Unit 2: Examining the Distribution of Mass Wasting Events

Unit 2 Learning Outcomes

×

Students preforming terrestrial lidar scanning along a normal fault. In this unit, lidar data is used to calculate landslide susceptibility index values for various geospatial factors. Image from Bruce Douglas.

Provenance: From Bruce Douglas (Indiana University)
Reuse: This item is offered under a Creative Commons Attribution-NonCommercial-ShareAlike license http://creativecommons.org/licenses/by-nc-sa/3.0/ You may reuse this item for non-commercial purposes as long as you provide attribution and offer any derivative works under a similar license.

Students will:

• Describe the importance of pattern prediction for hazard mitigation and explain patterns of mass movements and limitations to prediction through short discussion-based exercises.
• Predict the relationship between geospatial factors (e.g. elevation, slope, precipitation, geology, etc.) and the frequency and distribution of mass movements for different geographic regions within small working groups.
• Select a qualitative or quantitative classification scheme for a geospatial factor based on knowledge of mass movement types and behaviors in a computational GIS environment (ArcMap).
• Quantitatively verify predictions using a frequency-ratio method to generate maps (for both regions) of landslide susceptibility indices (LSI) for a single factor and explain regional differences to the class through a short presentation.
• Compare and contrast the relationship between different factors and mass movements in different regions through a synthesis assignment, or discussion.

Show More info on how learning outcomes connect to science literacy principles and module goals

Hide

Unit 2 Teaching Objectives

• Cognitive: Facilitate consideration of the relationship between geospatial factors and the frequency and distribution of mass movements, and how these relationships change in different environments.
• Behavioral: Develop skills in implementing the scientific method (hypothesis, testing, analysis) and GIS skills—frequency-ratio analysis toward a Landslide Susceptibility Index (LSI).
• Affective: Encourage reflection on pathways to prediction.

This upper-level content is appropriate for junior- or senior-level courses in geohazards, geomorphology, GIS, remote sensing, and other geoscience courses where there may be an interest in developing skills in computational experimentation and early stage development of predictive models. This unit assumes that students have had physical geology and some upper-level exposure to mass movements types including exploration of slope failure mechanisms. Concepts of weathering, as it relates to physical and chemical properties of rocks, are also expected to have been taught prior to this unit. The content would also best be introduced after some ArcMap software skills have been practiced, so that students are familiar with the software, where to find tools, and map preparation. A brief introductory discussion to motivate exploration of material is presented in the form of discussion questions. This content can be executed as a jigsaw, where in small-enrollment courses individual, or partnered, student work is presented to the rest of the class, while in large-enrollment courses, a more traditional jigsaw experience can be utilized within more manageable working groups. This unit is meant to be a precursor to Unit 3: Develop and Test Models of Landslide Susceptibility, and it is necessary to complete this unit before attempting Unit 3.

Estimated time: Classroom (1–1.5 hours) and Laboratory (3 hours). Additional time may be needed depending on students' experience with GIS software and tools.

In Unit 1: Mass Wasting Identification and Quantification, students were introduced to methods of identifying landslides from digital imagery and other remotely sensed data. These methods are useful for constructing a landslide inventory, or a record of all known landslide events in a region. This concept of landslide inventories is a key concept that ties together Unit 1 and Unit 2 in the context of the larger module. Using existing landslide inventories, this unit will show students how to generate early stage landslide susceptibility maps of single factors as a means of "prediction."

Suggested reading in advance of this unit as review and brief introduction of new concepts: Appendix B of the USGS Landslide Handbook (Acrobat (PDF) 39.5MB Oct15 19).

Introduction

To motivate class discussion on the topic of prediction, as a means to recognize the importance of pattern prediction for hazard mitigation, a presentation slide with pattern recognition and prediction exercises and discussion prompts is provided. Additionally, this introductory discussion will include prompts for discussion that attempt to link student understanding of cognitive processes and the need for mathematical/computational modeling.

• Unit 2 Pattern Prediction and Susceptibility Presentation (PowerPoint 2007 (.pptx) 1.4MB Mar12 23)—This presentation helps structure an interactive lecture on why pattern recognition is critical to making predictions and quantitative analysis is critical to pattern recognition/prediction in any sort of complex setting (like geoscience). Further information and explanation are in the notes portion of the file to help guide discussion.
• Introduction to DEMs and Lidar (PowerPoint 2007 (.pptx) 19.1MB Mar10 23)—This could also be used to give students a background on the data sources that will be used, if it has not been used in Unit 1.

Learning about the Frequency-Ratio Method

Following the introduction, where a case has been made for quantitative and computational methods of analyzing patterns of geospatial data, students will now learn about a frequency-ratio method. This particular frequency-ratio method is used to calculate the Landslide Susceptibility Index (LSI). Please note that the term landslide is used to refer to all mass wasting event types.

