Grundlagen Fernerkundung - 14

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1 Grundlagen Fernerkundung - 14 Wrap up GEO123.1, FS2015 Michael Schaepman & Hendrik Wulf, 5/22/15 Page 2

2 Department of Geography Fernerkundung!! ist das berührungsfreie Messen und Interpretieren von reflektierter oder emittierter elektromagnetischer Strahlung.! umfasst die gesamte Erdoberfläche einschließlich der Erdatmosphäre.! wird durch satelliten-, flugzeug-, dronen- und handgetragende Sensoren (Kameras und Scanner) ermöglicht.

3 Systeme & Anwendungen Bei der Fernerkundung finden passive oder aktive Systeme Verwendung. Passive Systeme zeichnen die von der Erdoberfläche reflektierte Sonnenstrahlung auf sowie die von der Erdoberfläche emittierte Eigenstrahlung (z.b. Thermalkamera). Im Gegensatz dazu senden aktive Systeme Mikrowellen- oder Laserstrahlen aus und empfangen deren reflektierte Anteile (z.b. Radarsysteme und Laseraltimeter). Fernerkundungsdaten sind insbesondere in den Geowissenschaften/ Geographie von großer Bedeutung, da eine globale Beobachtung der Erdoberfläche/Atmosphäre in hoher räumlicher Auflösung nur mit Hilfe von Fernerkundungssensoren möglich ist. Neben dem synoptischen Überblick über große Räume ermöglichen satellitengestützte Fernerkundungssensoren zudem eine wiederholte (zum Teil tägliche) Abdeckung ein und desselben Gebietes.

4 Strahlungspfade der Fernerkundung

5 Strahlungskomponenten an einem Sensor L s E 0 L atm L gnd " d " u L g,adj L g,dir E dif E gnd!

6 Das elektromagnetische Spektrum < 0.3 µm Ultraviolet / UV 0.4 µm 0.5 µm blue / VIS 0.5 µm 0.6 µm green / VIS 0.6 µm 0.7 µm red / VIS 0.7 µm 1.3 µm near IR / NIR 1.3. µm 3 µm shortwave IR / SWIR 3 µm 14 µm thermal IR / TIR 1 mm 1 m microwave

7 Solar radiation spectrum Solarkonstante W/m 2

8 Räumliche, spektrale und zeitliche Auflösung

9 Spektralbereiche und Systeme Überblick über Fernerkundungssensoren und Systeme System Spektralbereich Photographische Kameras UV NIR Solid State Kameras VIS IR Multispektralscanner UV IR Abbildende Spektrometer VIS IR Radiometer UV MW Radar, SAR MW Lidar VIS NIR Sonar Audiowellen

10 Spektrale Auflösung

11 Die atmosphärische Interaktion 4 Typen der Streuung der Strahlung in der Atmosphäre - Rayleigh Streuung - Raman Streuung - Mie Streuung - unselektive Streuung

12 Definition des räumlichen Referenzsystems

13 Spekulare und diffuse Reflektanz

14 Geometrische Effekte, die eine Veränderung der Reflektanz zur Folge haben

15 Direktionale Effekte 09:58 13:40 17:12

16 Department of Geography Komponenten einer auf ein Laubblatt eintreffenden Strahlung

17 Charakteristische Reflexionskurve eines grünen Blattes

18 Reflexionskurven für Blätter mit unterschiedlichem Wassergehalt

19 Characterizing Land Surface Processes Temporal! Photos: M. Kneubühler ! ! !!!!!!!! ! ! !!!!!!!! ! ! !

20 Characterizing Land Surface Processes Spectral! Daten: M. Kneubühler

21 Imaging Spectroscopy In literature, the terms imaging spectroscopy, imaging spectrometry, hyperspectral, superspectral, and ultraspectral imaging are often used interchangeably. Even though semantic differences might exist, a common definition is: " Imaging spectrometry is the simultaneous acquisition of spatially coregistered images, in many, spectrally contiguous bands, measured in calibrated radiance units, from a remotely operated platform. " Imaging spectroscopy is the simultaneous acquisition of spatially coregistered images, in many, spectrally contiguous bands, measured as reflectance, from a remotely operated platform.

