to important theoretical concepts in remote sensing and disaster studies. The second half of the semester will include significant lab time, where students are asked to independently implement techniques learned in the first half of class. The final project will be group-based and will involve self-reflection as a large component of the project grade. The CRediT (Contributor Roles Taxonomy) authorship roles will recognize individual contributions of group members to the final project.
Because active learning is a major component of this class, full participation is essential for success, which is why class participation makes up such a large component of the course grade. Students should arrive ready to be engaged and present for each class period.
Course Texts
- Required Texts: None
- Readings: Will be provided weekly on the course site.
Course Goals and Learning Outcomes
By the end of the course, students will be able to:
- Articulate the common environmental and social impacts of natural hazards and disasters, and how these impacts may evolve due to climate change.
- Explain key concepts in remote sensing, including radiometry, spectral signatures, and the electromagnetic spectrum, and identify key components of the history and development of the field (including more recent developments like critical remote sensing)
- Employ JavaScript to access and manipulate Google Earth Engine’s API for visualizing and analyzing gridded data products related to natural hazards and disasters.
- Apply essential remote sensing analytical techniques, such as image classification, calculating indices, change detection, and temporal analysis, to real-world scenarios involving natural hazards.
- Develop and refine effective techniques to present and interact with their analysis results, including through oral presentations, writing, and the use of Google Earth Engine Applications.
Grade Breakdown
Class Participation (25%): The participation component of the course grade will be made up of your individual in-class participation, as well as effort on pair-programming in-class exercises.
Labs (50%): There will be 6 labs that will be completed throughout the course. These assignments are designed to have students independently implement skills learned through course material and in-class exercises.
Project Tasks (10%): Project tasks will allow students to iteratively develop their final projects throughout the course of the semester. Each task will allow students to complete a portion of their final project and allow the instructor to give feedback throughout the semester. 70% of the task grade will be based on quality of the material, the other 30% will be based on self-reflection of contribution to the group for each project task.
Final Project (10%): The final project will be a polished version of the completed project tasks that also implement any instructor feedback given on individual project tasks. 70% of the final project will be based on quality of the material, the other 30% will be based on self-reflection of contribution to the final project
Final Presentation (5%): Groups will give a 15-20 minute presentation on their final project during the course exam period.
Grade Scale
| Grade | Percentage |
|---|---|
| A | 93.5% + |
| A- | 89.5-93.4% |
| B+ | 86.5-89.4% |
| B | 82.5-86.4% |
| B- | 79.5-82.4% |
| C+ | 76.5-79.4% |
| C | 72.5-76.4% |
| C- | 69.5-72.4% |
| D+ | 66.5-69.4% |
| D | 59.5-66.4% |
| F | < 59.4% |