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GIS and Remote Sensing
o Visualizing the world
 Raster – grid, pixels with a location and value (satellite and aerial images
format)
 Vector – linear, features with points lines and polygons (Attributes)
 Converting between the two – vector to raster is most
straightforward and can look similar with small pixel size
o Raster to vector, problematic grid size
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Must be geometrically aligned – data must me geometrically rectified and
registered to a suitable projection and coordinate system
 Creating GIS layers
o Heads up digitizing
o Classification
 GIS Raster data sets – grid/matrix based – interchangeable with remotely sensed data
(.GEOTIFF)
 Merging GIS data – GIS data in support of image correction – radiometric can be sued to
find invariant features, geometric can us GCPs to register image and layer
 Linking with census data
 Uses – Delimiting Study Area (park boundary), Potential Training sites
(supervised class), Geographical stratification (elevation, veg type), Reference
data set for error analysis
 GIS operations
 Attribute Re-coding – changing the values of the data (thematic outputs)
 Re-scaling – changing the resolution (degrading, MMU)
 Algebra – treats map values as variables that can be transformed or combined to
create new layers
 Focal, zonal and regional operations – compute each location’s new value as a
function of the existing value within a specified distance
o Majority filter, clump, sieve
 Model maker – compare data sets
Radiant Earth Foundation - non-profit organization founded in 2016
o Neutral organization - back-end infrastructure for global development
o Mission - open geospatial data for positive global impact - connecting people globally to Earth
Imagery, geospatial data, tools and knowledge to meet the world’s most critical challenges
Biophysical remote sensing - application of physical principles and methods to biological problems
(inferring info on - environmental structures, processes, 9 fundamental variables)
o Vegetation - 70% of the earth’s land surface is covered by vegetation
 Reflects NIR
 Spongy Mesophyll
 Absorbs red
 Chlorophyll
o Changes in vegetation - timing is very important (growth cycles/crop cycles)
 Climate change
 Crop Mapping
Spectral Vegetation Indices (SVI) - dimensionless, radiometric measures that function as indicators of
relative abundance and activity of green vegetation
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Reduces the spectral signature to a single, quantitative,
value (generally based on red-NIR and differences in
response)
o Common ones
 NDVI - (NIR - Red)/(NIR + Red)
 Simple Ratio - (NIR/Red)
o Physical relationships - relative measure of abundance,
health and stress
 Crop disease/stress
 Leaf area index (LAI) - related to total biomass of
vegetation - influence by the amount of reflecting
soil between plants
 Carbon fluxes
o Applications - Global change issues - landscape pattern & process, vegetation change,
biogeochemical cycling processes (carbon cycling, quantify decomposition process)
Change Detection - change in land cover over time
o Why important
o Why remote sensing
o Steps
 1. Define study area
 2. Determine temporal scale for change
 3. Select appropriate classification system - classes compatible with remote sensed data,
comparable through time
 4. Select resolution characteristics of image data - should be consistent across all dates of
imagery
 Anniversary Dates - limits effects of illumination differences, limits effects of
seasonal/phenological differences
 Spatial - same pixel size & accurate spatial registration
 Spectral - same bands or best approximation
 Radiometric - same radiometric resolution
 5. Minimize effect of environmental considerations
 Atmosphere - cloud cover, Anniversary date imagery can minimize seasonal
weather variation
 Soil moisture - conditions same for both dates, rainfall levels
 Phenology - terrestrial & aquatic ecosystems, man made development cycles
 Urban-Suburban - stages: undeveloped -> landscaping, some classes may be
spectrally similar
 6. Acquire image and ancillary data
 7. Preprocess data - geometric and radiometric registration
 8. Select change detection algorithm
 9. Compute area and type of change
 10. Assess accuracy
o Algorithms
 Band overlay (write memory insertion) - place image in different color plane of image
display, provides a visual display of changes
 Limitations - not quantitative, no “from-to” information
 Multi-date classification – PCA, classification - composite image - classification of all
bands, areas of change will form their own class
 Limitations - may be difficult to label change classes
 Image Algebra - image differencing - subtract same spectral band for two different dates
 Limitations - require setting change/no change thresholds, no information on
type of change
 Ratio & Differencing
 Post classification - classify images from both dates - change is determined from
classified images - “from-to” information,
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Limitations - dependent on accuracy of classification, errors impact change
detection
Map or classification as first date - using existing land cover data in place of
remotely sensed image for one date
o Depends on quality of classification and ancillary data
Heads Up Digitizing
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Thermal Infrared
o Everything emits energy - emitted from all objects that have a temperature greater than absolute
zero
 Longer wavelengths - sensed primarily through touch - TIR (3.0 - 14 μm)