• Unit 2 Frequency Ratio Method (LSI) (PowerPoint 2007 (.pptx) 863kB Mar12 23)—This presentation includes an introduction and methodology to calculating LSI values using a frequency-ratio method, and ends with discussion prompts toward building a hypothesis regarding the relationship between factors and mass wasting frequency.
• Unit 2 Arizona LSI Values (Microsoft Word 2007 (.docx) 204kB Mar12 23)—This document is a statewide analysis of LSI values for Arizona. Instructors should use the information contained to motivate discussion on the use, classification, and impact of the factors presented in this document, focusing on why some factors are more or less related to the distribution of mass wasting events. This document is meant to supplement the last two slides of the frequency-ratio method presentation.

×

Provenance: Stephen Hughes, University of Puerto Rico-Mayaguez
Reuse: This item is offered under a Creative Commons Attribution-NonCommercial-ShareAlike license http://creativecommons.org/licenses/by-nc-sa/3.0/ You may reuse this item for non-commercial purposes as long as you provide attribution and offer any derivative works under a similar license.

Calculating LSI for a Region

In this section of the unit, students will be responsible for selecting classification schemes of factors and calculating LSI values. They will then explore the effect of a new classification scheme on LSI values. This will be done in two different regions—a subregion of Arizona and all of Puerto Rico—to encourage a compare/contrast analysis. Students will need to generate tables and maps to be used in a PowerPoint presentation that will be presented to the class. This presentation serves as a summative assessment for this unit.

In small courses (around 12 students), it is recommended that students be split into pairs that will explore a single factor for both regions. In larger courses (up to 24 students), it is recommended that student pairs each focus on a single factor for a single region. In courses where enrollment is quite large (more than 24 students), the subgroups for each factor can be increased, with an expectation that more classifications schemes are being tested.

Unit 2 Calculating and Analyzing LSI Student Exercise (Microsoft Word 2007 (.docx) 210kB Dec22 23)—This handout walks students through how to classify a factor and calculate LSI values for each class grouping, and guides students through experimenting with different classifications, comparing/contrasting their results, and selecting the best classification for different regions. The assignment assumes familiarity with ArcGIS software. Additionally, this document lists the elements of a short presentation needed for the summative assessment for this unit. These presentations are meant to jigsaw together the best classifications and LSI for each factor and region.

Unit 2 Calaculating and Analyzing LSI Student Exercise using QGIS (Microsoft Word 2007 (.docx) 345kB Dec22 23) - This handout provides instructions for the student exercise using QGIS. Disclaimer: QGIS is not able to perform LSI calculations. These instructions are only beneficial for classifying data. If students do not have access to ArcPro, then the final products from this assignment must be shared with them in order for them to move on to unit 3 using QGIS.

Unit 2 Calculating and Analyzing LSI Student Exercise Answer Key -- private instructor-only file

Hide

End of Unit Discussion

At the end of the assignment and group presentations, it is recommended that teachers guide their students through some discussion points that will help to provide a close to the unit, but also provide an opportunity for metacognition. Suggested discussion questions are below:

1. In region X (Arizona or Puerto Rico), what factor has the greatest correlation with slope failures? Why do you think this is?
2. If you had to rank each factor for each region, how would you rank them? Start with most favorable (high LSI) and work your way down.
3. Was the class's initial hypothesis correct? Why, or why not?
4. Think about your home. What factors do you think might have the highest LSI values?
5. What other factors, beyond the six tested in this unit, would you want to consider? Why?
6. Do you think any other classification may have resulted in a better LSI estimate? Why or why not?
7. This unit introduces the initial part of predictive modeling (continued in Unit 3). How simple do you think predictive modeling is now? NOTE: think about how many classifications you tried versus how many you might have tried to get the best results?

A guide/key for the discussion prompts are provided here: Unit 2 - End of Unit Discussion Key (Microsoft Word 2007 (.docx) 202kB Mar12 23)

Unit 2 Datasets

The essential datasets necessary for completion of Unit 2 are the LSI_Calculation toolbox, Arizona data, and Puerto Rico data. Sample completed LSI raster datasets that could be referenced by the instructor are included in the Unit 3 web page Datasets section.

