Concepts and foundations of Remote Sensing

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Chapter 1

• Concepts and foundations of Remote Sensing
• Introduction to Remote Sensing
• Instructor Dr. Cheng-Chien Liu
• Department of Earth Science
• National Cheng-Kung University

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1.1 Introduction

• General definition of Remote Sensing
• 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.
• e.g. reading process
• word ? eyes ? brain ? meaning
• data ? sensor ? processing ? information

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1.1 Introduction (cont.)

• Collected data can be of many forms
• variations in force distribution ? e.g. gravity
  meter
• acoustic wave distribution ? e.g. sonar
• electromagnetic energy distribution ? e.g. eyes
• our focus electromagnetic energy distribution

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1.1 Introduction (cont.)

• Fig. 1.1 Generalized processes and elements
  involved in electromagnetic remote sensing of
  earth resources.
• data acquisition a-f (1.2 - 1.5)
• data analysis g-i (1.6 - 1.10)

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1.2 Energy sources and radiation principles

• Fig. 1.3 electromagnetic spectrum ? memorize
• Wave theory c nl
• c speed of light (3x108 m/s)
• n frequency (cycle per second, Hz)
• l wavelength (m)
• unit micrometer mm 10-6 m

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1.2 Energy sources and radiation principles
(cont.)

• Fig. 1.3 (cont.)
• Spectrum
• UV (ultraviolet)
• Vis (visible)
• narrow range, strongest, most sensitive to human
  eyes
• blue 0.40.5mm
• green 0.50.6mm
• red 0.60.7mm
• IR (infrared)
• near-IR 0.71.3 mm
• mid-IP 1.33.0 mm
• thermal-IR 3.0 mm1mm ? heat sensation
• microwave 1mm1m

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1.2 Energy sources and radiation principles
(cont.)

• Fig. 1.3 (cont.)
• Particle theory Q hn
• Q quantum energy (Joule)
• h Planck's constant (6.626x10-34 J sec)
• n frequency
• Q hn hc/l ? 1/l
• implication in remote sensingl????Q???? ?
  viewing area???enough area?????

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1.2 Energy sources and radiation principles
(cont.)

• Stefan-Boltzmann law
• M sT4
• M total radiant exitance from the surface of a
  material (watts m-2)
• s Stefan-Boltzmann constant (5.6697x10-8 W
  m-2K-4)
• T absolute temperature (K) of the emitting
  material
• blackbody
• a hypothetical, ideal radiator totally absorbs
  and reemits all incident energy

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1.2 Energy sources and radiation principles
(cont.)

• Fig 1.4 Spectral distribution of energy radiated
  from blackbodies of various temperatures
• Area ? total radiant exitance M
• T?? M? (graphical illustration of S-B law)
• Wien's displacement law
• lmA/T ? 1/T
• lm dominant wavelength, wavelength of maximum
  spectral radiant (mm)
• A 2898 (K)
• T absolute temperature (K) of the emitting
  material
• e.g. heating iron dull red ? orange ? yellow ?
  white

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1.2 Energy sources and radiation principles
(cont.)

• Fig 1.4 (cont.)
• Sun T?6000K ? lm?0.5mm (visible light)
• incandescent lamp T ? 3000K ? lm ? 1mm
• "outdoor" file used indoors? ? "yellowishneed
  high blue energy flash ? compensate??????
• Earth T ? 300K ? lm ?9.7mm ? thermal energy ?
  radiometer
• llt3mm reflected energy predominates
• lgt3mm emitted energy prevails
• Passive ?Active

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1.3 Energy interaction in the atmosphere

• Path length
• space photography 2 atmospheric thickness
• airborne thermal sensor very thin path length
• sensor-by sensor

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1.3 Energy interaction in the atmosphere (cont.)

• Scattering
• molecular scale d ltlt l ? Rayleigh scatter
• Rayleigh scatter effect ? 1/l4
• "blue sky" and "golden sunset"
• Rayleigh ? "haze" imagery ? filter (Chapter 2)
• wavelength scale d ? l ? Mie scatter
• influence longer wavelength
• dominated in slightly overcast sky
• large scale d gtgt l
• e.g. water drop
• nonselective scatter ? f(l)
• that's why fog and clod appear white
• why dark clouds black?

