midterm Flashcards
Atmospheric Window
regions that are not blocked by the earth’s atmospheric gases, so we can see the surface from space
*H20, CO2 and O3 are the main gas species that absorb photons in the VIS-TIR and block our view
*energy is interacting with gases and particulates, so no atmospheric window is 100% clear
remote sensing
the non-contact recording of information from the electromagnetic spectrum by means of instruments on platforms such as spacecraft, and the analysis of the acquired information by means of visual and digital image processing (1)
advantages of remote sensing
-unobtrusive (passive)
-unbiased data collection
- non-single point data
-data collected on site (1)
disadvantages of remote sensing
- not a solution for everything
- human-introduced errors
- emit EM radiation (active)
- uncalibrated data over time
- expensive (1)
what can remote sensing measure?
- x,y geographic location
- topographic location
- vegetation health: chlorophyll content, water, % biomass, phytoplankton
- surface/sea temperature
- soil moisture & evaporation
- atmosphere: chemistry, temperature, water %, wind speed, precipitation, clouds
- other: snow/ice, volcanoes, earthquakes, wildfires, land use, ocean health (1)
what are the two types of remote sensing?
passive and active (1)
what is passive sensing?
detection of energy from natural illumination or emission
(ex. camera, visible near-infrared sensors, thermal infrared sensors) (1)
what is active sensing?
detection of energy reflected back to the sensor after providing the illumination
(ex. camera w flash, flashlight and your eye, radar, lasers) (1)
what are the five types of energy/matter interaction that can take place?
reflected, scattered, transmitted (refracted), absorbed, emitted (1)
energy is reflected. what does that tell you?
- energy returned from the surface with an angle reflection equal and opposite to angle of incidence
- caused by surfaces “smooth” relative to the incident wavelength (1)
energy is scattered. what does that tell you?
- deflection of energy in multiple directions
caused by surfaces “rough” relative to the incident wavelength (1)
energy is transmitted. what does that tell you?
-energy passes through the material
- a change in density (index of refraction) between two materials causes the velocity of the incident wave to change (1)
energy is absorbed. what does that tell you?
- energy is transformed (usually to longer wavelength heating) (1)
energy is emitted. what does this tell you?
- release of energy from the material (it is now the source) (1)
properties of EM waves
- waves have a constant velocity in a vacuum
- waves vary in wavelength and frequency by the following equation: v = c/λ
– c=speed of light, v= frequency, λ=wavelength
-E=hv
– E= energy of the wave, h=Planck constant, v=frequency - SMALLER WAVELENGTHS (HIGHER FREQUENCIES) HAVE HIGHER ENERGY
– ex. X-rays penetrate deeper and are more damaging to your body than radio waves (1)
what are the units of v in v=c/λ
Hz
(frequency)
what are the units of λ in v = c/λ
m
what are the units of E in E = hv?
Joules (J)
gamma rays
-produced by change in the energy state of the neutron/protons
-best for measuring variations in elements (light ones) (1)
x-rays
photons absorbed by the inner shell of electrons (1)
UV
-photons emitted/absorbed by the outer shell of electrons
-information on transition metals and chlorophyll
– Fe, Cu
(1)
VIS
photons emitted/absorbed by the outer shell of electrons (1)
NIR
photons emitted/absorbed by the outer shell of electrons (1)
SWIR
vibrational structure of certain materials
– OH- (hydroxide)
– CaCO3 (calcium carbonate) (1)
TIR
- info on the molecules and bond strength
- excellent for surface mineralogy
- information on surface temperature (1)
microwave (radar)
-radar wavelengths good for remote sensing
-little information about composition, but a lot about the particle size and surface roughness (1)
color theory (key point of general theory)
-color display and mixing is different than common (1)
contrast ratio (CR)
tells you how easily you can separate gray-scale values
-human eyes can only distinguish about ~30 shades of gray
- white = 255, black = 1
(1)
primary or additive colors
-red, green, blue
- R+B+G=white, -R-G-B=black (1)
true color vs. false color
