Review Questions for 2nd Midterm Exam (12 November)
BOT/GEOG 4111/5111
Fall 2015
The second exam will have the same format as the first—5 questions worth 20 pts.
each. Bring your calculators to the exam. The exam will cover material since the last
exam up through unsupervised and supervised classification. Accuracy assessment and
field data collection will be on the final exam. You will have the full class period (8:10-11
a.m.) to finish the exam if needed.
Radiometric Correction
1. What is the difference between radiometric correction and geometric
correction?
2. Be able to list and describe the factors that affect the amount of radiance
measured by a satellite.
3. What is “path radiance”? Be able to show it in a diagram. How does it affect
spectral reflectance curves if imagery is not atmospherically corrected?
4. What are Rayleigh scattering and Mie scattering? Which is responsible for blue
skies and why?
5. Why does each band of a satellite need to undergo a separate atmospheric
correction?
6. Why and when are atmospheric corrections necessary? When are they not
necessary?
7. Describe dark pixel subtraction. Does dark pixel subtraction address differences
in atmospheric transmittance?
8. Be able to draw histograms showing the pixel distribution in an image before and
after dark pixel subtraction, and to explain the differences.
9. What are the advantages and disadvantages of atmospheric modeling for
atmospheric correction?
Geometric Correction
1. Why is geometric correction important?
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2. What are Ground Control Points (GCPs)? What types of image features are
useful for defining GCPs? Why can water features be problematic GCPs?
3. Understand when you would use a 1st, 2nd or 3rd order transformation. How
many GCPs do you need at a minimum for each? How many GCPs SHOULD you
use?
4. Be able to show the relationship between GCPs and the transformation type on a
graph.
5. How should GCPs be arranged on an image to achieve good results?
6. Why should you use the lowest order GCP that is appropriate for your area?
7. What is resampling? What is the difference between nearest neighbor, bilinear
convolution, and cubic convolution? What are the advantages and
disadvantages of nearest neighbor vs. the other two methods?
8. What does data fusion mean, as described in your textbook?
Vegetation Indices
1. What three things should an ideal vegetation index should do?
2. Be able to draw the reflectance spectra of vegetation and soil and then show,
using your drawing, why a difference or a ratio vegetation index distinguishes
the two materials.
3. Is the Difference Vegetation Index (DVI) an ideal vegetation index? Why or why
not?
4. Is the simple ratio (SR) index an ideal vegetation index? Why or why not? What
problems might the SR cause?
5. Be able to calculate NDVI. Does the NDVI meet the criteria for an ideal
vegetation index?
6. How does the simple ratio index reduce the effects of the atmosphere?
7. Why is the relationship between NDVI and LAI non-linear?
8. Why is NDVI sensitive to differences in soil beneath the plants?
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9. What is “feature space?” Be able to draw a 2-dimensional example of feature
space using red and NIR bands for a typical image. Where would vegetated
pixels be found in this feature space and why? Where would soil be found?
10. Describe the similarities and differences between Principal Components Analysis
(PCA) and the Tasseled Cap Transformation (TC). Which TC or PCA component
might be related to the amount of green vegetation and why?
11. Be able to graphically show how PCA and/or TC works for a 2-dimensional
example.
12. Be able to invent a spectral index given the spectral curves of two differing
materials. In other words, be able to figure out simple algebraic combinations of
2 bands that enhance differences between spectral curves.
Multi-temporal Remote Sensing of Vegetation (lab exercise)
1. Be able to invent a multi-temporal vegetation index like you did in lab for
evergreen vs. deciduous trees for other vegetation that differs in phenology.
2. Explain how NDVI can be used as a drought index.
Classification
1. What is image classification?
2. What is the difference between a spectral class and an informational class?
3. What are the two most commonly used strategies for automated (digital) image
classification? Be able to explain how they differ.
4. When and why is manual interpretation a good choice for making maps?
5. What are the basic steps that one uses to classify imagery (in general)?
6. What is a classification scheme and what are the characteristics that a good one
must possess? Be able to design a simple classification scheme given some basic
theme occurring in an area.
7. Be able to describe how a supervised classification works.
8. What characteristics should good training data have?
9. What is “spectral confusion”?
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10. Be able to describe in general terms the differences between minimum distance,
parallelepiped, and maximum likelihood classifiers. What are some advantages
and disadvantages of each?
11. How does a decision tree work?
12. Why are field data important for digital classification?
13. What do we mean by ancillary data and when do you need to use it for
classification?
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