Remote Sensing - Paper Answers

GEOG4770EnvironmentalRemoteSensingExam2SP2020212 Week5ActiveRemoteSensing1 Week6-ThermalRemoteSensing1 Week8-UnsupervisedClassification1 Week7Resolution1

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GEOG 4770: Remote Sensing of the Environment

Exam 2

Due April 9

150 points

Instructions:
You must complete the exam on your own. You may use any reference materials, including course text, your notes, lecture materials on Blackboard, etc. Answer the questions in your own words.

1. What is the difference between Manual Interpretation and Digital Classification of remotely sensed imagery? Give an example of each.

2. List and briefly explain the 7 general steps involved in the classification of remotely sensed images.

3. Discuss some of the main issues you will consider in developing a Classification Scheme (legend) for a remotely sensed image classification project.

4. What are the advantages and disadvantages of unsupervised classification?

5. What are the similarities and differences between Radar and Lidar?

6. Explain some of the advantages and disadvantages of:

a. Radar

b. Lidar

7. Explain some of the applications of Thermal Remote Sensing.

8. Explain (give examples where appropriate) the following types of resolution as it relates to remote sensing. Be sure to include why they are an important consideration in a remote sensing project.

a. Spatial

b. Spectral

c. Radiometric

d. Temporal

Active Remote
Sensing

Radio Detection and Ranging = Radar

Light Detection and Ranging = Lidar

Passive vs. Active Remote Sensing
• Passive remote sensing uses the energy from the sun

• Active remote sensing sends out its own energy and
records how much bounces back

o Imaging Radar uses microwave wavelengths

o Lidar uses visible wavelength (laser) or
sometimes NIR

Oil spill off NW coast of Spain

IKONOS image

Oil
reaching
shore

Radar image of oil spill off NW coast of Spain

(Black areas = oil)

Side-looking Radar

• Most radar systems do look off to the side (off-nadir)

o For military applications allows planes to fly over
friendly territory and look into enemy territory

o More importantly, gives us more info about
surface characteristics

• Radar is sensitive to surface roughness

Side-looking Radar

Radar Terminology

• Swath width = range

• Direction of flight = azimuth

• Backscatter = reflectance

• Angle of view = depression angle

• Etc.—unique terminology

Depression
Angle

Radar Geometry

4
Radar Geometry

Radar Advantages

• Can penetrate clouds

• Active, so can use day or night

• Less of a radiance vs. reflectance problem
since you know exactly how much energy
you send out and can measure what you
get back—and atmosphere not a problem

• Can penetrate dry soil and get subsurface
characteristics (e.g., archaeology)

Radar Disadvantages

• Developed by military, less civilian experience so far
than passive remote sensing

• Difficult to interpret—complicated causes of
reflectance

• Geometric distortions caused by side looking
geometry

Radar bands were originally
code names assigned by
the military:

P-band

L-band

S-band

C-band

X-band

K-band

Radar Bands

Radar penetration increases with wavelength

Radar Bands

Interpretation of Radar
Data

• Surface “smoothness” or “roughness” with
respect to radar depends on wavelength
and incident angle

o A smooth surface reflects in one direction
(specular)

o A rough surface scatters radiation in all
directions (Lambertian or diffuse)

o Rough surfaces tend to depolarize
radiation

Surface Roughness

Shorter wavelength radar can
resolve roughness more
finely

Interpretation of Radar Data

Type of Backscatter
(Reflection)

• Diffuse
o Vegetation canopies

o Cross polarization (use HH and HV)

• Specular
o Microwaves reflected away (not scattered) (e.g., oil slick)

• Corner reflectors
o Tree trunks and buildings

o can make objects very “bright” or very “dark” depending on orientation

Surface roughness is described as a function of
wavelength and the angle of incidence of the incoming
radiation

Interpretation of Radar Data

Interpreting Radar Data

• Longer wavelength bands (P and L bands)
o Penetrate canopy and reflects off of standing tree trunks

o Can detect amount of wood in a forest

o Estimate forest biomass

• Shorter wavelengths (C and X bands)
o X band can detect leaves

o C (and L) band can detect twigs

Resolving Objects with
Radar

• Radar resolution determined by…

o Size of area “illuminated” by microwaves at one
time

• Depends on antenna length (longer antenna
= better spatial resolution)

o But…difficult physically for airplane or
satellite to carry a very large antenna.