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1.3 Energy interaction in the atmosphere (cont.)

• absorption
• absorbers in the atmosphere water vapor, carbon
  dioxide, ozone
• Fig 1.5 Spectral characteristics of (a) energy
  sources (b) atmospheric effect (c) sensing
  systems
• atmospheric windows

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1.3 Energy interaction in the atmosphere (cont.)

• important considerations
• sensor spectral sensitivity and availability
• windows in the spectral range ? sense
• source magnitude, spectral composition

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1.4 Energy interactions with earth surface
features

• Fig 1.6 basic interactions between incident
  electromagnetic energy and an earth surface
  feature
• EI(l) ER(l) EA(l) ET(l)
• incident reflected absorbed transmitted
• ER ER(feature, l) ? distinguish features ?
  R.S.
• in visible portion ER(l) ? color
• most R.S. ? reflected energy predominated ? ER
  important!

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1.4 Energy interactions with earth surface
features (cont.)

• Fig. 1.7 Specular versus diffuse reflectance
• specular ? diffuse (Lambertian)
• surface roughness ? incident wavelength lI
• if lI ltlt surface height variations ? diffuse
• for R.S. ? measure diffuse reflectance
• spectral reflectance

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1.4 Energy interactions with earth surface
features (cont.)

• Fig 1.8 Spectral reflectance curve (SRC)
• object type ? ribbon (envelope) rather than a
  single line
• characteristics of SRC ? choose wavelength
• characteristics of SRC ? choose sensor
• near-IR photograph does a good job (Fig 1.9)
• Many R.S. data analysis ? mapping ? spectrally
  separable ? understand the spectral
  characteristics

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1.4 Energy interactions with earth surface
features (cont.)

• Fig 1.10 Typical SRC for vegetation, soil and
  water
• average curves
• vegetation
• pigment ? chlorophyll ? two valleys (0.45mm
  blue o.67mm red) ? green
• if yellow leaves ? r(red) ? ? green red
• from 0.7 mm to 1.3 mm ? minimum absorption (lt 5)
  ? strong reflectance f(internal structure of
  leaves) ? discriminate species and detect
  vegetation stress
• l gt 1.3 mm ? three water absorption bands (1.4,
  1.9 and 2.7 mm)
• water content ?? r(l) ?
• r(l) f(water content, leaf thickness)

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1.4 Energy interactions with earth surface
features (cont.)

• Fig 1.10 (cont.)
• soil
• moisture content ?? r(lwab) ?
• soil texture coarse ?? drain ?? moisture ?
• surface roughness ?? r ?
• iron oxide, organic matter ?? r ?
• These are complex and interrelated variables

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1.4 Energy interactions with earth surface
features (cont.)

• Fig 1.10 (cont.)
• water
• near-IR water ??r(lnear-IR) ?
• visible very complex and interrelated
• surface
• bottom
• material in the water
• clear water blue
• chlorophyll green
• CDOM yellow
• pH, O2, salinity, ... ? (indirect) R.S.

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1.4 Energy interactions with earth surface
features (cont.)

• Spectral Response Pattern
• spectrally separable ? recognize feature
• spectral signatures ? absolute, unique
• reflectance, emittance, radiation measurements,
  ...
• response patterns ? quantitative, distinctive
• variability exists!
• identify feature types spectrally ? variability
  causes problems
• identify the condition of various objects of the
  same type ? we have to rely on these variabilities

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1.4 Energy interactions with earth surface
features (cont.)

• Spectral Response Pattern (cont.)
• minimize unwanted spectral variabilitymaximize
  variability when required!
• spatial effect e.g. different species of
  planttemporal effect e.g. growth of plant ?
  change detection

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1.4 Energy interactions with earth surface
features (cont.)