o Temperature - high positive correlation between the true kinetic temperature of an
object (Tkin) and the amount of radiant flux radiated from an object (T rad)
 Kinetic heat - energy of particles of molecular matter in random motion converted to radiant energy
 Radiant flux - electromagnetic radiation exiting an object
 Radiometric temperature - concentration of the amount of radiant flux
emitted from an object
o Emissivity (ε) - the ratio between the radiant flux exiting a selective radiating body
(Fr) and a blackbody at the same temperature (Fb)
 Ε = Fr/Fb - emissivities ranging from 0 to <1
 Blackbody - a theoretical construct that absorbs all the radiant energy
striking it and radiates energy at the maximum possible rate per unit area
at each wavelength for any given temperature (Nothing in nature is a true
blackbody)
 Variations between objects & with wavelength - emissivity influence by a
number of factors (color, roughness, moisture, compaction, viewing angle)
o Kirchoff’s Law - in the infrared portion of the spectrum the spectral emissivity of
an object generally equals its spectral absorptance
 Good absorbers are good emitters and good reflectors are poor emitters
 ε(l)≈a(l)
 All of the energy leaving the object must be accounted for by the inverse
relationship between reflectance and emissivity
 If reflectivity increases, then emissivity must decrease. If
emissivity increases, then reflectivity must decrease
 Stefan-Boltzman
 Relationship between Tkin and Trad - TIR systems generally record
the apparent radiant temperature of the terrain rather than the true kinetic
temperature
o Trad4 =εTkin 4
 Emissivity - therefore the radiant temperature of an object recorded by a sensor
is related to its true kinetic temperature and emissivity by the following
relationship
o ε = (Trad/Tkin )4
o Influence of emissivity on T rad
o Atmospheric effects
 Gases and Aerosols - can emit or absorb energy - thermal sensors can be biased as much
as 2 C when acquired as altitudes as low as 300m
 Cloud Effects - generally blocks thermal radiation
o Sensors
 NOAA AVHRR - Thermal Bands (3.55-3.93μm, 10.3-11.3μm, 11.5-12.5μm)
 NOAA Geostationary Operational Environmental Satellite (GOES) - 8 km, every 30
minutes
 Moderate Resolutions Imaging Sensor (MODIS) - 1 km resolution, 17 bands (mid - TIR),
daily coverage
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Advanced Spaceborne Thermal Emission and Reflection Radiometer
(ASTER) - 30-90 m resolution, 6 MIR bands, 5 TIR bands, surface
temperature product
o Day and Night differences - allows for determining different surface materials
 Some materials respond to changes in temperature more rapidly than
others
o Applications
 Houses, Fisheries, Energy Balance, Sea surface temperature
 Fire Mapping - smoke plumes consist of ash particles and other
combustion products so fine that they are penetrated by relatively
long TIR wavelengths
 TS-NDVI relationship - physical explanations for the relationship is
amount of energy partitioned into LE - fractional vegetation cover,
cold background due to permafrost
RADAR systems - Radio Detection and Ranging - transmit from sensor to terrain, interacts
with the terrain producing a backscatter of energy, energy received and recorded by sensor
o Radar Wavelengths - pulse of the electromagnetic radiation sent out by the
transmitter through the antenna is of a specific wavelength and duration
 Much longer than visible, NIR, MIR, TIR - usually measured
in centimeters
SLAR - Side looking Airborne Radar
o Directions & Angles
 Azimuth - line of flight of the aircraft
 Range - direction of radar illumination that is at right angles to the
direction of the aircraft (significant impact on feature interpretation)
 Depression Angle (γ) - the angle between the horizontal plane and
extending out from the aircraft fuselage and the electromagnetic pulse of
energy from the antenna to a specific point on the ground
 Near range - closest point to the aircraft
 Far range - furthest distance that the beam is sent away from the
aircraft
 Incident Angle - the angle between the radar pulse of electromagnetic
energy and a line perpendicular to the Earth’s surface where it makes
contact
 Flat terrain - incident angle + complement of the depression
angle (sum of both = 90)
o RADAR logic - by electronically measuring the return time of signal echoes, the
range, or distance between the transmitter and the objects may be determined
 Slant range distance - direct distance between transmitter and object
 SR = ct/2
 c= speed of light
 t= time between pulse and transmission
o Spatial Resolution
 Range Direction - proportional to the length of the microwave pulse (shorter
= finer resolution)
 Speed of light
 Depression angle - a function of the depression angle
o Rr = (τ X c)/(2cosγ)
 Pulse length (τ) - a function of the speed of light X the duration of
transmission (t)
 Azimuth resolution - computing the width of the terrain strip that is illuminated
by the radar beam (the beam width is inversely proportional to antenna length)
 Longer the radar antenna = narrower beam = higher resolution
 Antennae length (L), slant range distance (S), wavelength (λ)
o Ra = (S X λ)/L
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Influences on Radar Return o Terrain
 Foreshortening - all terrain that has a slope inclined toward the
radar will appear compressed or foreshortened relative to slopes
inclined away from the sensor
 Affected by
 Object height - greater the height of the object- the
greater the foreshortening
 Depression angle - greater the depression angles the
greater the foreshortening
 Location of objects in the across-track range - features