• Depends on length of radar pulse (longer
pulse = lower spatial resolution)

Pulse length in part determines radar
resolution

Interpretation of Radar Data

Real Aperture vs. Synthetic

Aperture Radar (SAR)
• Real aperture radar actually uses a single

antenna of a given length – resolution
limited to what plane or satellite can carry.

• Synthetic Aperture Radar (SAR) can simulate
a large antenna by taking advantage of
the Doppler effect
o Doppler shift allows sensor to identify

electromagnetic waves from ahead and behind
the platform and therefore track an object for
longer than it otherwise could, as if the antenna
were longer.

Synthetic Aperture Radar – Doppler Effect

Interpretation of Radar Data

Radar Sensors

• There are many imaging radar sensors available,

both airborne and on satellites
o Most aircraft use SAR

o All satellites use SAR (to achieve reasonable spatial resolution)

9
Radar Sensors

Radar Applications

• Forest inventory

• Oceanography

• Archaeology

• Sea ice studies

• Digital Elevation Models (DEMs)

• Urban mapping

• Wildfire studies

Radar Image – Angkor,
Cambodia. Purple spots are
possible buried ruins

Radar Applications

Radar image of oil slicks

Radar Applications

Radar derived elevation with TM draped over it

Radar Applications

Kamchatka Peninsula – Shuttle Radar
Topography Mission (SRTM)

(Mission generated detailed
topographic data for 80% of earth’s
land surface)

Radar Applications

Lidar Remote Sensing

• Like Radar but sends laser pulses instead of

microwave/radio pulses

• Can collect extremely accurate elevation data
quickly (vs. ground survey)

• Typically flown on aircraft

High-resolution

LIDAR topography

Lidar Remote Sensing

Lidar vs. DEMs from topo sheets

landslides

Southern Bainbridge Island

Thermal Remote Sensing

Distinguishing materials on the
ground using differences in
emissivity and temperature

Landsat-based thermal change of Nisyros Island (volcanic)

Thermal = Emitted Infrared
• IR = 0.76 um to 1000 um

– Reflective IR = 0.7 – 3.0 um
– Thermal IR for remote sensing = 7 – 18 um

• Sometimes called far IR (vs. near and mid
IR)

• Experiences almost no atmospheric
scattering

• But…lots of absorption by atmospheric
gases (e.g., CO2)
– Must use atmospheric windows for rem. sens.

The Infrared portion of
the electromagnetic
spectrum

Emitted Thermal

Atmospheric transmission by λ

Thermal Properties of Objects

• All objects with temperature > 0o K emit
thermal radiation
– Amount depends on temperature (Stefan-

Boltzman Law)
• M = εσT

– Peak wavelength emitted also depends on
temperature (Wien’s Displacement Law)

• Peak λ(µm) = 3000/T(oK)

Wien’s
Displacement
Law

Emissivity

• Emissivity is the ratio of the emittance of
an object to that of a Black Body
– A black body has ε = 1

– A white body has ε = 0

– Water has ε close to 1

– Most vegetation has ε close to 1

– Many minerals have ε << 1

• Can find tables of emissivities in reference
books and textbooks

Kinetic Temperature vs. Radiant
Temperature

• Kinetic temperature is caused by the vibration of
molecules
– sometimes called “true temperature”

– measured using conventional temperature scales
(e.g. oF, oC, oK)

• Radiant temperature is the emitted energy of an
object
– sometimes called “apparent temperature”

– what we measure with thermal remote sensing

– depends on kinetic temperature and emissivity

Thermal Remote Sensing

• Incoming radiation from the sun is
absorbed (converted to kinetic energy)
and object emits EM

• Objects vary in the amount of sun they
“see” (different slopes, etc.) and in their
emissivity

• Thermal remote sensing is sensitive to
differences in emissivity.

