Concepts and foundations of Remote Sensing - PowerPoint PPT Presentation

Loading...

PPT – Concepts and foundations of Remote Sensing PowerPoint presentation | free to download - id: 3d3ddf-Njg5Z



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

Concepts and foundations of Remote Sensing

Description:

Chapter 1 Concepts and foundations of Remote Sensing Introduction to Remote Sensing Instructor: Dr. Cheng-Chien Liu Department of Earth Science National Cheng-Kung ... – PowerPoint PPT presentation

Number of Views:445
Avg rating:5.0/5.0
Slides: 50
Provided by: tangtang8
Learn more at: http://tangtang88.sg1010.myweb.hinet.net
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: Concepts and foundations of Remote Sensing


1
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

2
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

3
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

4
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)

5
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

6
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

7
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?????

8
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

9
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

10
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
  • llt3mm reflected energy predominates
  • lgt3mm emitted energy prevails
  • Passive ?Active

11
1.3 Energy interaction in the atmosphere
  • Path length
  • space photography 2 atmospheric thickness
  • airborne thermal sensor very thin path length
  • sensor-by sensor

12
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?

13
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

14
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

15
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!

16
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

17
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

18
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)

19
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

20
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.

21
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

22
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

23
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)

24
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

25
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

26
1.6 Reference data
  • R.S. needs some form of reference data
  • Purposes
  • Analysis and interpretation
  • calibration
  • verification

27
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

28
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

29
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!!!

30
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

31
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

32
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, ...

33
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

34
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?

35
1.9 Successful application of remote sensing
  • Premise integration
  • many inventorying and monitoring problems are not
    amenable to solution by means of R.S.

36
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

37
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

38
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? ...

39
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

40
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

41
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

42
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

43
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

44
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

45
1.10 Land and geographic information systems
(cont.)
  • GIS analysis ? overlay analysis
  • aggregation
  • buffering
  • network analysis
  • intervisibility
  • perspective views

46
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

47
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

48
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!

49
1.11 Organization
  • simple ? complex
  • short l ? long l
  • photographic system ? Chapter 2, 3, 4
  • non-photographic system ? Chapter 5, 6, 7, 8
About PowerShow.com