Building Skills for the Future: Teaching High School Students to Utilize Remote Sensing of Wildfires
Abstract
:1. Introduction
2. Materials and Methods
2.1. Scientific Topic and RS Technology
2.2. Training and Learning with Data
2.3. Training and Work-Related Activities
3. Results
3.1. Project Outcome
3.2. Educational and Work Related Learning Outcome
3.3. Students’ Perspective on the Training–Work Experience
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Project | Cohort 1 (2017) | Cohort 2 (2018) | Cohort 3 (2019) |
---|---|---|---|
Title | Earth observation with satellite: the case of burned areas | Earth observation with satellite: the fire perimeter | Earth observation with satellite: validation of fire detection system |
Pre-requisite | Ability to engage with new software, web basic, curiosity | Ability to engage with new software, web basic, curiosity | Accuracy, precision, ability to engage with new software, web basic |
Timeline | 40 h in person + work from home (March–May) | 40 h in person + work from home (Jan–May) | 40 h in person + work from home (Jan–March) |
Deliverable | Burn areas | Burn perimeter | Fire classes validation |
Class | 4th year | 4th year | 5th * year |
No. of students | 5 | 5 | 3 |
Tutors | 2 | 1 | 1 |
Activity | Cohort 1 (2017) | Cohort 2 (2018) | Cohort 3 (2019) | Aim |
---|---|---|---|---|
Module introduction talk | YES | YES | NO 1 | Give an overview of expected work and outcome |
Pre-requisite assessment | Questionnaire | Questionnaire | Set in advance | To check good matching and level of technical preparation |
Theory of remote sensing (lectures) | 50% traditional approach 50% interactive (videos, questioning, websites, etc.) | 20% traditional approach 80% interactive (videos, questioning, websites, etc.) and direct instruction | 20% traditional approach 60% interactive (videos, questioning, websites, etc.) and direct instruction 20% mentoring | Providing the basic knowledge of RS, algorithms and technology to be used in the project to understand the sensors and satellite technology |
Active learning | Practicals on SNAP Toolbox functions Additional self-learning with online tutorial To give a presentation PowerPoint basics | Practicals on SNAP Toolbox functions Google Earth To give a presentation PowerPoint basics | Practicals on WebGIS interface, Google Earth, Word, Excel | To learn how to visualize and manipulate data To understand what the imagery has to say To learn to use the tools useful to accomplish the assigned tasks |
Academic ethic and integrity | Lectures | Lectures | Lectures | To understand integrity, plagiarism copyright and credits, images |
Guided analysis | To select the test case To implement algorithms To understand the data | To select the data set To implement algorithms To understand the data and the technical challenges | To understand images To implement data validation criteria To report the results | To support and mentor the students |
Expected result | To produce a burned-area map with the Sentinel 2 (NDVI 2 and NBR 3) Compile and give a presentation atschool | To extract burn-scar perimeters To present the result through Google Earth Compile and give a presentation at school | To verify hot-spot categories To report significant data in Excel and Word | High score in assessment criteria Produce good-quality results |
Assessment | Testimonials MIUR criteria | Questionnaires + testimonials MIUR criteria | Questionnaire + testimonial MIUR criteria | High score in MIUR assessment |
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Amici, S.; Tesar, M. Building Skills for the Future: Teaching High School Students to Utilize Remote Sensing of Wildfires. Remote Sens. 2020, 12, 3635. https://doi.org/10.3390/rs12213635
Amici S, Tesar M. Building Skills for the Future: Teaching High School Students to Utilize Remote Sensing of Wildfires. Remote Sensing. 2020; 12(21):3635. https://doi.org/10.3390/rs12213635
Chicago/Turabian StyleAmici, Stefania, and Marek Tesar. 2020. "Building Skills for the Future: Teaching High School Students to Utilize Remote Sensing of Wildfires" Remote Sensing 12, no. 21: 3635. https://doi.org/10.3390/rs12213635