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
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
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
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)
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
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: 0.4~0.5mm
 green: 0.5~0.6mm
 red: 0.6~0.7mm
IR (infrared)
 near-IR: 0.7~1.3 mm
 mid-IP: 1.3~3.0 mm
 thermal-IR: 3.0 mm~1mm  heat sensation
microwave: 1mm~1m
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 sensing:lQ  viewing
areaenough area
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
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:
lm=A/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
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  "yellowish“
need high blue energy flash  compensate
• Earth: T  300K  lm 9.7mm  thermal
energy  radiometer
l<3mm: reflected energy predominates
l>3mm: emitted energy prevails
• Passive Active
1.3 Energy interaction in the
atmosphere
 Path length
• space photography: 2 atmospheric
thickness
• airborne thermal sensor: very thin path
length
• sensor-by sensor
1.3 Energy interaction in the
atmosphere (cont.)
 Scattering
• molecular scale: d << 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 >> l
 e.g. water drop
 nonselective scatter  f(l)
 that's why fog and clod appear white
 why dark clouds black?
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
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
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!
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 << surface height variations  diffuse
for R.S.  measure diffuse reflectance
• spectral reflectance
E R (l )
l 
E I (l )
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
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  (red)   green + red
from 0.7 mm to 1.3 mm  minimum absorption (< 5%)  strong
reflectance = f(internal structure of leaves)  discriminate species and
detect vegetation stress
l > 1.3 mm  three water absorption bands (1.4, 1.9 and 2.7 mm)
 water content  (l) 
 (l) = f(water content, leaf thickness)
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
1.4 Energy interactions with earth
surface features (cont.)
 Fig 1.10 (cont.)
• water
near-IR: water (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.
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
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
plant
temporal effect: e.g. growth of plant 
change detection
1.4 Energy interactions with earth
surface features (cont.)
 Atmospheric influences on spectral
response patterns
• sensor-by-sensor
• mathematical expression:
: reflectance
E: incident irradiance
T: atmospheric transmission
Lp: path radiance
• E = Edir + Edif
• E = E(t)
ET
Ltot 
 Lp

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
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
interpretation
1.6 Reference data
 R.S. needs some form of reference data
 Purposes:
• Analysis and interpretation
• calibration
• verification
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
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
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!!!
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
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
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, ...
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
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
1.9 Successful application of
remote sensing
 Premise: integration
• many inventorying and monitoring
problems are not amenable to solution by
means of R.S.
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
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
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? ...
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
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
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
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
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
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
1.10 Land and geographic
information systems (cont.)
 GIS analysis  overlay analysis
• aggregation
• buffering
• network analysis
• intervisibility
• perspective views
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
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
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!
1.11 Organization
 simple  complex
 short l  long l
 photographic system  Chapter 2, 3, 4
 non-photographic system  Chapter 5,
6, 7, 8