Interpreting Thermal Images
• Thermal images are often single-band and

look like black and white photographs
– Bright areas = relatively warmer places
– Dark areas = relatively cooler places
– Can be the opposite for thermal weather

images!

• Must know if the image is a negative or a
positive!

• Should know the time of day the image
was acquired – day vs. night alters the
interpretation

Atlanta — Daytime Atlanta — Nighttime

Daily change in radiant temperature of common objects

North

Thermal Infrared Multispectral Scanner (TIMS) image of Death Valley

Daytime Positive – Bright = warm, Dark = cool

Multi-band thermal

• Thermal imagery can also be multi-band
(different parts of the thermal IR spectrum)

• When displayed in color, colors primarily
represent differences in emissivity.

North

TIMS image of Death Valley made by combining thermal
bands from different wavelengths after “decorrelation
stretching”

Interpretation (cont.)
• It is difficult to accurately calculate the

kinetic temperature of objects from their
radiant temperature
– Must know the emissivity of the target(s)

– Often have to estimate or assume emissivity
values

Complicating Factors

• Topography (effects amount of incoming
radiation from sun)

• Fine scale differences in emissivities of materials
in scene

• Cloud cover history

• Precipitation history – differences in soil
moisture

• Vegetation canopy geometry

• Geothermal areas

• Many others

Thermal Sensors

• Thermal Infrared Multispectral Scanner (TIMS)
(Airborne – 18 m spatial res.)

• Landsat 3 MSS (237 m spatial resolution)

• Landsat TM (Band 6) (120 m spatial)

• Landsat ETM+ (Band 6) (60 m spatial)

• Landsat 8 (Band 10 and 11) (100 m spatial)

•

ASTER

(5 thermal bands at 90 m spatial)

• MODIS (many thermal bands at 1 km spatial
resolution)

• Many others…

Applications

• Agricultural water stress (energy balance)

• Heat loss from urban areas

• Identifying and mapping materials based on their
emissivities (e.g. minerals)

• Earthquake and volcanic activity prediction

• Mapping moisture amounts

• Ocean current mapping

• Plumes of warm water from power plants, etc.

• Atmospheric studies, weather forecasting, etc.

Evapotranspiration (ET)
estimation using thermal RS

• If you know how much energy is being
used to evaporate water, you can estimate
how much water is evaporating!

E = H + L + r + G

Where E = irradiance, H = sensible heat, L =
latent heat, r = reflected energy, and G =
ground storage of energy.

– R

Thermal
Image of Lava
Flows

ASTER

Airborne
thermal
image of
warm creek
flowing into
ocean near
Anchorage,
AK

ASTER images of San
Francisco.

Bottom right is thermal
image used for water
temperature

Summary – Thermal Remote
Sensing

• Typically used to map surface materials that
differ in thermal properties (like emissivity)

• Usually NOT used to map absolute kinetic
temperature

• Many applications but not especially good for
distinguishing among vegetation types because
all veg has about the same emissivity

• Gives us another tool to help distinguish
materials that may be spectrally similar in the
reflected wavelengths!

3/23/2020

Classification of Remotely
Sensed Data

General Classification Concepts
Unsupervised Classifications

What is Image Classification?

• Process of converting image pixels or regions
to classes that represent self-similar features
or “themes”

• Using images to create “thematic maps”

How?

• Two general approaches:
– Manual interpretation (e.g., photointerpretation,

“heads-up

digitizing”)

– Digital classification (per pixel)

• Many digital techniques developed
– Unsupervised classification
– Supervised classification
– Classification and Regression Trees (CART)
– Neural Networks
– Etc., etc., etc.

3/23/2020

General Classification Steps

Field reconnaissance
Develop a classification scheme (legend)
Enhance imagery (as needed)
Use classification algorithms
Incorporate ancillary data (as needed)
Check accuracy of product
Refine iteratively

Field Reconnaissance

• Critical for understanding the distribution of
your theme in the real world

• Helps you choose useful ancillary data
• Useful for understanding satellite imagery

back at the office
• Nice to get out once in a while

3/23/2020

What characteristics of this landscape might be important for making a map
using satellite data?