22 Image Cube

23 Data cube Spectral information

24 Data Acquisition a)" Film camera b)" Frame camera c)" Scanner d)" Pushbroom imager e)" Whiskbroom imager f)" Pushbroom imaging spectrometer

25 Begriffe Mikrowellen: Elektromagnetische Wellen - Wellenlängen:1 m - 1 mm - Frequenzen: 300 MHz GHz Radiometer: Instrument zur (passiven) Messung elektromagnetischer Strahlung - Imager: Bildgebender passiver Sensor - Sounder: Instrument zur Messung von Profilen Radar: RAdio Detection And Ranging - aktives System, Detektion und Distanzmessung - SAR: Synthetic Aperture Radar - SLAR: Side-Looking Airborne Radar - Altimeter: Höhenmessung (nicht abbildend) - Scatterometer: Messung der Rückstreuung (nicht abbildend) Sonar: SOund Navigation And Ranging - Aktives System analog zu Radar, arbeitet mit Schallwellen

26 Strahlengang bei einem passiven Mikrowellen- System

27 Ursprung von passiven Mikrowellenstrahlen Emitted from object:!" z.b. Wärme-Abgabe, radioaktive Strahlung, Sonnenenergie Emitted by atmosphere:!" z.b. Polarlicht, chemische Prozesse wie Ozon-Abbau Reflected from Surface:!" Sonnenenergie Transmitted from subsurface:!" Erdstrahlen, radioaktive Strahlung, elektromagnetische Wellen hervorgerufen durch seismische Phänomene

28 Summary: Passive Microwaves Applications Oceans:!" Sea Surface Temperature & Salinity!" Sea Ice!" Surface wind velocities Land:!" Soil moisture!" Snow melt & water equivalent Atmosphere:!" Water vapor

29 Aktive Mikrowellen - Fernerkundung Aktive Sensoren!" Radar!" Altimeter!" Scatterometer!" SLAR / SAR (abbildend) Quelle der Strahlung!" System selber Vorteile!" Unabhängig von Beleuchtung (Sonne) und somit Zeit (Tag/Nacht)!" System-Design (Art der Quellstrahlung) ist definierbar

30 Aktive Mikrowellen - Fernerkundung Systeme mit realer Apertur (Real Aperture Radar RAR) Systeme mit synthetischer Apertur Tracking Radars Altimeter Scatterometer SLAR Side-Looking Airborne Radar Abbildende Verfahren SAR Synthetic Aperture Radar

31 Aktive Mikrowellensysteme: Seitensichtradar

32 Konzept-Vergleich: Synthetischen vs. Reale Apertur Ein Radar mit synthetischer Apertur erlaubt eine sehr gute Auflösung in Flugrichtung (Bewegungsrichtung des Sensors). Dies wird durch synthetisches Zusammenfügen vieler Radarechos erreicht, welche ein Objekt aus verschiedenen Winkeln zeigen.

33 Vergleich SLAR- vs. SAR Gurnigel, BE Gute Auflösungen in beiden Bilddimensionen Bilder RSL / Fraunhofer FHR

34 Effekte der Geländegeometrie auf das SAR- Radarbild III University of California, Santa Barbara

35 Reflexionseigenschaften und Eindringtiefen Volumen-Streuung volume scattering Doppelreflexion double bounce Oberflächen-Streuung Surface scattering

36 Begriffe LiDAR: Light Detection And Ranging - aktives System analog zu RADAR - funktioniert im UV, VIS, IR Bereich - primäre Anwendung: Distanzmessung Laser: Light Amplification by Stimulated Emission of Radiation - hohe Intensität, enger Frequenzbereich, grosse Koheränzlänge ALS: Airborne Laser Scanning - flugzeug oder helikoptergestütztes System - Erstellung von Gelände und Oberflächenmodellen

37 LiDAR & Laser Scanning - Unterschiede LiDAR - nur Distanzmessung topographic LiDAR - Distanzmessung + Position/Ausrichtung zur Bestimmung einer Koordinate, profilierender LiDAR (z.b. GLAS, SLICER) (Airborne) Laser Scanning (ALS) - Distanzmessung + Position/Ausrichtung + Scanner, zur Strahlablenkung quer zur Flugrichtung, Abdeckung eines Schwates d.h. ein LiDAR ist Teil eines ALS, aber nicht das Gleiche!

