Final Exam - Multiple Choice

Question 1

Crop Phenological Cycles

Answer 1

-Periodic biological phenomena that are correlated with climatic/annual conditions
-When looking at graphs, it looks like when there is more yield has to do with the time of year
-Useful for accessing crop conditions, droughts, wildfire risks, invasive species etc

Tells us when data should be collected, when crops should be harvested

Question 2

Vegetation Index

Answer 2

-It is a synthetic image layer that is created from the existing bands of a multispectral image
-Provides information that is not found in other individual bands

Question 3

Vegetation Index Example

Answer 3

Shows vegetation biomass, land cover, leaf area, and vegetation ground cover

Question 4

NDVI (Normalized Difference Vegetation Index)

Answer 4

-Simple numerical indicator that can be used to analyze remote sensing measurements
-Can access if area contains live green vegetation or not

Question 5

NDVI Values

Answer 5

-Numbers ranging form -1 to 1
-0.1 and below represent barren rock, sand and snow
-0.2 to 0.3 represent shrub and grassland
-0.6 to 0.8 represent temperate and tropical rainforests
-Well-vegetated has higher values
-Water has negative values

Question 6

NDVI Formula

Answer 6

NIR - Red / NIR + Red

Question 7

EVI (Enhanced Vegetation Index)

Answer 7

-Improved sensitivity to high biomass regions
-C are coefficients adjusting for atmosphere and L is a soil factor

Question 8

Image Classification

Answer 8

assigning pixels in an image to one of a number of classes
–> creates a thematic map

Question 9

Classification schemes; Classes

Answer 9

Simplification and generalization of the real world

Question 10

What types of classification schemes can you design?

Answer 10

1. Broad type-abstractive
2. Specific type-concrete

Question 11

What are Anerdsons Level one land cover classification?

Answer 11

1. Urban
2. Agriculture
3. Rangeland
4. Forest Land
5. Water
6. Wetland
7. Barren Land
8. Tundra
9. Snow or Ice

Question 12

What is a spectral reflectance curve?

Answer 12

Earth surface features distinctive electromagnetic reflectance properties

Question 13

Multispectral Classification -> Unsupervised Classification

Answer 13

ISODATA –> more computer automated

Question 14

Multispectral Classification -> Supervised Classification

Answer 14

Maximum Likelihood classification –> more prior knowledge is needed, more controlled by users

Question 15

What is ISODATA

Answer 15

-Iterative Self-Organizing Data Analysis Techniques
-Uses minimum spectral distance to assign a cluster for each pixel
-Repeatedly performs an entire classification and recalculates statistics

Question 16

Whats the classification process for unsupervised classification?

Answer 16

1. Classification scheme
2. Classification methods
3. Accuracy assessment

Question 17

Whats the classification process for supervised classification?

Answer 17

1. Selection of training samples
2. Generation and evaluation of statistical signatures
3. Class assignment using a decision rule

Question 18

What is a training sample?

Answer 18

-samples of homogeneous areas with known class types
-training samples can include fieldwork, aerial photography, maps
-spectral characteristics of training samples are used to generate signatures

Question 19

What is the decision rule

Answer 19

-supervised classification
-mathematical algorithm that performs the sorting of pixels into distinct classes

Question 20

What is parallelepiped?

Answer 20

-supervised classification
-has upper and lower limits for every signature in every band
-if pixels are in parallelepiped, they are assigned to that features class
-simple and fast, useful for broad classification
-pixels in gap regions will not be classified or improperly

Question 21

What is minimum distance?

Answer 21

-supervised classification
-calculates the spectral distance between the measurement vector for the pixel and the mean vector for each signature
-no unclassified pixels, fast decision rule
-pixels that should be unclassified are classified, does not consider class variability

Question 22

What is maximum likelihood?

Answer 22

-supervised classification
-based on probability that a pixel belongs to a particular class
-most common decision rule
-assumes probabilities are equal for all classes and input bands have normal distribution