Developing Classification Schemes
(Legends)

• How many types do you want to map?
• How should you divide up the feature you are

interested in?
• Can be very controversial!

Classification Schemes
(List of types to map)

• What thematic classes are you going to
assign pixels to?

1) Must be useful
2) Must be detectable using the data you have
3) Should be hierarchical
4) Categories must be mutually exclusive
5) Require explicit definitions of each class

3/23/2020

Classification Scheme — Example

I. Vegetated
A.

Forest

1. Evergreen
a. Spruce-fir forest

i. Spruce-fir with winterberry
understory

b. Lodgepole pine forest
c. etc

2. Deciduous

B. Shrubland

II. Non-Vegetated

Basic Classification Steps

1) Field reconnaissance
2) Development of classification scheme
3) Image enhancements (Veg indices, etc.)
4) Run classification algorithm
5) Incorporate ancillary data
6) Check accuracy of product
7) Refine iteratively

Classification Algorithms

• Procedures for grouping pixels and/or areas
into the classes from your classification
scheme

3/23/2020

Manual processing

• Aerial photos, print-outs of images, photos

– Transparent overlays
– Delineate features (interpretation)
– Compile maps
– Generate reports

Digital processing

• Satellite images, digital or scanned photo
• Digital on-screen interpretation (“Heads-up

digitizing”)

– Display geo-referenced image/photo on-screen
– Digital line-drawing (with mouse or digital pen) to

delineate features
• Analogous to drawing on an overlay

– Processed lines are converted to features in a
Geographic Information System (GIS)

– Generate maps and reports

Detailed view of Wyoming GAP Land
Cover Map

3/23/2020

Digital classification

• Conversion of pixel values into thematic
classes
– Statistical clustering of the data (lumping

spectrally similar pixels into the same class)
– Spectral vs. informational classes
– Sometimes combine spectral classes together

to make informational classes
– Converting digital satellite data into meaningful

maps—the heart of remote sensing!

Use many bands at once to create a map of classes

Classification

General Types of Classifications

• Unsupervised – computer clusters pixels
together based only on the similarity of their
DNs.

• Supervised – computer uses training data—
examples of target classes—and assigns pixels
to the training class that they are most similar
to.

• Others (neural networks, fuzzy logic etc).

3/23/2020

Classification Analogy

– Truck-load of fruits (pixels): Apples, oranges, kiwis,
nectarines, bananas, pineapples, tangerines,
plums, peaches, lemons (hundreds of each)

– Goal: separate them by type and put them in
separate baskets (classes)

– Using a person (= computer) who has never seen
these fruits before (!) or doesn’t know the
difference between them

Unsupervised Classification

• Software Identifies natural groups (spectral classes)
within multi-spectral data based on limited input
from the analyst

• Pixel values are grouped based similarity of their
DNs, radiance or reflectance
– Pixels => Clusters

• Analyst has to match each cluster to a thematic class

Unsupervised Classification 2-bands

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Band X0 Max

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3/23/2020

Unsupervised Classification 3-bands

1 pixel

1 Class

Unsupervised Classification
Unsupervised Classification

• Choose bands, indices, enhancements, etc.
that highlight differences in your classes

• Decide how many classes to separate
• Choose a grouping algorithm

– Simple clustering, K-means, etc.

• Classify the image
• Group and evaluate the results

3/23/2020

Advantages of Unsupervised Classifications

• No extensive prior knowledge of map area
required (but you have to label the classes!)

