Teaching Units for the ICCARS Project

Unit Design

There have been a few questions that have been coming up regarding the Climate Change units that are due on April 8, 2011.  The blog is a good place to discuss the unit.  Please share your comments and suggestions.  This will be helpful so that everyone can learn from each other.

Please keep in mind that the unit you turn in on April 8 may look different from its final form in 2012.  You will be able to adjust your unit as you use it and as your thinking changes.  We will have a template within the next month that we will be using.  As long as you are doing your work in Word format (Office, Pages, etc.), you will not have a problem with the template.  It will just be a matter of cutting and pasting.  The work that you share does not have to be original work.  You are not expected to design brand new activities to do with students.  But please cite all of your work.  The goal of these units is to provide teachers, outside of our project, with units that they can use with their students.

There are three topics that need that must be included within each unit:

•         Climate Change
•         Remote Sensing
•        NASA Data

There are two reasons why these topics must be included.  First, it is a requirement of the grant.  But secondly, in order to study climate change, the other topics are necessary.  In order to study climate change we need to collect data.  There are many ways to collect data.  Remote sensing is the act of collecting data about an object without physically contacting the object.  Our students will collect data from their local environment, which in turn can be added to the global database.  Probably the largest database comes from NASA.  We want students to know where they can turn to, in order to use data to support their conclusions.

As the units are developed, the process of inquiry should be clearly identified.  Inquiry will look very different in classrooms.  The sad part is that sometimes it is absent in a classroom.  Inquiry can be teacher directed, learner directed, or somewhere in between.  You are encouraged to look at the Inquiry Continuum to see where your classroom fits in.  Please visit:

http://www.docstoc.com/docs/11519881/Inquiry-continuum

to view the continuum. 

Another source  to view is the ACT College Readiness Standards.  It will provide you with guidance on what students should know about interpreting data, scientific investigations, and evaluating models, inference, and exponential results.  You can view the standards at:

http://www.act.org/standard/planact/science/index.html

Finally, the 5 E’s should be prevalent within your lessons.  The 5 E Instructional Model will promote the practice of inquiry.  The 5 E’s are Engage, Explore, Explain, Enrich, and Evaluate.  Normally it takes 3-5 classroom periods to get through the 5 E’s.  You are encouraged to visit:

http://www.bscs.org/curriculumdevelopment/features/bscs5es.html

This is the BSCS website where you can learn more about the instructional model.  Even NASA encourages teachers to use the 5 E model, as you can see at:

http://www.nasa.gov/audience/foreducators/nasaeclips/5eteachingmodels/index.html

You can download a pdf document at:

learningcenter.nsta.org/files/PB186X-4.pdf

which will allow you to view all of the components of each of the 5 E’s.  Even though the document was written for an elementary book, the chart on the 4^th and 5^th page provides the BSCS document for the BSCS 5 E Teacher and Student.  It should provide you with all of the information you need to understand each of the E’s.

Now for the unit!  You need to make sure that all of the following information is included (much of this has already been turned in, but now you are putting everything together into one document):

1. Title of Unit
2. Aligned list of the Standards, Statements, and or Expectations.
3. List of the essential content that you want students to have.
4. Major Understanding (s) and/or Minor Understandings.
5. Essential Questions that need to be answered during the unit.
6. List of instructional materials that you will be using in the unit.
7. Day by day lesson plans, including:
1. Pre/Post tests
2. Instructional Activities (aligned to the 5E’s)
3. Assessments and Evaluations

As you design your day-by-day lesson plans, you do not need complete lesson plans, but you need enough description so that others would have a clear idea of what they would have to do to mirror your instruction.

Tom Green has developed a unit on the Carbon Cycle, that you may want to use.  He will be making instructional videos to help explain how to implement them into your curriculum.  Please visit:

http://resa.net/curriculum/curriculum/science/professionaldevelopment/climatechange/carboncycle/

Please share you comments and your ideas.  The goal of this unit is twofold:

• To provide instructional units for other teachers so that they can more effectively teach climate change to their students.
• Provide you with the opportunity to show the quality of work that you do in the classroom.  It is your chance to be published.

Vegetation Indices and NDVI

Some remotely sensed imagery such as Landsat images are very well suited to identifying land-use and land-cover (LULC) types on medium scales. In our project we are also using 4-band AEROKATS TwinCam imagery to identify local LULC types at much finer spatial resolutions. Through the use of supervised or unsupervised classification methods in image processing software, it is possible to break up images into discrete classes that can then be quantified and subjected to statistical analysis.

