This is a Remote Sensing Test I uploaded its got 4 pages worth of questions. Please use the Internet and my uploads for answe

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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

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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

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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

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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

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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

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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

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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

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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)

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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

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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

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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

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landslides

Southern Bainbridge Island

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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.

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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 = εσT4

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

• Peak λ(µm) = 3000/T(oK)

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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

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Thermal Remote Sensing

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

• 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

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Atlanta — Daytime Atlanta — Nighttime

Daily change in radiant temperature of common objects

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Thermal Infrared Multispectral Scanner (TIMS) image of Death Valley

Daytime Positive – Bright = warm, Dark = cool

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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.

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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

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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.

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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

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Thermal Image of Lava Flows

ASTER

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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!

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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.

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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

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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

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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

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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

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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).

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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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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

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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

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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

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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

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Task 1 – Unsupervised Classification

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Resolution

Landsat ETM+ image

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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

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IFOV

1 pixel

Raster grid size

finer

Coarser

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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.

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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

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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 e

g re e n

re d

0.4 0.70.60.5 UV Near-infrared

Case 1

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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 e

g re e n

re d

0.4 0.70.60.5 UV Near-infrared

Coarser (lower) Spectral Resolution

Finer (higher) Spectral Resolution

RGB

Red Green Blue

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

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0.2

0.3

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0.5

0.6

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0.8

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

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High Spectral Resolution

Low Spectral Resolution

Wavelength (nm)

Wavelength (nm)

R e

fl e

c ta

n c e

R e

fl e

c ta

n c e

Spectral Resolution

Spectral Resolution Trade-Offs

Data Volume!

Signal to Noise Ratio

Processing complexity (time)

Money

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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

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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

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Calculating Image Size

Computer hard drives store data in &#x201C

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