-true color: RGB corresponds to the actual RGB wavelengths that your eye sees
-false color: RGB is used to display other wavelength regions (1)
secondary colors
three secondary colors formed by the subtraction of one color from white
-R = cyan
-G = magenta
-B = yellow (1)
what plant chemicals/colors tell us
-Chlorophyll reflects green and NIR
-Anthocyanin reflects red/yellow (wavelengths longer than green)
-a graph of a healthy plant shows a small peak in green and a large peak in NIR (1)
how do chemical components of objects interact with incoming light
-the light is altered and reflected back as a continuous spectrum at all wavelengths
-the spectrum are averaged together and captured by the satellite sensor
-the sensor can’t capture al wavelengths (spectral resolution)
-putting it in the computer produces color (1)
what two things determine the interaction with EM wavelengths and the surface
the sensor and the surface material (composition texture) (1)
function of the sensor in interacting with the surface
-the spatial resolution (depends on the altitude and the instrument characteristics)
-the sensitivity of the detector
-number of wavelength bands (1)
function of the surface being sensed
chemical composition, roughness, temperature, distance from the sensor (1)
imaging characteristics (pixels)
-the quantized spatial resolution of the image
-displayed as a square as the image is zoomed in
-value is recorded as DNs (1)
image display
able to display only 1, 8-bit image in each of the 3 primary colors
-the mixing of these colors produces all other colors (1)
spatial resolution
-the size of the pixel
-determined by height of the sensor and instantaneous field of view (1)
pixel size equation, values, and units
-pixel size = H x IFOV
-H = height, m
- IFOV = instantaneous field of view, radians (1)
order to follow when altering an image
fix up the data, remove errors, calibrate
then extract quantitative info (1)
density slice
-visualization tool to add color to a gray-scale image
-DN range is divided into groups and assigned a color (1)
histogram
-distribution of all the DN values for an image, single band, or subset thereof
-for an image with a large variation of DN values, the corresponding histogram is generally normally-distributed with a mean at some DN value (1)
linear stretch
-application of a linear equation to the data
-2% linear stretch = lowest 2% is fit to 0, highest 2% is fit to 255
-stretching or separating the data to cover most/all of the available dynamic range (0-255) is known as a stretch
-Lightens/darkens the image
-Always just an equation of a line, you’re determining what equals 0 and what is 255, then making the rest of the data fit that line
-CAN NEVER BE USED TO EXTRACT QUANTITATIVE INFORMATION (1)
gaussian stretch
-fit of the histogram to a gaussian distribution WHICH IS JUST A NORMAL DISTRIBUTION (1)
Band Ratios
-used to extract information in multi-spectral images
-Dividing one spectral band by another produces an image that provides relative band intensities
-highlights subtle spectral and/or temporal variations
-Done after atmospheric correction and conversion to surface units
-Ratios reduce topographic and albedo effects
(1,4)
what does NDVI stand for
normalized difference vegetation index (1)
NDVI band ratio
NDVI = (TM4 - TM3) / (TM4 + TM3)
-produces values from 0-1, with a higher value implying healthier vegetation (1)
what is spectroscopy?
science and analysis of the EM spectra of materials
- the type of spectroscopy is a function of the wavelength region under study
- diff wavelengths tell you diff things about the surface (4)
electronic processes (energy interaction)
-interested in reflection (reflection dominates VNIR)
- Our goal is detect presence/absence of photons
(4)
Reflectance
- reflectance = measure of how much incident EM energy is reflected from the surface, giving us
R(λ)=Eref/Einc - Perfect reflector R = 1, No energy reflected R = 0
(4)
Three Factors that Control amount of reflectance
- Physical properties of material (n-refraction and k-absorption)
- Particle size
- Wavelength - longer wavelength = no longer able to interact with individual parts of atoms, but interacts with bonds
(4)
What is n?
- Index of refraction
- Varies with wavelength
(4)
What is k?
- Absorption coefficient
- Varies with wavelength
(4)
Relationship between k and n?