Another method for understanding the imagery is though the identification of photosynthetically active, healthy green vegetation. Healthy green vegetation absorbs a high percentage of Photosynthetically Active Radiation (PAR) in the visible portion of the EM spectrum - roughly from 0.4 - 0.7µm (or 400 - 700nm).

In healthy green vegetation, chloroplasts in the outer palisade mesophyll layer of the leaves contain the pigment chlorophyll. Chlorophyll pigment controls the plant's absorption of visible light. This absorption is particularly high in the blue (around 0.4 - 0.5µm) and red (0.6 - 0.7µm) portions of the spectrum. The red and blue wavelengths are converted by the chloroplasts into food for the plant. Green light (~ 0.5 - 0.6µm) has a lower absorption rate (therefore higher reflectance), resulting in the overall green appearance of healthy plants. Reflectance percentage (or albedo) in the red and blue wavelengths is generally well below 5%, with green around 10%.

A very different thing happens to the light in the near-infrared (NIR) portion of the spectrum, (~0.7 - 1.1µm). These longer waves penetrate deep into the leaf, and are reflected by the cellular structure of the spongey mesophyll near the back wall of the leaf. Reflectance (or albedo) ranges around 50-60% in the NIR.

If plants become stressed and leaves begin to desiccate less visible light is absorbed to make food and less near-infrared radiation is reflected. Therefore the ratio of reflectance in the visible versus reflectance in the NIR ranges begins to change.

A simple ratio (SR) can be used to describe this:

SR = VIS/NIR

Because red acts as a good proxy for the visible portion of the spectrum as a whole we can use it to rewrite the simple ratio:

SR = R/NIR

To compensate for shadows and variations in slope in some terrains, and to avoid extreme numeric ranges in the result, another index was developed - the Normalized Difference Vegetation Index (NDVI). Here the difference between the NIR and VIS reflectance is divided (or normalized) by the total reflectance in those ranges:

NDVI = (NIR-R)/(NIR+R)

This ratio is calculated on every pixel in the image using the red and NIR bands. The result of this calculation is always a value between -1 and +1. The closer the value is to +1, the more likely the target pixel is healthy photosynthetically active vegetation. The closer it is to 0 or -1, the less likely it is to be healthy vegetation.

NDVI is useful for calculating biomass and primary production. It is also useful for monitoring green-up and green-down, as well as changes in the occurrence times of each over different years. NDVI is employed in monitoring drought and desertification as well.

Sources and more information:

NASA/GSFC Remote Sensing Tutorial (RSt), Primary Author: Nicholas M. Short, Sr.

http://rst.gsfc.nasa.gov/Sect3/Sect3_1.html

Measuring Vegetation (NDVI & EVI), NASA Earth Observatory,

http://earthobservatory.nasa.gov/Features/MeasuringVegetation/measuring_vegetation_2.php

Introductory Digital Image Processing - A Remote Sensing Perspective, John R. Jensen 2005

Global System Science - ABCs of Digital Earth Watch Software, Lawrence Hall of Science, UC-Berkley

http://lawrencehallofscience.org/gss/

Summary of the ICCARS PLC Webconference on Monday, February 7, 2011

Review of the ICCARS PLC Webconference on Monday, February 7, 2011

Present:  Laura Amatulli, Lynn Bradley, Wanda Bryant, Russ Columbus, Erica Conley-Shannon, Greg Dombro, Jennifer Gorsline, Tome Green, Dan Neil, Kathleen O’Connor, Deena Parks, John Rama, Darcie Ruby, and Bruce Szczechowski

Absent:  John Bayerl and Caroline Chuby

The agenda for the meeting will follow, but before the agenda is listed, there was information that was shared from the group and we want that listed here, so that everyone can view the information quickly:

Free viewing of the PBS Program—Secrets Beneath the Ice.  Please visit:

http://www.pbs.org/wgbh/nova/earth/secrets-beneath-ice.html

EarthKAMM – Earth Knowledge Acquired by Middle School Students is an educational outreach program allowing middle school students to take pictures of our Earth from a digital camera on board the International Space Station.  For more information or to register for the April 5-8 EarthKAMM mission, please visit:

https://earthkam.ucsd.edu/

The Climate Literacy and Energy Awareness Network:

http://cleanet.org/

was again mentioned as a really good resource educational resources.

