chapter 1 concepts and foundations of remote sensing introduction to remote sensing instructor: dr....
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Chapter 1
Concepts and foundations of Remote Sensing
Introduction to Remote SensingInstructor: 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 processword eyes brain meaningdata sensor processing information
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1.1 Introduction (cont.) Collected data can be of many forms:• variations in force distribution e.g. gra
vity meter• acoustic wave distribution e.g. sonar• electromagnetic energy distribution e.g.
eyes• our focus: electromagnetic energy distrib
ution
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1.1 Introduction (cont.) Fig. 1.1 Generalized processes and elem
ents 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 =
c : speed of light (3x108 m/s) : frequency (cycle per second, Hz) : wavelength (m)
• unit: micrometer m = 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 eyesblue: 0.4~0.5mgreen: 0.5~0.6mred: 0.6~0.7m
IR (infrared)near-IR: 0.7~1.3 mmid-IP: 1.3~3.0 m thermal-IR: 3.0 m~1mm heat sensation
microwave: 1mm~1m
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1.2 Energy sources and radiation principles (cont.)
Fig. 1.3 (cont.) • Particle theory: Q = h
Q: quantum energy (Joule)h: Planck's constant (6.626x10-34 J sec): frequency
• Q = h = hc/ 1/implication in remote sensing:Q viewing
areaenough area
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1.2 Energy sources and radiation principles (cont.)
Stefan-Boltzmann law:• M = T4
M: total radiant exitance from the surface of a material (watts m-2)
: 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 al
l 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:m=A/T 1/T
m : 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: T6000K m0.5m (visible light)
• incandescent lamp: T 3000K m 1m"outdoor" file used indoors "yellowish“
need high blue energy flash compensate
• Earth: T 300K m 9.7m thermal energy radiometer<3m: reflected energy predominates>3m: 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 << Rayleigh scatter
Rayleigh scatter effect 1/4
"blue sky" and "golden sunset" Rayleigh "haze" imagery filter (Chapter 2)
• wavelength scale: d Mie scatter influence longer wavelength dominated in slightly overcast sky
• large scale: d >> e.g. water drop nonselective scatter f() 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 availabilit
y
• 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() = ER() + EA() + ET()
incident = reflected + absorbed + transmitted
• ER = ER(feature, ) distinguish features R.S.in visible portion: ER() 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: I
• if I << surface height variations diffuse for R.S. measure diffuse reflectance
• spectral reflectance
)(
)(
I
R
E
E
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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 sensornear-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 (0.45m: blue; o.67m: red) green
if yellow leaves (red) green + red from 0.7 m to 1.3 m minimum absorption (< 5%) strong
reflectance = f(internal structure of leaves) discriminate species and detect vegetation stress
> 1.3 m three water absorption bands (1.4, 1.9 and 2.7 mm) water content () () = 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 (lwab) soil texture: coarse drain moisture surface roughness iron oxide, organic matter 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 (near-IR) visible: very complex and interrelated
surface bottom material in the water
clear water ® bluechlorophyll ® greenCDOM ® 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, uniquereflectance, 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 r
ely on these variabilities
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1.4 Energy interactions with earth surface features (cont.)
Spectral Response Pattern (cont.)• minimize unwanted spectral variability
maximize 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:
: reflectanceE: incident irradianceT: atmospheric transmissionLp: path radiance
• E = Edir + Edif
• E = E(t)
ptot LET
L
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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 variationbroader 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 range:8-bit: 0~255, 10-bit: 0~1023 easier for automatic processing, but limited in spectral pattern interpretat
ion
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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 consumingtime-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 spectroscopy
in-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 interactio
n 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 missiondeal with "relative energy"
atmosphere• effects = fn(, t, X)• importance of these effects = fn(, 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-offsphotographic 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 > 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 ma
ny 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 categoriesrefined stage of images aerial extent and position
delineate stressed sub-areasfield-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 manage
ment
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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 watershedvarious 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 mapred 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 long photographic system Chapter 2, 3, 4 non-photographic system Chapter 5,
6, 7, 8