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Chapter 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. 1.1 Introduction. General definition of Remote Sensing:

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Chapter 1

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  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: 0.4~0.5mm • 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

  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 sensing:l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: • 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

  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 • l<3mm: reflected energy predominates • l>3mm: 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 << 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?

  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 << 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 (< 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 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 variabilitymaximize variability when required! • spatial effect: e.g. different species of planttemporal 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 range:8-bit: 0~255, 10-bit: 0~1023 • 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 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

  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 > 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

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