- Inversely related
Two components of reflectance
- Volume component - energy is scattered through the mineral grains in the spectral regions of low k(lambda)
- Specular component - energy reflects off the mineral grains in the spectral regions of high k(lambda)
(4)
Formula for Reflectance
- Reflectance = volume component + specular component
- R(lambda) = rv(lambda) + rs(lambda)
(4)
Particle size
change in size and effect on reflectance
- Change in particle size has net effect of increasing (or decreasing) the volume transmitted component depending on k(lambda)
- Tiny particles can appear to have a higher reflectance which SIMULATES a higher k(lambda), but is not actually a higher k(lambda)
(4)
Electronic Spectral Processes
- the VNIR region is dominated by the spectral features arising from the electronic (electron - ic) transitions
(4)
Charge transfer effect
- Incident photons raise electron energy state of the mineral, causing the outer electrons to migrate to other ions in the lattice
- Results in complete absorption of incident energy from the UV to visible green
- common in iron-bearing materials (Fe - O)
- Results in their reddish coloration
- TLDR: photons cause electrons to move around lattice, absorb UV through green
(4)
Electronic transition
- Common in transition metals (Fe2+, Fe3+. Cu2+, etc.)
- Complete transfer of electrons
- Produces reflectance minimal in 0.9 - 1.2 micron region
(4)
Conduction band absorption
- Similar to charge transfer only stronger
- Produces a prominent and sharp reflectance increase from VIS green to IR wavelengths
- Common in sulfur-bearing minerals
(4)
Vibrational Processes
- Occur from 2.0 - 100 micron wavelengths (short wave IR to thermal IR)
- Cause bending/stretching of atomic bonds
- Most common Earth forming minerals show strong features
- Produces spectral features in the 2-3 micron region in the short wave IR - water, clay
NDMI
- Normalized Difference Moisture Index
- NDMI = (TM4 - TM5)/(TM4 + TM5)
- Good indicator of “stressed” vegetation
- Values from -1 to 1, higher NDMI implies greater abundance of moisture content
OH- bearing rocks (clays, etc.)
band ratios
- TM5/Tm7
- Absorption (low reflectance) in TM7 (2.2 microns)
- Large values in ration -> strong OH-
Fe2+ bearing rocks (basalts, “red-beds”, etc.)
band ratio
- (TM5/TM4) x (TM3/TM4)
- Strong curvature between TM3 and TM5
- Large values in the ratio: strong Fe2+
path radiance
any energy contributed by interactions with the atmosphere over the path-length prior to detection
path-length
distance traveled through the atmosphere by a photon
*function of the location of the energy source, location of the sensor and the wavelength
transmissivity
measure of the fraction of energy that passes through the atmosphere unattenuated (varies between 0 and 1)
*T = 1 (perfectly clear atmosphere)
selective rayleigh scattering
caused by particles much less than the size of the scattered wavelengths
*example- atmospheric gases (N2, O2, O3)
selective mie scattering
caused by particles about equal to the wavelength
*example- dust, smoke, aerosols
non-selective scattering
caused by particles much larger than the wavelength
*example- water vapor, ice crystals
*produces haze, clouds etc
framing camera using digital imagery or directly on film
*positives- high spatial resolution, low costs, large amount of data captured
*negatives- limited spectral range, non-digital, higher amounts of geometric distortion away from the image center
ground resolution
ability to resolve ground features
Rg= (Rs x f)/H
*Rs= system resolution (mm); f= focal length of the camera (mm); H = camera height above ground (m)
scale
f/H
*commonly written as 1:20,000
*1 mm on the photograph= 20,000 mm (20m) on the ground
relief displacement
geometric distortion at image edges giving the effect that taller objects are “leaning” away from the optical center of the photo
*distortion amount is related to:
1. vertical height of the object
2. distance from the principal point
3. inversely proportional to the camera height
*h = (H x d)/r
- where h= actual height of the object (m); H = camera height above ground (m); r = distance from image center to the top of the object (m); d = relief displacement
*removal of large-scale relief displacement produces an “orthophotograph”
stereo-pairs
successive overlapping air photos
*because each photograph images each point on the ground from a slightly different angle, the offsets can be used to reproduce the vertical dimension
*known as DEM (digital elevation model)
*what are used to produce USGS topographic maps
low sun angle
images taken generally early morning, late afternoon, or at high latitudes, where the sun is < 15 degrees above the horizon
*produces pronounced shadows if object is perpendicular to the sun
*excellent for interpretation of subtle topographic features
high sun angle
shows no topographic difference, can do spectroscopy
scanners
systems used to build up electronic images line by line/row by row
dwell time
