Lecture 19:Introduction to Remote Sensing

PrinciplesBy Austin Troy

University of Vermont

------Using GIS--Introduction to GIS

Thanks are due to Jarlath O’Neil Dunne, upon whose lecture much of this material is based, and whose graphics were used in many slides for this lecture

©2005 Austin Troy

What is remote sensing•Remote sensing is “the science and art of obtaining information about an object, area or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area or phenomenon under investigation.” (Lillesand and Kiefer 2000)

•In the case of geography, this refers to sensing of electromagnetic energy operated from airborne or spaceborne platforms.

•These sensors collect data on how earth surface features emit and reflect electromagnetic energy

Introduction to GIS

©2005 Austin Troy

Why is remote important•Remotely sensed imagery is the original source for most of the GIS data we use

•RS data can be used to assess ground conditions over a very large area

•RS data allows us to look at changes in the environment

Introduction to GIS

©2005 Austin Troy

Some Applications•Planning and transportation

•Road updates

•Infrastructure monitoring

•Growth monitoring

Introduction to GIS

Source: Halcon

©2005 Austin Troy

Some Applications•Natural resource mapping

•Tree cover

•Tree conditions

•Crop conditions

•Yield estimation

Introduction to GIS

Clubroot disease

Source: NGIC

©2005 Austin Troy

Some Applications•Natural resource mapping

•Land use change analysis

•Habitat and natural communities mapping

Introduction to GIS

Source: TRIC

©2005 Austin Troy

The Physics of RS•Remote sensing data are collected in the electro-magnetic radiation spectrum, principally the visible, infra-red and radio regions

•Passive RS systems collect data on energy that is reflected or emitted from the earth

•Most systems are passive, except for microwave and radar, which are active sensing mechanisms.

•Most RS platforms record reflectance in multiple wavelengths spectrums

Introduction to GIS

©2005 Austin Troy

Physics of RS•EM radiation exists across a range of wavelengths, referring to distance between two peaks

Introduction to GIS

Source: http://rst.gsfc.nasa.gov/Intro/Part2_2.html

©2005 Austin Troy

Physics of RS•The visible spectrum constitutes a small portion, bounded by ultraviolet spectrum below and the infrared spectrum above

Introduction to GIS

Source: http://www.sci-ctr.edu.sg/ssc/publication/remotesense/em.htm

©2005 Austin Troy

Physics of RS

Introduction to GIS

Ultra-Violet

Visible Near IR Shortwave IR Midwave IR

Longwave IR

Pan

chrom

aticB

lack &

Wh

ite F

ilm

Color Film

.01 .04 .07 1.0 3.0 5.0 14.00 um

Color IR Film

Spectral Imagery

Visile

Comprises 2%of EM Spectrum

Wavelength(Micrometers)

Source: Jarlath O’Neil-Dunne

©2005 Austin Troy

The Physics of RS•Light can either be reflected, absorbed or transmitted on a surface, and the proportion of those three will vary at each wavelength for a given object.

•Reflection is emission of photons caused by excitation of the surface, due to incident radiation

•Reflected E = incident E - absorbed and transmitted E

•This relationship varies in each wavelength

•This is why two features may appear similar in the same wavelength band, but distinguishable in different wavelength band

Introduction to GIS

©2005 Austin Troy

The Physics of RS

•The wavelengths in which it is reflected determine the color of the object

Introduction to GIS

High

Low

Blue Green Red

Ref

lect

ance

0.4m 0.5m 0.6m 0.7m

White LightGreenGreen

BlueBlue

RedRed

Source: Jarlath O’Neil-Dunne

©2005 Austin Troy

The Physics of RS•Spectral reflectance = E(r)E(i)

Or the proportion of reflected to incident radiation

•The power of emitted photons in each wavelength depends on the surface.

•An RS sensor can detect spectral responses from objects in various wavelength ranges.

•Each class of objects has a different spectral responses across wavelength

•Spectral reflectance values of an object can be plotted on a graph as a function of wavelength, known as a spectral reflectance curve.

Introduction to GIS

©2005 Austin Troy

The Physics of RS•Each object feature class on the earth has a spectral reflectance curve that helps us to identify it remotely. This is why we can use RS to tell the difference between types of objects

•A spectral response pattern delivers much more information than a single pixel value

•Spectral response usually plotted as an “envelope” of values rather than a line, because the relationship varies within a range for a given class of object

•RS sensors only look at small portion of the x axis

Introduction to GIS

©2005 Austin Troy

The Physics of RS•A spectral reflectance curve for several very different classes of object; note how different the responses are

Introduction to GIS

Source :Lillesand and Kiefer 2000. Remote Sensing and Image Interpretation Wiley and Sons

©2005 Austin Troy

The Physics of RS

Introduction to GIS

50

40

30

20

10

0

0.4 0.6 0.7 0.8 1.3

Artificial turfAsphalt

Fallow field

Sandy loamy Soil

Concrete

REFLECTANCE

(%) Clear water

Wavelength (micrometers)

Grass

Visible0.5

GREENBLUE GREEN RED

Near IR

Source: Jarlath O’Neil-Dunne

©2005 Austin Troy

The Physics of RS

Introduction to GIS

•A Spectral reflectance curve for two classes of similar object: conifers and deciduous trees

•Note how visible band is similar, but near IR band is very different: means eye could not pick this up

•The shape of an objects curves will determine what bands we use to ID it

Source :Lillesand and Kiefer 2000

©2005 Austin Troy

The Physics of RS

Introduction to GIS

•Panchromatic B&W: can’t tell deciduous from conifer

Source :Lillesand and Kiefer 2000

•Infrared B&W: can clearly see deciduous because higher reflectance in those wavelengths