38 Messprinzip III Range First Echo Full waveform Intensity Last Echo

39 Department of Geography Oberflächen- und Terrain-Modelle Digitales Geländemodell (DGM) Digitales Oberflächenmodell (DOM) 40

40 Anwendungsbeispiele GIS Orthorektifizierung von Luftbildern Infrastruktur und Planung Gefahren- und Risikomanagement Forstwesen... Referenzdaten fuer die SAR-Geokodierung Überall dort wo eine präzise Modellierung der Erdoberfläche benötigt wird

41 Anwendungsbeispiele Messung der Ozonkonzentration in... "... bodennahen Luftschichten "... der Stratosphäre Detektion von Aerosolen Detektion von Wasserdampf, Stickstoff und Schwefelverbindungen Windmessungen (Doppler - LiDAR)

42 Satellite Orbits 5/22/15 Page 43

43 Terms & Definitions: Orbits Inclination: orbit angle with respect to the equator " near polar orbit = close to 90 " equatorial orbit = 0!"#$#%&'!"!"#$!%&'()*"&%!"&"'+%!&,-+.'/"& " #$%&'(')%*(+,"%+-)'!".(/"012333"45" " &6*7&,*.8+%*%6&!"9337:33"45" (')$*&!"%#0"&%)&/)012"%"&)3"&)+(#%&45 ) 6& " ;(*<&('!"=3>"45?"@:/@"5)*A%+-)'?"B9/>"+$C%D6')%*&"E$+"<(," " FGG!"933"45?"@H/:"5)*A%+-)'?"B>/9"+$C%D6')%*&"E$+"<(," +,%-#*)$-"./)*00$12.#$3'!" " #$*$+(DD,"-$'I$$*"B3"(5"(*<"H"E5 5/22/15 Page 44

44 Terms & Definitions: Sensor characteristics Spatial resolution: ground surface area represented by one pixel " Instantaneous field of view (IFOV) " Ground sampling distance (GSD) Temporal resolution: time between two image acquisitions for a given location " Revisit time or repeat cycle (measured in days) Spectral resolution: number of spectral bands and their wavelength intervals " Panchromatic, multispectral, imaging spectroscopy Radiometric resolution: effective bit-depth of the sensor " Gain settings: range of brightness sensitivity " Signal to noise ratio 5/22/15 Page 45

45 Landsat 8 (Landsat Data Continuity Mission) " Launched: February 11, 2013 " Scientific goal: Characterize and monitor land-cover use and change over time " Applications: Carbon Cycle, Earth Surface, Ecosystems, and Biogeochemistry " Sensors: Operational Land Imager Thermal Infrared Sensor " Data: for the general public (no costs)

46 Department of Geography Landsat 7: Spectral bands µm µm µm µm µm µm µm

47 Department of Geography MODIS (Moderate Resolution Imaging Spectroradiometer) " Launched: December 18, 1999 (1st of 2 sat.) " Scientific goal: provide measurements of large-scale global dynamics " Applications: cloud cover, radiation budget and oceanic terrestrial and lower atmospheric processes " Sensor: multispectral radiometer " Data: for the general public (no costs)

48 MODIS products overview Surface temperature (land and ocean) and fire detection; " " " " " " Ocean color, currents; Global vegetation and change maps; Cloud characteristics; Aerosol concentrations and properties; Temperature and moisture soundings; Snow cover and characteristics; 5/22/15 Page 49

49 Department of Geography ESA s Earth Observation Programme 5/22/15 Page 50

50 MERIS! ASAR! GOMOS! AATSR! MEdium Resolution Imaging Spectrometer! Advanced Synthetic Aperture Radar! Global Ozone Monitoring by Occultation of Stars! Advanced Along Track Scanning Radiometer! RA-2! Radar Altimeter 2! SCIAMACHY! MIPAS! MWR! LRR! DORIS! SCanning Imaging Absorption SpectroMeter for Atmospheric CartograpHY! Michelson Interferometer for Passive Atmospheric Sounding! MicroWave Radiometer! Laser RetroReflector! Doppler Orbitography and Radiopositioning Integrated by Satellite!