• LSI_Calculation Toolbox (Zip Archive 3kB Jan9 19)—Toolbox that calculates the LSI for classified rasters.
• AZ_Data (Zip Archive 1055MB Jan9 19)—Arizona dataset including elevation (ASTER GDEMV002 30m resolution from EarthExplorer: https://earthexplorer.usgs.gov/), slope (derived from elevation), aspect (derived from elevation), landcover (from the Multi-Resolution Land Characteristics Consortium: https://www.mrlc.gov/), mean annual precipitation (raster generated from NOAA weather station using 10-year normals: https://www.ncdc.noaa.gov), and lithology (from the USGS online spatial data repository: https://mrdata.usgs.gov/geology/). Landslide points were generated from an inventory of landslide polygons courtesy of the Arizona Geological Survey.
• Land Use And Land Cover Classification System For Use With Remote Sensor Data, USGS, Anderson et al 1976 (Acrobat (PDF) 334kB Jan9 19)—Classification used for Arizona landcover dataset.
• PR_Data (Zip Archive 125.5MB Nov7 19)—Puerto Rico dataset including elevation (ASTER GDEMV002 30m resolution from EarthExplorer: https://earthexplorer.usgs.gov/), slope (derived from elevation), aspect (derived from elevation), landcover (from the Multi-Resolution Land Characteristics Consortium: https://www.mrlc.gov/), mean annual precipitation (Fick, S.E. and R.J. Hijmans, 2017. Worldclim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology.), and lithology (http://www.agencias.pr.gov/agencias/gis/descargaGeodatos/ambientales/Pages/Geologia.aspx).
• Puerto Rico Landcover Classification Codes.xlsx (Excel 2007 (.xlsx) 9kB Feb6 19)—Classification used for Puerto Rico landocver dataset meant to match that for Arizona.

• This unit uses ArcMap software. See Unit 1 for related links.
• Much time spent on this unit will be devoted to computer processing and data manipulation. Instructors should be patient with common student pitfalls while using GIS software that include: clicking too fast, confusion with projections, new file naming issues, and new file storage/organization issues. This unit is not meant to be an introduction to GIS, so some students may struggle with some operations. The instructor should be able to guide them through software hiccups. **Note: One common problem is that not all the ArcMap extensions are turned on.**
• For the assignment where students are generating LSI maps and tables, it is recommended that a common server geodatabase or folder be used. If this is not possible, make sure all outputs are saved into a single folder by the students. In Unit 3, where all the LSI maps are required, this folder can then be shared by using external drives with other students.
• Additional factors may be added to this unit, but to ensure ease of transition from Unit 2 to Unit 3, teachers should make sure that the pixel resolution and extent of the new datasets matches current datasets.
• Landcover and Lithology datasets for both regions are by far the most time-consuming to properly classify if students have limited GIS and Geology background, so teachers my choose to skip these datasets, or provide students with more time to process these datasets.
• Readings (see additional resources below) may be a good transition between Unit 2 and Unit 3. Have students read one of the papers (particularly the Chalkias et al., 2014 paper), between the two Units to tie the preparation input and early stage susceptiblity modeling (Unit 2) with susceptibility model assessment (Unit 3).

Formative Assessment

Formative assessment for this unit is recommended as observation and feedback on student conversations, questions, and group discussions. Additionally, further formative assessment can occur while students are working on the LSI assignment; the teacher may choose to briefly touch base with each group to provide guidance and correct any misconceptions. Student metacognition is presented as part of suggested group discussion guiding questions at the end of the unit (see above).

Summative Assessment

The student exercise (and associated presentation) is the summative assessment for Unit 2. Instructors may use this to evaluate how well students have grasped concepts and data manipulations to generate LSI tables and maps based on classification exploration. Instructors should feel free to modify this rubric and assign point values in a manner consistent with their course scheme.

Unit 2 Student Presentation Summative Assessment (Excel 2007 (.xlsx) 17kB Jan8 19)—This is the rubric for student exercise presentation. It is recommended that teachers provide a copy of the rubric to the students, or at least walk them through the grading criteria in advance of preparing their presentations.

Additional Resources for Instructors:

Provided here are some additional readings on LSI/Frequency-Ratio methods. Instructors may choose to have students read these papers in advance of the class/lab to further motivate discussion.

• Chalkias C., M. Ferentinou, and C. Polykretis, "GIS-Based Landslide Susceptibility Mapping on the Peloponnese Peninsula, Greece," Geosciences, 4, 176–190. (2014).
• Lee S., and B. Pradhan, "Probabilistic landslide hazards and risk mapping on Penang Island, Malaysia," Journal of Earth System Science, 115, 661–672. (2006).
• Lepore C., S. A. Kamal, P. Shanahan, and R. L. Bras "Rainfall-induced landslide susceptibility zonation of Puerto Rico," Environmental Earth Sciences, 66, 1667–1681. (2012).
• Li L., H. Lan, C. Guo, Y. Zhang, Q. Li, and Y. Wu "A modified frequency ratio method for landslide susceptibility assessment," Landslides, 14, 727–741. (2017).