A great site to check out for student projects is Think Quest, which can be found at:

http://www.thinkquest.org/en/

eCyberMission is a free, web-based science, technology, engineering and math competition for students in grades six through nine.  The deadline to enter is March 3.   For more information, please visit:

https://www.ecybermission.com/public/Login.aspx?ReturnUrl=%2fDefault.aspx

Agenda:

1.     Updates – New Picasa Photos, CTN Grant Update, E-Rate Grant Update, Michigan Climate Coalition, ICCARS Calendar, ICCARS Pre/Post Test Developed based on Yale Study, Blog

2.     Important Dates:  The 2011 Climate Summit will take place on April 27 from 4:00 – 8:00 and the Summer Training for ICCARS will take place August 8 – 12 at Wayne RESA.

3.     Lifelines is offering two optional webinars—February 23 and March 16 from 6:00 – 7:00.   Possible collaborations among teachers may be coming.

4.     Additional Resources/Opportunities: Thacher Environmental Grants; GLOBE Student Climate Research Campaign, NASA Explorer Schools.

5.     iPad Updates:  Movie—Secrets Beneath the Ice; Book—Frasers Penguins; new eBook from Andy Henry on Image Processing.

6.     Assessments have been collected and are being reviewed.

7.     Next Assignment Due April 11 – Full Unit Development

8.     Climate Change Science – Discussion of Climate Change an Winter Storms

9.     Remote Sensing – Satellite Meteorology focusing in on the CERES, GOES, and POES.

Thoughts on the relationship between weather, climate, and global warming

After last week's snow storm there was a lot of noise about whether this was caused by climate change, whether it refuted global warming, or if it was just weather in the Midwest. This brought me back to how much difficulty there is in understanding the relationship between weather, climate change and global warming. Without getting into whether we are observing a natural or man-made phenomena, I am going to take a stab at this clarifying these relationships.

• The underlying phenomena driving climate change is global warming – the Earth is heating up. This is a simple, verifiable fact.
• This heating occurs unevenly, largely do to local and regional variables, (e.g., air and ocean currents, atmospheric moisture, relative albedo, carbon sequestration, urban heat islands, etc.).
• As the Earth gets warmer, ocean and air currents change, distributing heat and moisture in new patterns. Warmer oceans also mean more moisture is available to the atmosphere.
• We experience these changes in our daily lives through weather, which is highly sensitive to such forcings.
• Over longer periods, these effects produce changes in climate, which can dramatically alter the physical and biological characteristics of place on local, regional and global scales.
• These changes in climate can also create feedback that amplifies these effects locally, regionally and globally.

More concisely, the warming of the planet causes changes in distribution of heat and moisture through weather. These patterns, sustained over time, cause changes in climate. Changes in climate in turn can create feedback that further impacts weather patterns.

And so on...

When we talk about the response of weather to global warming, we are talking about a large and nonlinear system (weather) responding to an influx of additional energy (global warming). Even though the increase in energy input may be gradual and seemingly small, the system response can be chaotic and severe.

By itself, the storm tells us very little about global warming and climate change. However, as part of a larger pattern of increasingly severe and unusual storms, heat-waves, droughts, and floods - all set against a background of record setting global temperatures - last weeks storm is entirely consistent with what we should be expecting to see. That is, just about anything.

As an added note, evidence suggests that historically, dramatic shifts in global climate have not tended to occur gradually, but quite often occur in time periods as short as a decade or less. Again, this is not inconsistent with non-linear systems.

Driving Questions

Driving Questions from http://pbl-online.org

The Driving Question is central to the inquiry process and must come before deciding on the project or unit activities.  The natural outcome of the project or unit is that it is driven by the Driving Question. 

A good Driving Question makes a unit or project intriguing, complex, and problematic.  Although standard classroom assignments, like story problems and essays, pose questions that students must answer, a Driving Question requires multiple activities and the synthesis of different types of information before it can be answered. 

It brings cohernce to disparate unit or project activities and serves as a “lighthouse” that promotes student interest and directs students toward the units or project’s goals and objectives.

The Driving Questions should address authentic concern.  For example, when creating the Driving Question it is useful to ask yourself: “Where is the content I am trying to teach used in the real world?”  Although it is usually easier to focus students’ attention on a single question, some topics will require multiple Driving Questions.

Driving Questions are:  provocative; open-ended; go to the heart of a discipline or topic; challenging; can arise from real world dilemmas that students find interesting; and are consistent with curricular standards and frameworks. 

Take the quiz at: http://pbl-online.org/driving_question/dqtest/dqtest.html  to determine how much you understand Driving Questions.

In the ICCARS project, there are three things that should come through in any Driving Question for this project:  it should focus in on climate change; requires students to have some understanding of remote sensing to provide evidence to their answer; and utilize NASA data in some way.