in the near-range portion of the swath are generally
foreshortened
 Image layover - extreme case of image foreshortening - occurs
when the incident angle is smaller than the foreslope
 Shadowing - backslope is a shadow when its slope angle is
steeper than the depression angle
 Grazing illumination - the backslope equals the
depression angle
 The backslope is fully illuminated when it is less than
the depression angle
 Radar shadows occur only in the across-track dimension therefore the orientation of shadows in a radar image provides
information about the look direction and the location of the
near and far range
o Polarization Effects - can send and receive in the same or different
polarized energy
 Objects on the ground modify the polarized energy they reflect
 Usually terrain returns energy in the same polarization
 Vegetation - multiple reflections “volume scattering” of
incident energy is depolarized - comes back varied
o Surface Roughness- terrain property that most strongly influences the
strength of the radar backscatter – micro-scale roughness measured in centimeters (rough,
intermediate, smooth)
 Smooth areas send very little backscatter toward the antenna - dark in image
 Intermediate surface - 0.17 - 0.96 cm - grey in image
 Rough surface - 0.96 cm - brighter in the image
 Diffuse reflector - rough surfaces - scatter incident energy in all direction and return a
significant portion of the energy to the radar antenna
 Specular reflector - smooth surfaces - reflect most of the energy away from the sensor,
resulting in a very low return signal
 Corner reflector - very bright response - returns energy incident upon it
and the surrounding area (buildings, bridges, metal objects) - used for
geometric rectification
o Electrical Characteristics - terrain types conduct electricity form the microwave
energy from the radar sensors better - complex dielectric constant
 Dry surfaces (soil, rock) - constant of 3-8 in the microwave portion of
spectrum
 Water - constant of approx. 80 - most significant parameter affecting the
material’s constant is moisture content - moist soils reflect more radar
than dry soils
 Vegetation Response - Plant canopies can be thought of as seasonally
dynamic 3D water bearing structures - microwave can penetrate canopy
to varying depths
 Surface Scattering - energy interacts with leaves and stems
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Volume Scattering - scattering from leaves, trunks, etc.
Ground Surface scattering - interactions with the ground surface -moisture
content
 Longer the wavelength - the greater the penetration into a canopy
 Urban Response - urban areas are typically light toned in active microwave energy
because of their many corner reflectors
 Cardinal effect - reflections from urban areas, often laid out according to
cardinal directions caused significantly larger returns when features were
illuminated at an angle orthogonal to their orientation
o Radar System Characteristics
o Features (veg, soils, water, ice, urban)
Digital Elevation Models: Interferometric Radar - based on analysis of the
phase of the radar signals as received by two antennas located at different
positions in space
o Output is an interferogram - displays the phase difference values for
each pixel as acquired by the 2 antenna
 Each fringe corresponds to a particular elevation range
Shuttle Radar Topography Mission (SRTM) - joint project - NASA & NIMA
National Imagery and Mapping Agency (NGA)
o Covered 99% of the land area between 60 N and 56 S- used 2
antenna
o Spatial Resolution - 30 m US (public) - 90 m outside
o Available for free from CGIAR
You will be given a hypothetical scenario pertaining to land cover/land use change analysis that exploits
digital remote sensing data and image processing methods. The final exam essay should cover the
following aspects:
1. Assigned objective -- more detailed restatement of problem that is given.
2. Data acquisition -- image and ancillary data; must involve optical image data; may include Thermal
or active microwave (RADAR) image data.
o Identify classes of interest from a classification system
o Acquire appropriate data - imagery, ancillary, ground reference
3. Image processing and analysis -- must involve all of the steps in digital image processing and
probably involve computer-assisted image analysis.
o Preprocess data
 radiometric correction – errors in the digital number values
 random bad pixels, line drops, systematic noise
 Atmospheric effects
 geometric registration – (x, y) coordinates
 Earth rotation, scan skew, velocity, altitude
 transform bands
o Select classification logic/algorithm
 Supervised – select training sites (automated clustering) -> calculate statistics for
training sites (Mean, SD, variance, min/max) -> evaluate separability of sites
(Histograms, Transformed Divergence, J-M) -> apply training sites in decision rule
for image -> evaluate output
 Minimum distance to mean (not widely used if bands close & high variance)
 Parallelepiped (problems with overlap, sensitive to variance)
 Gaussian Max Likelihood –pixel assigned to most likely class
 Bayesian (extension of Gaussian)
Unsupervised – the computer generates spectral similar clusters – clustering ->
labeling -> evaluate output
 ISODATA – not geographically biased, time consuming
Extract training sites
Select bands or transformation of bands to classify
Extract final training sites
Classify image
Assess the accuracy of the classified map
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4. Final products -- Description of product types and characteristics of how each will be
utilized/analyzed.