©2005 Austin Troy

The Physics of RS

Introduction to GIS

© Space Imaging © Space Imaging

Green Reflectance NIR Reflectance

©2005 Austin Troy

The Physics of RS

Introduction to GIS

•RS hardware’s ability to sense in these non-visible wavelengths allow us to visualize things we normally could not perceive with the human eye, like water temperature

Source :Lillesand and Kiefer 2000

©2005 Austin Troy

The Physics of RS

Introduction to GIS

•Here’s one showing suspended sediment in San Francisco Bay

Source :USGS

©2005 Austin Troy

The Physics of RS•Atmosphere has a big impact on RS imagery

•Scattering of light degrades the image in shorter wavelengths, particularly the ultraviolet and blue

•The scattering causes “noise” which reduces contrast in these wavelengths

Introduction to GIS

©2005 Austin Troy

The Physics of RS•Many wavelengths are also absorbed by gases in the atmosphere, including CO2 and O3

Introduction to GIS

Source :Lillesand and Kiefer 2000

©2005 Austin Troy

So, what are RS data?•RS imagery is raster data

•Each pixel has a geographic coordinate and reflectance/intensity value, or digital number (DN).

•The dimensions of the area represented by a single pixel defines the resolution

•High resolution images have small pixel size, like 1 meter square, while coarse images have large pixel size, like a square kilometer

Introduction to GIS

©2005 Austin Troy

Introduction to GIS

Source: http://www.sci-ctr.edu.sg/ssc/publication/remotesense/em.htm

©2005 Austin Troy

RS Data•RS data also have “radiometric” resolution, which is the smallest change in reflectance value, or intensity level that can be detected by the system

•This, like with any raster image, is determined by the data bits

•The fewer pixel values, the less realistic, and the more abrupt the changes look

Introduction to GIS

©2005 Austin Troy

Panchromatic imaging•With panchromatic imaging, the sensor is a single channel detector sensitive to radiation in a broad wavelength range. Where the wavelength range coincides with the visible range, the resulting image resembles a "black-and-white" photograph. The physical quantity being measured is the apparent brightness of the targets. The spectral information or "colour" of the targets is lost.

Introduction to GIS

©2005 Austin Troy

Multispectral Imaging•With multispectral, or multiband data, there are several layers, or values for each pixel, each representing a different “channel” or reflectance in a wavelength spectrum

•Each “band” or “channel” is sensitive to radiation within a different band of wavelength, through use of different filters

•The sensor takes an average value for the spectral window in which it is sensing; that is, it averages the curve within that region

Introduction to GIS

©2005 Austin Troy

Multispectral Imaging

Introduction to GIS

40

30

20

10

0

0.4 0.6 0.7 0.8 1.3

Concrete

REFLECTANCE

(%)

Wavelength (micrometers)

Grass

Visible0.5

GREENBLUE GREEN RED

Near IR

Source: Jarlath O’Neil-Dunne

©2005 Austin Troy

Multispectral Imaging• These bands can be combined to make “composite” images, can be looked at separately, or can be analyzed using overlay analysis methods

•One band from a multispectral image would be displayed as a grayscale image, with each pixel represented by a grayscale value

•When three layers are combined, they can be assigned to the three color channels (red, green, blue) to make a display that appears to us in humanly visible colors, although they may represent colors outside the visible spectrum and may not coincide with the real colors

Introduction to GIS

©2005 Austin Troy

Multispectral Imaging•Here is an example of three bands, green, red and near infra-red displayed separately as grayscale

Introduction to GIS

green

red

Near-infra red

Source: http://www.sci-ctr.edu.sg/ssc/publication/remotesense/opt_int.htm#multispectral

©2005 Austin Troy

Multispectral Imaging•The “best” combination of bands will depend on what the object is that’s being sensed, and what it’s spectral response curve looks like.

•For instance, if we go back to the conifer-deciduous example from before, we know that near IR is the key band for differentiating the two, but this won’t be so for all object types

•The key is to get the band that best shows contrast between two feature classes that may be indistinguishable to the human eye

Introduction to GIS

©2005 Austin Troy

Multispectral Imaging•True color composite: where bands are assigned to color channels in such a way that colors in the image roughly correspond with the colors in the real world. Often assigned red to red, green to green and blue to blue can result in this

• Another is a false color composite, which shows colors that don’t really exist in that location. An example is color infrared composite, where green band is assigned to blue display channel, red is assigned to green and Near IR is assigned to red

Introduction to GIS

©2005 Austin Troy

Multispectral Imaging•Composite images are usually displayed by assigning three of the bands to the red, green and blue channels and displaying them additively. This can be done in image processing software.

•For instance, here in AV we can assign bands to channels in the legend editor

Introduction to GIS

©2005 Austin Troy

Multispectral Imaging

Introduction to GIS

BLUEBLUE

GREENGREEN

REDRED NEAR IR SHORT

WAVE IRMID-

WAVE IRLONGWAVE IR

1Landsat TM Band 2 3 4 5 7 6

Band Combination = 7 4 2 (LANDSAT)

Color Guns =

Band Composite Output =

Source: Jarlath O’Neil-Dunne

©2005 Austin Troy

Multispectral Imaging•Here are examples of simulated normal color composite (top) and simulated IR color (bottom)

•Other bands can be used for composites as well

Introduction to GIS

Source :Lillesand and Kiefer 2000