• Repeatable – objective classes are based only
on spectral information

• Unique spectral classes recognized as units

Disadvantages of Unsupervised
Classifications

• Spectral classes do not always correspond to
informational classes

• Limited control over the output classes you
end up with

• Spectral properties of informational classes
change over time so you can’t always use
same class statistics when moving from one
image to another

Grouping Algorithms

• Statistical routines for grouping similar pixels
together

• Differ in how they:
– Determine what is similar (distance measures)
– Determine the statistical center (centroid) of a

class
– Test the distinctivness of classes

3/23/2020

Common Algorithms

•

ISODATA

•

K-means Clustering

ISODATA

• ISODATA = Iterative Self-Organizing Data Analysis
– Choose how many classes you want
– The algorithm chooses class centers (centroid) by

spreading them evenly through the “data cloud”
– Groups each pixel with nearest centroid
– Calculates the centroid of the new cluster
– Regroups each pixel with nearest new centroid
– Keeps doing this until centroids don’t move much

K-means Clustering

• Like ISODATA but starts by picking centroids as
far apart as possible from one another in the
data cloud

• Iteratively groups pixels with the centroid and
then re-calculates centroid

• Iterates until centroid stops moving

3/23/2020

K-means Clustering

Seed the Clusters

Assign Pixels

Move Centroids

Recalculate Cluster

Members

Unsupervised Classification – Summary

• Classification is the statistical clustering of
pixels into groups

• Clusters => Thematic classes
• Results should be checked and the

classification revised if necessary

3/23/2020

Task 1 – Unsupervised Classification

3/23/2020

Image Classification

Thematic information can be mapped and further analyzed.

• Satellite images “clustered” based on spectral similarities.

• These “clusters” are then assigned into a “theme” or class

Image Classification

Derives thematic information from spectral information
• Reduces data volume

• Permits analysis of features

Class 1

Vegetation

Class 3

Water

Class 2

Urban

Water
Vegetation
Urban

Image Classification: K Means

This process organizes groups (clusters) of pixels with similar spectral responses

Spectral clusters (like land covers) are identified

K Means requires minimal input
• # of desired classes

• Iterations

• Convergence threshold

3/23/2020

Image Classification: K Means

2. Minimum Distance calculations: Each
pixel is associated with closest mean

Band 1

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Cluster Means
1. Means are initialized along diagonal

3. New mean calculated for each cluster
and means migrate to new locations

4. Iterations continue until convergence
or maximum iterations is reached

5. Each cluster associated with a value.
Each pixel given this value

Task 2 – Renaming Classes

Labelling Classes

The process of identifying land cover classes and naming them

Label
Water
Forest

Grass

Agriculture
Urban

ISODATA
Class 1
Class 2
Class 3
Class 4
Class 5

Class Names

3/23/2020

Labelling Classes

Raster attribute editor
Color assignments

Naming Classes

Making educated guesses

Forest
Water
Grass

Wheat

Forest1 (Decid)

Forest2 (Conif)

Forest3 (Mix)

Water1 (Deep)

Water2 (Shallow)

Task 3 – Recoding Classes

Recode or Merging Classes

This allows for the combining similar classes. For
example:

Water 1(Silted) and

water 2 (clear)

may be RECODED to a new class of WATER

3/23/2020

Recode (Merge) Classes

Greater class delineation by selecting more classes than ultimately desired

The likelihood of mixed classes is reduced

Water

Land

Water
Vegetation1

Vegetation2

Vegetation3

Vegetation4

Vegetation5

Resolution

Landsat ETM+
image

Types of Resolution

Spatial

Spectral

Radiometric

Temporal

Spatial Resolution

The dimension of a single pixel

The extent of the smallest object on the
ground that can be distinguished in the
imagery

Determined by the Instantaneous Field of
View of satellite instruments (IFOV)

Determined by altitude and film
characteristics for air photos.

Spatial Resolution

IFOV

1 pixel

Raster grid size

finer

Coarser

Available Resolution

Satellites: ~ .61 m to > 1 km

Air photos ~ <0.6 m to large.

Satellite data resolution

MODIS: 250 – 1000 m

Landsat MSS: 80 m

Landsat TM5, 7: 28.5 m

IRS MS: 22.5 m

SPOT: 20 m

ASTER: 15m

IRS Pan: 5 m

Quickbird Pan: 0.6 m pan

Quickbird (Digital
Globe, Inc.)

~ 2.4 m spatial
resolution in
multispectral
bands.