Here are some examples of Driving Questions that have been turned in so far.  What do you think of them?  Share your comments below:

1. How can we, your family and society, lessen our impact on Global Climate Change and what data supports these solutions?
2. How can you as a student use simple tools and instruments (remote sensing) to gather data and evidence to draw conclusions about patterns of weather and climate in your own environment?
• What kind of correlation exists between altitude and weather statistics such as temperature and wind speed?  Will this correlation be consistent as weather conditions change?
• How do human activities affect climate change such as global warming?
• How does climate change affect living organisms in Dearborn, MI?
• How can you explain the relationship between weather and climate and how does this relationship contribute to climate change?
3. How is climate change and global warming evident in Southgate?
4. Multiple Driving Questions, but still needs a main one.
• How do changes in land use affect local water quality?
• How do changes in land use affect local temperature and precipitation?
• How do changes in land use affect carbon sequestering?
• How do changes in land use affect air quality?
• How do changes in land use affect biodiversity?
5. How can we use kites equipped with weather sensors and cameras to learn about how animals adapt to climate change?
• How does a kite fly on a windy day?
• How does our team work together safely to fly large kites equipped with remote sensing devices?
• What information can we gather with remote sensing devices and what can it tell us about animals and their habitats?
6. What’s in the Atmosphere and Why Should I Care?
• How does the composition of the earth's atmosphere affect its properties and behavior?
• How does solar radiation influence conditions on earth?
• How can scientific tools help us understand human impact on the atmosphere?
7. Why does Avondale Middle School and it's local area look the way it does?
8. How can you lessen your Carbon Footprint, here in Detroit?
• What role does carbon dioxide play in global warming?
• How do cardon dioxide levels in Detroit compare to levels in other areas?
• How do our actions impact carbon dioxide levels?
• What can we do at the individual level to reduce carbon dioxide levels?  At the local level?  At the global level?

Summary of the December 13, 2010 ICCARS PLC Teleconference

Equipment Used: Audio—Phone Conference provided by Wayne RESA; Video—Adobe Connect, provided by Wayne RESA

You can listen and view the Videoconference by visiting:
http://remc.adobeconnect.com/p47275523

4:15 – 4:30 – Login

4:30 – 6:00 – Teleconference

Attendees: John Bayerl, Lynn Bradley, Wanda Bryant, Russell Columbus, Erica Conley-Shannon, Greg Dombro, Jennifer Gorsline, Tom Green, Dan Neil, Kathleen O’Connor, Deena Parks, John Rama, Darcie Ruby, Bruce Szczechowski, and Yichun Xie.

Absent: Laura Amatulli and Caroline Chuby

Hosts: David Bydlowski and Andy Henry



Agenda and Notes:

1. Audio Test

2. Update on our participation in the Michigan Climate Coalition and Kathleen O’Connor’s participation in the Condition 1 project.

3. Update on iPad Update to 4.2.1 and the use of RSS Readers.

4. Review of Units that were turned in on December 10.

a. Theme, Time Span, Alignment to Standards, Identification of Key Knowledge and Skills

b. Units are posted at: http://geodata.acad.emich.edu/iccars/ in Resources and then Lesson Plans

5. Next Assignment – Write the Driving Questions for each unit. Due January 7.

6. Group Sharing

a. Kathleen spoke about her participation in Condition 1.

b. No major issues with the iPad Update or RSS Readers

c. Many groups commented on their units. A few questions were asked about Driving Questions. In particular—how many driving questions are appropriate within a unit.

7. Information about Climate Change based on the Yale Report-America’s Knowledge of Climate Change and the presentation given at the 2010 UN Climate Conference.

a. It was recommended that participants download the report and possibly use some of the questions with their students. It also provides a way of getting a better understanding of the misconceptions associated with climate change.

b. http://environment.yale.edu/climate/news/knowledge-of-climate-change

c. Yale presentation at the UN Climate Conference: http://environment.yale.edu/climate

d. Global Warming’s “Six Americans.” – What they think, why they think, and the questions they would ask.