scan per line/number of cells per line
*in other words, the amount of time a scanner has to collect photons from a ground resolution cell
cross-track scanners
*rotation or “back and forth” motion of the foreoptics (mirror)
*scans each ground resolution cell (pixel) one by one
along-track scanners
*mulitple cross-track scanners (no scanning motion)
*positives- dwell time increases
*negatives- large arrays are more difficult to fabricate
whisk-brook scanners
*combination of a cross-track scanner and a push-broom scanner
*scan with a small line array of detectors
*long dwell time
push-whisk scanners
*scanning is done with a line array of the same wavelength
*add a cross-track scanning with a line array
*scanning is performed with a line array of detectors at different wavelengths
*negatives- short dwell time, imprecise alignment
spectral resolution
spectrum for each pixel over the number of instrument channels
Hyper - you can measure smaller and smaller points
Multi - taking 4-8 points
Takes basic shape, not small details
data transforms
*corrections to the data due to scan errors, sensor position, motion
*transformations (new coordinate systems, image mosaics
non-systematic distortions
*uncontrolled variations
*much more complicated to correct
systematic distortions
*regular occurrence
*example: edge foreshortening due to scan angle
*easier to correct
panoramic distortion
*systematic error in whiskbroom style instruments
*gets worse away from the nadir (non-vertical position)
distortion due to the earth’s curavture
*systematic error seen in geostationary sensors (further from earth)
*effects images covering a large portion of the earth’s surface (i.e. weather satellites)
*produces the opposite effect as panoramic distortion
*images are elongated with respect to image center
over/under sampling
*NON-systematic error
*function of the sensor velocity vs. scan rate
*most common on aircraft scanners
- if sensor is moving too fast for scan rate, then under-sampling occurs and there are gaps in the data (image appears compressed in flight direction)
-if sensor is moving too slow for scan rate, then over-sampling occurs and there are scanned more than once (image appears elongated in the flight direction)
image classification
*algorithms designed to clump data into certain classes in order to minimize scene variability and extract certain user-defined parameters
*what determines good class? covers more than one pixel, mathematically valid (need 10 x (n+1) pixels per class, n = number of spectral bands of the instrument)
supervised classifications
minimum distance (to means)
*simplest method
*determines the distance to the mean DN value (in n dimensional space) of each class and assigns unknown pixels to the class with a mean closest to that pixel
*limitation- ignores the shape (variance) of the data cloud
-can cause errors if an unknown pixel lies near/within one class, but is closer to the mean of another class
unsupervised classifications
limitation: all unsupervised classifications may produce non-intuitive classes
*user must still interpret the results
different algorithms for unsupervised classifications
*k-means approximation
-user specifies number of classes
-algorithm locates cluster centers
-computes statistically significant number of classes
*iso-approximation
-user seeds the algorithm with a min and max number of data clusters
-then a k-means (or other rule) is performed
maximum likelihood
*creates a n-dimensional parallelepiped or ellipsoid around the DN values of each class
*statistically determines whether an unknown pixel falls within that shape
*most accurate method of classification
*limitations- longer computer run time, requires a large number of pixels for accuracy
accuracy assessment
*user validation and check of the classification accuracy is critical
*without it, the results of the classification could be wildly incorrect
*check can be performed via field work, use of higher spatial resolution data, or other (non-raster) datasets
LANDSAT HISTORY
Landsat 1
*launched in 1972, MultiSpectral Scanner (MSS)
Landsat 2 + 3
*also constained MSS
Landsat 4
*lower orbit (higher spatial resolution) and later overpass time (less shadows)
*began to transmit data to numerous ground receiving stations and satellites
Landsat 5
*operational for 29 years! retired in 2013
Landsat 6
*failed to reach orbit
Landsat 7
*new instrument: Enhanced TM (ETM+)
*doubled the resolution of the thermal IR band
*added a new “pan-band”
-averages the wavelengths of bands 2,3,4,
Landsat 8
*new instrument: Operational Land Imager (OLI)
*push broom rather than whisk broom
*5 to 10 year life
*new instrument: Thermal Infrared Sensor (TIRS)
- also push broom
-new array material
Landsat 9
*duplicate of landsat 8
Landsat orbits
*inclined polar orbit
-sun-synchronous
-meaning the same overpass time (~10:15 am/pm) at the equator every time
*repeat cycle of 16 days (233 distinct orbital paths)
*all images are referenced to specific Row (N-S) and Path (E-W) grid system
