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Geog 272 > Test 2 > Flashcards

Test 2 Flashcards

1
Q

what does microwave have the ability to do?

A

measure the brightness temperature of the earth’s surface

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2
Q

What are the wavelengths of microwave?

A

1 cm to 1 m

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3
Q

Passive microwave has…

A

low energy and coarse spatial/ spectral resolution

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4
Q

Types of active microwave sensors

A

scatterometer and altimeter- Non-imaging Radar
Real Aperture Radar and synthetic aperture radar- Imaging Radar

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5
Q

What does a scatterometer do?

A

Coarse resolution, good for ocean surface, wind, and wave

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6
Q

What does an altimeter do?

A

Measure the height of the surface of the earth

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7
Q

What are the characteristics of Radar principles?

A

Radar Detection and Ranging
Microwave range
active system
high spatial resolution (same as optical)
day-night & all-weather capabilities
complementary to optical systems

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8
Q

How does RADAR work?

A

Radar sends pulses through the transmitter and the target reflects the echo which captured by the receiver

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9
Q

What does radar distinguish objects by?

A

They distance but when it looks Nadir, the same distance is on the right and left, this causes problems, leads to side looking geometry

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10
Q

How do you improve azimuth resolution?

A

a shorter wavelength pulse will result in improved azimuth resolution
The size of the antenna is inversely proportional to the size of the angular beam width (azimuth resolution)

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11
Q

synthetic aperture radar (SAR)

A

makes a relatively small antenna work like it is much larger, by taking advantage of the platform’s motion

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12
Q

What does radar measure

A

the ratio between the received electrical field over the field incident to that location on earth

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13
Q

What is the backscatter coefficient tell us?

A

amplitude information

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14
Q

Some ranges for backscattering

A

very high (>5db): man made objects, terrain slopes towards the radar, very rough surface, radar looks very steep

high backscatter (-10dB to 0dB): rough surface with dense vegetation

moderate backscatter (-20 to -10 dB): medium level of vegetation, agricultural crops, moderately rough surfaces

low backscatter (below -20 dB): smooth surface, water, road, or very dry terrain

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15
Q

why do we get speckle?

A

inherent to imaging of distributed scatterers because SAR is a coherent (same wavelength imaging sensor)

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16
Q

What is speckle?

A

Salt and peppa. areas with similar land or water cover can have a very “salt and pepper” appearance on radar imagery because of constructive and destructive interference between reflected microwave EM waves

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17
Q

geometric distortion or elevation displacement

A

elevation displacement, the image displacement in a remote sensing image toward the nadir point in radar imagery due to sensor/target imaging geometries

main types: foreshortening, layover, shadow

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18
Q

Foreshortening

A

mountains are leaning towards the satellite sensor

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19
Q

Layoever

A

the top of the mountain covers the glacial valley because of layover

extreme case of foreshortening

easy to fix

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20
Q

shadow

A

see paper

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21
Q

What does the backscatter coefficient provides information about the imaged surface and is a function of…?

A

atmospheric parameters

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22
Q

Radar observation parameters

A

wavelength, polarization, incidence angle of the electromagnetic wave emitted

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23
Q

surface parameters:

A

roughness, geometric shape/structure, dielectric properties of the target (moisture content)

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24
Q

Polarization of electromagnetic wave

A

the direction of the electric field in relation to the wave propagation

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25
Q

What is a dielectric constants

A

is a measure of the electrical conductivity of material

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26
Q

What is the determining factor for backscattered radar energy in terrestrial materials?

A

moisture content
wet materials- high reflection
dry materials- low reflection

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27
Q

The radar return can come from three sources

A
  1. direct scattering from the vegetation
  2. direct scattering from the ground
  3. Multiple scattering between the ground and the canopy (canopy might absorb some of the microwave energy, must account for attenuation by the canopy)
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28
Q

Surface factors that influence radar scattering from vegetated surfaces

A
  1. Changes in soil moisture
  2. changes in canopy moisture
  3. difference in canopy structures/biomass
  4. presence or absence of water on top of soil
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29
Q

Monitoring soil moisture variation

A
  1. low soil moisture
    a. low direct scattering from canopy
    b. low multi-path scattering
    c. moderate scattering from soil
  2. high soil moisture
    a. low direct scattering from canopy
    b. low multi-path scattering
    c. high scattering from soil
  3. flooding or inundation of the soil surface with water
    a. low direct scattering from
    b. low multi-path scattering
    c. no scattering from soil
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30
Q