8. Group Sharing—participants said the information was enlightening and informative.

9. Information about Remote Sensing

a. Remote Processing Process – Statement of the problem; Identification of In Situ and Remote Sensing Data Requirements; Remote Sensing Data Collection; Remote Sensing Data Analysis; Information Presentation

b. AEROKATS TwinCam Image Processing/Classification Steps – Acquire Imagery from Sensor; Preprocess Imagery; Process Imagery-Supervised Classification

c. John Rama spoke to the problems that he has had in the process to help others see what problems can arise.

d. MultiSpec Tutorials

e. Earth Observation Systems – NASA Global Climate Change Website and NASA/JPL Eyes on the Earth 3D

f. http://climate.nasa.gov/index.cfm

g. http://climate.nasa.gov/Eyes/eyes.html

h. Categories of EOS Missions—14 Satellites (8 atmosphere; 2 oceans; 4 land)

i. EOS Data Sources – NASA/GSFC Global Change Master Directory; CEOS Climate Diagnostics; USGS Earth Explorer; USGS GloVis and MODIS Web

j. http://gcmd.nasa.gov

k. http://idn.ceos.org

l. http://edcsns17.cr.usgs.gov/EarthExplorer

m. http://glovis.usgs.gov

n. http://modis.gsfc.nasa.gov

10. Group Sharing—Majority of the discussion centered around the difficulty of the process and that many had a general understanding but not a working understanding. It was also stated that there is a disconnect between remote sensing and climate change, in terms of understanding. Some participants suggested that we meet over the Holiday Break to work on Image Processing and related issues. Wednesday, December 22 from 9:00 – 3:00 was selected as the date to do this, at Wayne RESA.

11. Next PLC Teleconference will take place at 4:30 (EST) on January 10, 2011.

Editors Note: The PLC teleconference went pretty smooth. The major problem was that it went 30 minutes too long. As hosts, we have to work on working within the time constraints. But special thanks go out to all of the participants who not only stayed on, but actively participated. It is also very impressive that the group wanted to meet on their own time, to improve their skills and understanding.

Image Processing Question

John Rama had a question about the image processing exercise that I thought might be valuable for sharing with the group.

"The attached word file has the MultiSpec Image and what I think is the Statistical Analysis that goes with this image. I would like to have a discussion about what it all means. It may be so poorly done that it is not worth discussing. I would like to see something more than just pretty colors but need help understanding what information can be pulled from the analysis - even if it is information that says this image is not useable. Thoughts.
John R."



Hi John, I agree, thank you for your effort here. It is definitely worth discussing.

The information on the left refers to the statistical probability that each pixel in the category is classified correctly. In a supervised classification model (where you "train" the classification by selecting representative samples first) you determine the classification scheme. In a unsupervised classification, the program automatically classifies the image into a predetermined number of categories based on a sampling method and no training samples.

It looks like you ran your classification process properly. The issue I see here is that the visible and NIR images were not registered (spatially aligned) to each other. If you look carefully you will see that the house appears twice in the image, with a considerable offset. Once you see this you will probably recognize that all of the features maintain this offset. This means that any given pixel in the composite image will contain information from two different features (e.g., house/field, road/trees, etc.). Any statistical analysis of the pixels will therefor not be a valid representation of the site on the ground.

Also don't forget that when properly registered, the part of the image that extends beyond the overlap must be trimmed off in your image processing software. Pixels that contain information from only one image will distort the calculations from the overall image.

As for the statistics:

Classification of Training Fields provides information about the likelihood that a pixel in your selection was actually representative of the overall selection (a patch of dirt in a selection of grass will read as incorrect because it is not representative of the rest of the selection).

If you look at the column 1 "Grass", you will see that there are 19776 pixels in the training selection that are identified as Class Number 1, 0 are identified as Class Number 2, 65 are identified as Class Number 3, 8 are identified as Class Number 4, 8 are identified as Class Number 5, and 74 are identified as Class Number 6. This means that your training selection for "Grass" also contained what the system identified as 65 pixels of "Road", 8 pixels of "Car", 8 pixels of "Trees", and 74 pixels of "Field".

Training Class Performance tells you how the pixels in the image ended up being classified based on the training selections. Note that the Total and Reliability Accuracy rows at the bottom of the table are not properly aligned with the columns above. They need to shift left.

Class Distribution for Selected Area displays the actual results of the entire image classification. You can see here how many pixels of each class were identified, and the percentage of the total image that they comprise.

Again, because your images were not aligned, the classes do not represent the real classes on the ground because the pixels are mixed. If you go back and align and crop the images you should get much more accurate results.

Classification of images is very important because it allows us to quantify the data and make it available for analysis. It is only one of many things we can do with the images. We will post another tutorial next week on producing a vegetation index, which can tell you how much photosynthetic vegetation is in the image, as well as the status of photosynthetic plants.


I hope this is helpful, and not too confusing. Thoughts or questions?