Building an Online Learning Module for Satellite Remote Sensing Applications in Hydrologic Science
"> Figure 1
<p>Bloom’s Taxonomy adopted from the Vanderbilt University Center for Teaching.</p> "> Figure 2
<p>(<b>a</b>–<b>c</b>) Example of a drag-and-drop question in <a href="#sec3-remotesensing-12-03009" class="html-sec">Section 3</a> to test students’ ability to choose the appropriate spatial resolution according to a specific hydrologic application. The images are adopted from Reference [<a href="#B35-remotesensing-12-03009" class="html-bibr">35</a>].</p> "> Figure 3
<p>Example of the students’ work for the learning activity in <a href="#sec2-remotesensing-12-03009" class="html-sec">Section 2</a>—Temporal Dimension of Satellite Data, in which they identify trends in time series of satellite Terrestrial Water Storage observations. This example is for a location in Greenland and clearly shows a decreasing trend, as well as a cyclic pattern.</p> ">
Abstract
:1. Introduction
2. HydroLearn
3. The Design Approach
4. Instructional Content
5. Learning Activities
5.1. Check Your Understanding
5.2. Authentic Tasks
6. Discussion
7. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Section in Module | Learning Objectives (The Student Will Be Able To) | Learning Level (As Defined by the Bloom’s Taxonomy) |
---|---|---|
1. Fundamentals of Satellite Remote Sensing for Hydrology |
| |
2. The Temporal Dimension of Satellite Data |
| |
3. The Spatial Dimension of Satellite Data |
| |
4. Uncertainty and Error Assessment |
| |
Section in Module | Main Topics and Tools | Learning Activities |
---|---|---|
1. Fundamentals of Satellite Remote Sensing for Hydrology | Definitions, concepts, and characteristics of remote sensing images and data acquisition. The electromagnetic spectrum. The main steps of the remote sensing process. |
|
2. The Temporal Dimension of Satellite Data | Concepts and definitions of temporal resolution and data latency, time series analysis, cyclic variations, and significant trends. Linear regression and hypothesis testing. Online Visualization Tool from the Colorado Center for Astrodynamics Research. |
|
3. The Spatial Dimension of Satellite Data | Concepts and definitions of spatial resolution, image interpretation, and change detection. Spatial statistical analysis tools. Worldview online visualization tool. |
|
4. Uncertainty and Error Assessment | Uncertainties and errors in remote sensing observations, continuous and categorical verification metrics. Worldview and Giovanni online visualization tools. |
|
Learning Objective | Criterion | Level #1 (Does Not Meet Expectations) 0 Points | Level #2 (Meets Expectations) 4–7 Points | Level #3 (Exceeds Expectations) 10 Points |
---|---|---|---|---|
Objective #1: The student will be able to visualize and download satellite data. | Criterion #1: Download and create a maps using a visualization tool | Not included | Plots included, but incorrect. Or, one or more plots are missing. Or, plots are not properly labeled. | All plots are included, correct, and properly labeled. |
Objective #2: Given a specific event and a satellite image/product, the student will be able to quantify and interpret changes in a hydrologic variable. | Criterion #1: Compute basic statistics and plot histograms | Not included | Values are included, but are incorrect or incomplete. Or, histograms are incorrect, incomplete, or not properly labeled. | All values and histograms are correct, complete, and presented in an organized manner. |
Objective #2 | Criterion #2: Discussion and interpretation of maps and statistics | Not included | Part of the discussion and/or interpretation of results and maps is either missing or incomplete or incorrect. | Discussion and interpretation of results and maps are complete, correct, and well organized. |
Objective #2 | Criterion #3: Quantify damage to vegetation caused by a hurricane and hydrologic implications | Not included | Damage quantification and/or related discussion are either incorrect or incomplete. | Damage quantification and related discussion are complete and correct. |
Learning Objective | Criterion | Level #1 (Does Not Meet Expectations) 0 points | Level #2 (Meets Expectations) 4–7 Points | Level #3 (Exceeds Expectations) 10 Points |
---|---|---|---|---|
Objective #1: The student will be able to identify and apply techniques for assessing uncertainties and errors. | Criterion #1: Identify verification metrics and compute basic statistics | Not included | Values are included but are incorrect or incomplete. | All values are correct, complete, and presented in an organized manner. |
Objective #2: Analyze remote sensing data and evaluate their performance. | Criterion #1: Plot time series and scatterplots | Not included | Plots are included, but are incorrect or incomplete. Or, plots are incorrect, incomplete, or not properly labeled. | All plots are included, correct and properly labeled. |
Objective #2 | Criterion #2: Compute basic statistics | Not included | Values are included but are incorrect or incomplete. | All values are correct, complete, and presented in an organized manner. |
Objective #3: Formulate and test hypotheses around causes and effects in hydrologic variables, based on uncertainty and error analyses | Criterion #1: Discussion and interpretation of plots and statistics | Not included | Part of the discussion and/or interpretation of results and plots is either missing or incomplete or incorrect. | Discussion and interpretation of results and plots are complete, correct, and well organized. |
Objective #3 | Criterion #2: Identify remote sensing datasets whose dynamics might be cross related | Not included | Other variables are identified but the related discussion is either incorrect or incomplete. | Variables are identified and related discussion are complete and correct. |
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Maggioni, V.; Girotto, M.; Habib, E.; Gallagher, M.A. Building an Online Learning Module for Satellite Remote Sensing Applications in Hydrologic Science. Remote Sens. 2020, 12, 3009. https://doi.org/10.3390/rs12183009
Maggioni V, Girotto M, Habib E, Gallagher MA. Building an Online Learning Module for Satellite Remote Sensing Applications in Hydrologic Science. Remote Sensing. 2020; 12(18):3009. https://doi.org/10.3390/rs12183009
Chicago/Turabian StyleMaggioni, Viviana, Manuela Girotto, Emad Habib, and Melissa A. Gallagher. 2020. "Building an Online Learning Module for Satellite Remote Sensing Applications in Hydrologic Science" Remote Sensing 12, no. 18: 3009. https://doi.org/10.3390/rs12183009