Rules of SAR image interpretation

A
  1. regions of calm water and other smooth surfaces will appear black, because the incident radar reflections away from the spacecraft
  2. surface variations near the size of the radar’s wavelength cause strong backscattering
  3. wind-roughened water can backscatter brightly when the resulting waves are close in size to the radar’s wavelength
  4. a rough surface backscatters more brightly when it is wet
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31
Q

SAR Image interpretation rules continued

A
  1. a particularly strong response, say from a corner reflector, can look like a bright cross in a processed SAR image
  2. due to reflectivity and angular structure of buildings, bridges, and other human-made objects, these targets tend to behave as corner reflectors and show up as bright spots in a SAR image
  3. Hills and other large-scale surface variations tend to appear bright on the side that faces the sensor and dim on the side that faces away from the sensor
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32
Q

What are the dielectric constants of water, soil, and vegitation?

A

80, 3-6, and 1-3 respectively

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33
Q

What do you have to sacrifice in order to get rid of speckle?

A

spatial resolution

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34
Q

What is backscattering dependent on?

A

relative height or roughness of the surface

very rough surface strong backscatter

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35
Q

P band

A

smoother
doesn’t penetrate much through vegetation
Longer wavelength is penetrating the vegetation due

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36
Q

C Band

A

Can’t see the roads

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37
Q

Passive Systems

A

use natural energy sources, reflected or emitted energy, photography, thermal, passive MW

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38
Q

Active systems

A

Have own energy source, radar lidar, works in the dark

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39
Q

What is LIDAR?

A

Light Detection and Ranging
emits pulses of light towards a surface
works in the visible and NIR
pulses reflect off surface objects and return to the sensor to be recorded

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40
Q

What are the steps to LIDAR?

A

pulses contain individual quanta of light (photons)
recorded photons are converted to electric currents
electric currents are converted to digital counts
digital counts are measured over fixed time intervals
time intervals are converted to distances

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41
Q

What does LIDAR measure?

A

total time for a light pulse to leave the sensor, hit an object, and then return

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42
Q

Pulses

A

the laser pulse is emitted from the lidar system
then reflects off of every surface/ feature in its path

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43
Q

Properties of Lidar Pulses

A

pulse wavelength: NIR
rate: number of pulses per second in kHz
pulse spacing: distance between lidar pulses
pulse footprint: area converted by a single pulse
discrete return vs. full waveform: how a pulse is processed and stored

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44
Q

Pulse spacing

A

the number of lidar pulses emitted per unit area
depends on: flight speed, scanning pattern, and pulse rate

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45
Q

Pulse Footprint

A

ground area covered by a single pulse
the shorter the distance between the lidar and the surface, then the smaller the footprint

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46
Q

Clarification between spacing and footprint

A

footprint: ground covered by a single pulse
Spacing: distance between footprints

both important to resolution and precision

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47
Q

relationship between footprint and spacing

A

spacing= footprint: ideal
spacing<footprint: data replication
spacing> footprint: data gaps

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48
Q

discrete return vs. full waveform

A

each lidar pulse contains many, many photons
these photons produce a waveform based on returned energy amplification
every feature the pulse reflects off of will produce a peak in the waveform
the pulse is digitalized to create individual discrete returns or points
some systems store the full waveform

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49
Q

Properties of discrete lidar data

A

spatial coordinates: represents the spatial location of the point
return number: each pulse can have multiple point “returns”
classification code: similar to classifying features or land use in a digital image
intensity value: relative return energy strength

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50
Q

Lidar Data Return number

A

generated from the waveform discretization
each pulse is a collection of many individual
photons of light. results in multiple data
points that can be measured by each lidar
pulse
Multiple points –> multiple returns

the first point is closest object to lidar system
last return: farthest object from the lidar system

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51
Q

Classification of lidar

A

one can classify lidar points based on the feature

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52
Q

Lidar data intensity

A

generated from the waveform energy amplitudes

a relative intensity measure is provided for each point

amplitude of the pulse energy in the reflected waveform

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53
Q

Limitations of lidar intensity values

A

similar but not the same as passive NIR reflectance
dependent on many variables:
reflectance of object
lidar system and wavelength it uses
distance and scan angle between sensor and
target
rank of the return
normalized 8-bit

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54
Q

What happens to Lidar NIR pulses in water

A

they are absorbed. This causes gaps in dataset, a major limitation of lidar scanners. multiple tools necessary for surveying

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55
Q

Why is NIR popular for lidar?

