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/
I understand the theory behind looking at the color of the pants. I am wondering how to apply this technique to a learning unit for middle school. What would be the best type of land here in Michigan to look at to show these types of color changes?
ReplyDeleteGood questions Kathleen.
ReplyDeleteAs for site selection, if you are using the AEROKATS system, imagery with a mix of surface features is good because you can measure and understand the contribution of different surfaces to total biomass, carbon sequestration, impervious surface, etc. You might also want to collect imagery from multiple locations with different land-cover characteristics.
Landsat and MODIS imagery is good for looking at larger scales and longer time series. The team at IGRE has assembled a very thorough, long term set of Landsat images covering SE Michigan. I believe that data is nearly ready for us to begin using. They have other imagery as well. As soon as we have access to the database we will schedule some training sessions so that you can have a chance to work with it.
Understanding photosynthesis is a good tie in to MS life sciences. This is a way of understanding more about the relationship between light and photosynthetic cells. Plants use light to make food, but not all light. Plants actually have to protect themselves from the portions of the EM spectrum that fall outside of the visible range. Most of the energy beyond 700 nm must either pass through the leaves of be reflected by them of the plants will desiccate and perish.
I think this gets to the crux of what makes 4 band (or more) imagery and remote sensing in general special. It is the addition of the infrared band in our imagery that allows us to distinguish between between healthy green and stressed green vegetation, or between astroturf and grass for example. What makes remote sensing valuable as that we can detect these signatures and measure them over areas at many different scales.
We can also use this imagery to understand more about the impact that human land use decisions have on the CO2 balance in the atmosphere by measuring changes in the abundance of biomass and the resulting changes in carbon sequestration potential. This is more meaningful on the scale of Landsat or MODIS imagery, but we can come to understand the process while working with AEROKATS images.
Another approach is to look at time series - measure the same location on a regular schedule throughout the spring to monitor green-up and the change in total photosynthetic biomass. How might an earlier green-up impact a growing season? Are plants more at risk in such scenario from frost? What about flower plants and the timing of the arrival of pollinating insects? Would the risk of drought increase in a longer growing season?
If you want to measure the change amount of photosynthetic vegetation in your community in early July over several years, Landsat imagery is available going back several decades now. On a larger scales, global for instance, we have imagery from sensors such as MODIS and AVHRR. These types of measurements can be used to monitor drought conditions, changes in land use, carbon sequestration potential, etc.
Is this at all helpful? We can also schedule additional meeting times, whether in person or via phone or video conference if that would help. Let me know.
Andy
Okay, so as I plan my unit one of the remote sensing exercises I can do is the use the Aerokats to measure the level of "Greening" that occurs from early spring to late spring. I think looking at the biomass in terms of photosynthesis would be a great way to relate that life science concept to a real world application.
ReplyDeleteWhile I am good at teaching the process of photosynthesis, and am confident I can teach the students how to use the kites to collect the data. I am not as confident in using the computer program to track the changes in the biomass. This is where I can use some assistance.
Hi Kathleen,
ReplyDeleteI am trying to get a couple of dates in the next week or so for some evening lab time. I'll send out dates as soon as I have times confirmed. If you can't make the times I get, we'll figure something else out. I know that this is an area that needs additional attention.
BTW, one of the tools made available to us from the Lawrence Hall of Science, is a really good and straightforward introduction to principles of remote sensing and very easy to use piece of software to go with it.
If you are interested, the tutorial, "ABCs of Digital Earthwatch Software" can be downloaded from this address: http://lawrencehallofscience.org/gss/rev/studentBooks-SMPL/ABCsOfDEWsoftware.pdf
The software can be downloaded here:
http://lawrencehallofscience.org/gss/rev/ip/
Be sure to download the DEW Software Bundle, not the HOU software, and download the DEW Images to use with the tutorial.
I thought it was a very good introduction for the remote sensing and image processing, and something teachers might want to use with their students.
Thanks Andy and Dave,
ReplyDeleteI look forward to the lab time. I was lucky enough to meet Dr. Tim Samaras from the Discovery Channel program 'Storm Chasers' at the MSTA convention this past weekend. We had an interesting conversation about the type of remote sensing he does when he tornado chases and lightening chases.
Remote sensing is very cool and it will be great to show his work as an example of remote sensing to my students.
Thanks Andy for your expertise and time and for teaching us how to process images and get NDVI data.
ReplyDelete