MODIS

500 m
spatial
resolution

Spatial Resolution Trade-offs

Data volume

Signal to Noise Ratio

 Dwell Time

“Salt and Pepper”

Money

Spectral Resolution

How finely an instrument “divides up” the
range of wavelengths in the
electromagnetic spectrum

How many spectral “bands” an instrument
records

Spectral resolution

Related to the measured range of EMR

Wide range – coarse resolution

Narrow range – finer resolution

Case 1

Measure the EMR across a wide range

E.g., the visible portion of EMR

Assign a single DN for sum of all visible
light energy hitting the sensor

Analogous to black and white
(panchromatic) film

b
lu
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re
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0.4 0.70.6

0.5

UV Near-infrared

Case 1

Case 2

Measure EMR across narrower ranges

E.g., Blue, green and red bands

Assign a DN for each of these
wavelength ranges to create 3 bands

Case 2
b
lu
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g
re
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re
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0.4 0.70.60.5
UV Near-infrared

Coarser (lower) Spectral
Resolution

Finer (higher) Spectral
Resolution

RGB

Red Green Blue

400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500
0.0
0.2
0.4
0.6
0.8

High Spectral Resolution

Low Spectral Resolution

Wavelength (nm)

R
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fl
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c
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R
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Spectral Resolution

Spectral Resolution Trade-Offs

Data Volume!

Signal to Noise Ratio

Processing complexity (time)

Money

Radiometric Resolution

How finely does the satellite divide up the
radiance it receives in each band?

Usually expressed as number of bits used
to store the maximum radiance
 8 bits = 28 = 256 levels (usually 0 to 255)

64 levels (6 bit)

4 levels (2 bit)

Radiometric resolution

1 bit ( 0 – 1)

8 bit ( 0 – 255 )

16 bit ( 0 – 65,535 )

32 bit ( 0 – 4,294,967,295 ) & more

0: No EMR or below some minimum

value (threshold)

255: Max EMR or above some threshhold

for 8 bit data type

Radiometric resolution

8 bit data (256 values)
 Everything will be scaled from 0 – 255

 Subtle details may not be represented

16 bit data (65,536 values)
 Wide range of choices

 Required storage space will be twice that of 8
bit

Radiometric resolution

1 bit 2 ( coarse )

8 bit 256

16 bit 65536

32 bit 4 Billion

64 bit ( detailed )

Radiometric Radiation Trade Offs

Data volume
Signal to Noise Ratio

Calculating Image Size

Computer hard drives store data in “boxes”
called bytes (e.g., 1 Mb = 1 million bytes)

1 byte can hold 8 binary (base 2) digits (0s
or 1s or some combination of 0s and 1s)

Each “bit” is a single binary digit

An 8-bit number is made of of 8 binary digits
and fits into 1 byte.

A 9-bit number won’t fit in 1 byte and
requires 2 bytes.

Converting Base 10 to Binary
Base 10 Base 2 (Binary)

0 0

1 1

2 10

3 11

4 100

5 101

6 110

7 111

8 1000

255 11111111

256 100000000

257 100000001 (etc.)

Temporal resolution

Time lag between two subsequent data
acquisitions for an area
 Example:

Aerial photos in 1971, ’81, ’91 and 2001

The temporal resolution is 10 years

Return Time
(Temporal Resolution)

How frequently does a satellite view the
same place?

Depends on:
 Orbital characteristics

 Swath width

 Ability to point the recording instrument

Orbital
Characteristics

• Geosynchronous

• Polar

• Sun synchronous

Geosynchronous Orbits

Satellite orbits the earth at a rate that
allows it to match the earth’s rotation—so
the satellite is always over the same place

Narrow range of altitudes—about 35,786
km above the equator.

Useful for communications, weather etc.

Example: GOES satellite (weather)
 Geosynchronous orbiting earth satellite

Polar/Sun Synchronous Orbits

Pass roughly over the north and south
poles

Fly over the same place on earth at the
same time of day (sun always in same
position)

Examples: Landsat, AVHRR

Good for land remote sensing

Return time depends on

Swath Width

Swath Width
Swath Width

Return Time Trade Offs

Spatial resolution

Viewing geometry effects (off nadir)

Clouds and other atmospheric problems

Lack of archival repeat coverage for
pointable satellites

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