A

availability of stable materials for laser
most surfaced reflect NIR quite well
low signal-to-noise ratio in sunlight
more eye-safe than other wavelengths

56
Q

Digital Elevation Models

A

digital terrain models: ground itself
digital surface models: features on the ground
canopy height models: height above ground

57
Q

DTM’s

A

a digital model that only contains the elevation of ground points

last returns and single returns

if ground is classified, then the set of ground returns

58
Q

DSMs

A

surface model that contains the elevation of ground points as well as any fixed features

includes features such as vegetation, buildings, etc

set of first returns and single returns

set of both ground and non-ground

59
Q

“Normalized” DSMs

A

calculated as nDSM-DSM-DTM
“normalized” to represent height above ground

ex: canopy height models and building height models

60
Q

Drone Laser Scanning

A

potential for “best of both worlds”
great coverage and resolution (0.05m)

61
Q

What are the effects of leaf chlorophyll on leaf reflectance?

A

more chlorophyll content is more absorption (less reflectance) in the visible range

62
Q

What are the effects of leaf water content on leaf reflectance?

A

more water content is more absorption in the SWIR range

63
Q

what does a spectral transformation of two or more bands allow?

A

reliable spatial and temporal inter-comparisons or terrestrial photosynthetic activity and canopy structural variations

64
Q

What does healthy vegetation do?

A

absorbs most of the visible light that hits it, and reflects a large portion of the NIR

65
Q

what does unhealthy do?

A

reflects more visible and less NIR

66
Q

What do visible, NIR, and SWIR bands do?

A

give actual images of Earth’s surface

67
Q

What does the cirrus band do?

A

specific cloud detection

68
Q

Mulit-spectral

A

10s of bands

69
Q

hyper-spectral

A

100s of bands

70
Q

Vegetation indicies

A

a spectral transformation of two or more bands in to a single variable

71
Q

Does healthy vegetation have a high or low NDVI?

A

High

72
Q

What is temporal domain used for?

A

change detection, temporal composite, and phenology

73
Q

What can temporal domain be used for?

A

Urban Growth, agriculture expansion, forestry, water, floods, volcanic activity, drought, illegal mining

74
Q

NDVI anomaly can be an indicator of ?

A

drought

75
Q

What are neighborhood operations?

A

filtering, segmentation, classification, and object detection

76
Q

Mean value smoothing

A

pixel values are changed to the average of neighboring pixels

Doesn’t preserve edges

77
Q

Median filtering

A

pixel value is changed to the median of neighboring pixels

78
Q

Sobel filers

A

something with a gradient, used for boundaries, check lab

79
Q

The larger the swath…

A

the higher the temporal resolution

80
Q

… but by increasing swath

A

spatial resolution is coarse

81
Q

Classification

A

mapping from measurements acquired by a remote sensing instrument to a label(s) for each pixel that identifies it with what’s on the ground

82
Q

Quantitative Analysis

A

VHR image classification by object

83
Q

what the the problems with image classification?

A

Regression and classification ?

84
Q

Regression

A

predict continuous variables

85
Q

Classification

A

predict categorical variables

86
Q

What are the types of classification?

A

supervised and unsupervised

87
Q

supervised classification

A

target outputs are provided with input data

87
Q

Unsupervised classification

A

discover patterns in input data for the purpose of clustering, density estimation, dimensionally reduction, and visualization

88
Q

clustering

A

to discover groups of similar examples within the data

89
Q

density estimation

A

determine the distribution of data within the input space

89
Q

Visualization

A

to project the data from a high-dimensional space down to two or three dimensions

90
Q

supervised classification: the steps

A
  1. class nomenclature definition (general land cover classes: impervious, cropland, etc)
  2. features and algorithm selection
  3. generation of training data (in situ ground measurements, drones, and photo-interpretation)
  4. accuracy assessment
91
Q

what does image classification require?