• Atmospheric influences on spectral response
  patterns
• sensor-by-sensor
• mathematical expression
• r reflectance
• E incident irradiance
• T atmospheric transmission
• Lp path radiance
• E Edir Edif
• E E(t)

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1.5 Data acquisition and interpretation

• detection
• photograph ? chemical reaction
• simple and inexpensive
• high spatial resolution and geometric integrity
• detect and record
• electronic ? energy variation
• broader spectral range of sensitivity
• improved calibration potential
• electronically transmit data
• record on other media (e.g. magnetic tape)
• photograph ? image

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1.5 Data acquisition and interpretation (cont.)

• data interpretation
• pictorial (image) analysis
• human mind ? visual interpretation ? judgment
• disadvantages
• extensive training
• limitation of human eyes not fully evaluate
  spectral characteristics
• digital data analysis
• digital image ? 2-D array of pixels
• digital number (DN)
• A-D signal conversion
• Fig 1.13 input voltage (V), sampling interval
  (DT), output integer
• DN range8-bit 0255, 10-bit 01023
• easier for automatic processing, but limited in
  spectral pattern interpretation

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1.6 Reference data

• R.S. needs some form of reference data
• Purposes
• Analysis and interpretation
• calibration
• verification

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1.6 Reference data (cont.)

• Collecting reference data
• should be according to principles of statistical
  sampling design
• expensive and time consuming
• time-critical
• time-stable

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1.6 Reference data (cont.)

• Collecting reference data (cont.)
• ground-based measurement
• principle of spectroscipy
• spectroradiometer ? spectral reflectance curves
  (continuous)
• laboratory spectroscopyin-situ field measurement
  ? preferred!
• four modes of operation hand held, telescoping
  boom, helicopter, aircraft
• multiband radiometer (discrete)
• three-step process
• calibration ? known, stable reflectance
  measurement ? reflected radiation computation ?
  reflectance factor
• Lambertian surface
• bidirectional reflectance factor

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1.7 An ideal remote sensing system

• A uniform energy source
• A non-interfering atmosphere
• A series of unique energy/matter interaction at
  the earth's surface
• A super sensor
• A real-time data-handling system
• Multiple data users
• This kind of system doesn't exist!!!

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1.8 Characteristics of real remote sensing system

• energy source
• active R.S. ? controlled source
• passive R.S. ? solar energy
• Both are not uniform and are fn(t, X)
• need calibration mission by mission
• deal with "relative energy"
• atmosphere
• effects fn(l, t, X)
• importance of these effects fn(l, sensor,
  application)
• elimination/compensation ? calibration

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1.8 Characteristics of real remote sensing system
(cont.)

• The energy/matter interaction at the earth's
  surface
• reflected/emitted energy ? spectral response
  pattern ? not unique! ? full of ambiguity ?
  difficult to differentiate
• our understanding ? elementary level for some
  materials ? non-exist for others

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1.8 Characteristics of real remote sensing system
(cont.)

• Sensor
• no super sensor
• limitation of spectral sensitivity
• limitation of spatial resolution
• Fig 1.17 (a) crop (b) crop soil (c) two fields
  
• digital image ? pure pixel mixed pixel
• trade-offs
• photographic system spatial resolution ??
  spectral sensitivity ?
• non-photographic system spatial resolution ??
  spectral sensitivity ?
• platform, power, storage, ...

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1.8 Characteristics of real remote sensing system
(cont.)

• Data-handling system
• sensor capability gt data-handling capability
• data processing ? an effort entailing
  considerable thought, instrumentation, time,
  experience, reference data
• computer human

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1.8 Characteristics of real remote sensing system
(cont.)

• Multiple data users
• data ? information
• understand (a) acquisition (b) interpretation (c)
  use
• satisfy the needs of all data users impossible!
• R.S. ? New and unconventional ? not many users
• but as time ?? potential ?? limitation ?? users?

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1.9 Successful application of remote sensing

• Premise integration
• many inventorying and monitoring problems are not
  amenable to solution by means of R.S.

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1.9 Successful application of remote sensing
(cont.)