A

delineating boundaries of classes in n-dimensional space using class statistics

92
Q

pixel based

A

each pixel is grouped in a class

multiple changes in land use within a short period of time

best for complete data coverage and needs methods to ensure time series consistency at the pixel level

93
Q

Object-based

A

pixels with common spectral characteristics are first grouped together

94
Q

Two types of cluster based classification (it is unsupervised)

A

produce clusters (k-means and ISODATA) and label clusters

95
Q

K-means

A

assign centroids, classify using nearest centroid, recalculate mean and mean change

96
Q

supervised classification approaches

A

nearest neighbor, maximum likelihood, decision tree/ random forest, neural network, support vector machine

97
Q

Decision tree

A

using the concept of information entropy
splitting data based on the normalized information gain

98
Q

supervised classification: generation of training data

A

number of features: 10s-100s so classes are separable

size: if using n features, >10n pixels of training data should be collected for each class
should be large enough to provide accurate estimates of the properties of each class

location: each class should be represented by several training areas positioned throughout the image

uniformity: each training area should exhibit unimodal frequency distribution for each feature

99
Q

What is a key strength of RS

A

is enables spatially exhaustive wall-to-wall coverage of an area of interest

100
Q

sampling design

A

protocol for selecting the subset of spatial units that will form the basis of the accuracy assessment

101
Q

Response design

A

encompasses all aspects of the protocol that lead to determining whether the map and reference classification are in agreement

101
Q

analysis

A

protocols for defining how to quantify accuracy along with the formulas and inference framework for estimating accuracy and area and quantifying uncertainty of these estimates

102
Q

what are the properties of optical?

A

source is the sun

measures reflectance

includes vegetation indices and land cover mapping

doesn’t see through the clouds must use cloud masking

cloud top reflectance

103
Q

what are the properties of thermal IR

A

emitted from the sun
measures temperature
used for fire detection
cloud top height measurements

104
Q

What are the properties of SAR

A

sensors in mw
backscatter is measured (amplitude and face of the wave)
surface structure and moisture is measured
all sky and all weather

105
Q

What are the properties to passive mw

A

emitted from the earth
temperature
precipitation, moisture
not must interference from clouds

106
Q

What are the properties of lidar

A

sensors (light)
reflected or individual photon
topography, tree height, and surface structure
cloud can get in the way
measures reflected photon from the cloud

107
Q

Examples of supervised classification

A

nearest neighbor
maximum likelihood
decision tree/ random forest
neural network
support vector machine
kNN

108
Q

Examples of unsupervised classification

A

produce clusters
kmeans
ISODATA
label clusters

109
Q

commission (user’s accuracy)

A

you label it as class j but the reference map is not class j

110
Q

omission (producers accuracy)

A

should be class j but not labeled as class j. In the reference map it should be j but on your map it is class i

111
Q

User accuracy equation

A

uu/ total number of u across

112
Q

Producer accuracy equation

A

uu/ total number of u down

113
Q

user accuracy definition

A

the proportion of the area mapped as class i that has reference class i

114
Q

producers accuracy

A

of class j the proportion of the area of reference class j that is mapped as class j

115
Q

emissivity

A

emitting ability of a real material, compared to that of a blackbody

116
Q

blackbody

A

an object that absorbs all electromagnetic radiation that falls onto it

117
Q

greybody

A

imperfect blackbody. The ratio of a gray body’s thermal radiation to a black body’s thermal radiation at the same temperature is called the emissivity

118
Q

selective emitter

A
119
Q

Thermal Range

A

1.30um-12um

120
Q

Thermal Infrared radiation

A

fire monitoring
sea surface temperature
water stress in agriculture
volcano monitoring
surveillance
assessing inductrial heat efficiency

121
Q

measuring kinetic temperature

A

contact involved, internal manifestation of temp

122
Q

Measuring radiant temperature

A

no contact involved -RS

external manifestation of temp

123
Q

Kinetic heat

A

is the energy of particles in random motion
measured with a thermometer

124
Q

Planck’s Law

A

the temperature of the matter defines the wavelengths of the EM radiation that being emitted

125
Q

Radiant Temperature

A

the radiant flux being emitted by an object because of its temperature

126
Q

Kirchoff radiation law

A

the spectral emissivity of an object equals its spectral absorbance

127
Q

The lower an object’s reflectance…

A

the higher its emissivity

128
Q

Emissivity and color

A

darker colors are better absorbers/ emitters

129
Q

Surface Roughness

A

as surface roughness increases, emissivity increases

130
Q

Moisture Content

A

as moisture increases, emissivity increase

131
Q

EM wavelength

A

emissivity varies with wavelength

132
Q

viewing angle

A

emissivity varies with viewing angle

133
Q

Sources of temperature variation

A

the sun, atm, diurnal and seasonal cycles of temperature changes, and thermal capacity

Geog 272 (3 decks)

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