• Five conceptions of successful designs of R.S.
• Clear definition of problem
• Evaluation of the potential for addressing the
  problem with R.S.
• Identify the data acquisition procedures
• Determine the data interpretation procedures and
  the reference data
• Identify the criteria for judging the quality of
  information

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1.9 Successful application of remote sensing
(cont.)

• Improvement of the success for many applications
  of R.S. ? multiple-view for data collection ?
  more information
• multistage (Fig 1.18)
• multispectral (multi sensors)
• multitemporal

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1.9 Successful application of remote sensing
(cont.)

• Example detection, identification and analysis
  of forest disease and insect problems
  (multistage)
• space images ? overall view of vegetation
  categories
• refined stage of images ? aerial extent and
  position ? delineate stressed sub-areas
• field-checked and documentation
• extrapolate to other area
• detailed ground observation ? evaluate the
  question of what the problem is.
• R.S. ? where? how much? how severe? ...

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1.9 Successful application of remote sensing
(cont.)

• Likewise, multispectral imagery ? more
  information
• The multispectral approach forms the heart of
  numerous R.S. applications involving
  discrimination of earth resource types and
  conditions

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1.9 Successful application of remote sensing
(cont.)

• Multitemporal sensing ? monitor land use change
• Summary
• R.S. ? eyes of GIS (see 1.10)
• R.S. ? transcend the cultural boundaries
• R.S. ? transcend the disciplinary boundaries
  (nobody owns the field of "R.S.")
• R.S. ? important in natural resources management

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1.10 Land and geographic information systems
(LIS, GIS)

• Definition
• GIS A system of hardware, software, data,
  people, organizations, and institutional
  arrangements for collecting, storing, analyzing,
  and disseminating information about areas of
  earth
• LIS A GIS having, as its main focus, data
  concerning land records

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1.10 Land and geographic information systems
(cont.)

• Definition (cont.)
• Other definitions
• GIS large area, regional, national or global
• LIS small area, local, detailed data

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1.10 Land and geographic information systems
(cont.)

• GIS
• GIS ? computer-based systems
• GIS ? information of features
• GIS ? geographical location
• data type
• locational data
• attribute data

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1.10 Land and geographic information systems
(cont.)

• GIS (cont.)
• One benefit of GIS
• spatially interrelate multiple types of
  information stemming from a range of sources
• Fig 1.19 example of studying soil erosion in a
  watershed
• various sources of maps
• land data files (slope, erodibility, runoff)
• derived data
• analysis output ? high soil erosion potential

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1.10 Land and geographic information systems
(cont.)

• GIS analysis ? overlay analysis
• aggregation
• buffering
• network analysis
• intervisibility
• perspective views

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1.10 Land and geographic information systems
(cont.)

• GIS ? 2 primary approaches
• raster (grid cell)
• pros
• simplicity of data structure
• computational efficiency
• efficiency for presenting
• high spatial variability
• blurred boundaries
• cons
• data volume
• limitation of spatial resolution ? grid size
• topological relationship among spatial features ?
  difficult
• high spatial variability
• blurred boundaries
• vector (polygon)
• pros and cons refer to raster

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1.10 Land and geographic information systems
(cont.)

• Digital R.S. imagery ? raster format ? easier for
  raster-based GIS ? output raster format
• Plate 1
• (a) land cover classification by TM data
• (b) soil erodibility data
• (c) slope information
• (d) soil erosion potential map
• red row crops growing on erodible soils on steep
  slopes the highest potential

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1.10 Land and geographic information systems
(cont.)

• Two wrong conclusions
• must be raster format ? wrong!
• GIS ? conversion between raster and vector
• GIS ? integration of raster and vector data
• must be digital format ? wrong!
• visual interpretation of R.S. imagery ? locate
  features ? GIS
• GIS information ? classification R.S. imagery
• ?two-way interaction between R.S. imagery and
  GIS
• R.S. GIS ? boundary becomes blurred!

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1.11 Organization

• simple ? complex
• short l ? long l
• photographic system ? Chapter 2, 3, 4
• non-photographic system ? Chapter 5, 6, 7, 8