51 ESA s Earth Explorers Objective: Better understanding of Earth Science in various fields of interest based on innovative satellite technology Satellit GOCE SMOS Ziel der globalen Forschungen Bestimmung des Erdschwerefeldes Bodenfeuchte, Salzgehalt der Meere Start CryoSat Erfassung der planetaren Eismassen SWARM Erdmagnetfeld und Klima ADM-Aeolus Dynamik der Erdatmosphäre 2015 (geplant) EarthCARE Wolken und Aerosole 2016 (geplant) Biomass Biomasse der Wälder 2020 (geplant)

52 Department of Geography VHR satellite imagery - Applications Defense & Security Oil, Gas, Mining Civil Engineering Agriculture Mapping & 3D Forest & Environment Disasters & Crisis Culture & Heritage

53 Very high resolution satellites Satellite Organization Launch date IKONOS GeoEye 1999 Quickbird (3x) DigitalGlobe WorldView-1, 2, 3 DigitalGlobe 2007, 2009, 2014 Cartosat-2 (2x) ISRO GeoEye-1 GeoEye 2008 Pleiades (2x) CNES Skysat 1-2 (15x) Skybox Imaging

54 Copernicus pillars Department of Geography European independence & contribution to global observing system Space Component Global, timely and easily accessible information In-Situ Component 5/22/15 Services Component Page 55

55 The Sentinel Family Sentinel 1 SAR imaging All weather, day/night applications, interferometry 2014 / 2015 Sentinel 2 Multi-spectral imaging Land applications: urban, forest, agriculture,.. Continuity of Landsat, SPOT 2015 / 2016 Sentinel 3 Ocean and global land monitoring Wide-swath ocean color, vegetation, sea/land surface temperature, altimetry 2015 / 2017 Sentinel 4 Geostationary atmospheric Atmospheric composition monitoring, transboundary pollution 2020 Sentinel 5 / 5P Low-orbit atmospheric Atmospheric composition monitoring (S5 Precursor launch in 2015) 2015, 2021

56 Department of Geography Spectral bands: Sentinel-2 vs. Landsat-8

57 Sentinel-1 applications: (1) Land 5/22/15 Page 58

58 Sentinel-1 applications: (2) Sea 5/22/15 Page 59

59 Sentinel-2 applications Agriculture, Forestry and Range!" e.g. discriminating vegetation type & state Land Use/Cover Changes!" e.g. monitoring urban growth Geology!" e.g. mapping geologic landforms Hydrology!" e.g. determining snow and ice cover Coastal Resources!" e.g. tracking shoreline erosion and flooding Environmental Monitoring!" e.g. monitoring deforestation 5/22/15 Page 60

60 Sentinel-3 applications Marine and Coastal Environment!" e.g. Sea-surface topography & temperature, circulation, water quality, wave height Polar Environment!" e.g. Sea-ice thickness Global Change Ocean!" e.g. Sea-level rise, CO 2 flux Land Cover & Land-Use Change!" e.g. Forest monitoring, land-use mapping Risk Management!" e.g. Fire detection, Burnt area mapping 5/22/15 Page 61

61 Lillesand Fig Department of Geography Histogram or Contrast stretching DR sensor (0-255) DR display (0-255) DR sensor (60-158) DR display (0-255)

62 Level slicing " " " Discontinuous color mapping Subdividing the continuous range of values into discontinuous but sequential groups (called bins or classes) Simplest way of making classes based on spectral values 0 10 aquamarine sienna dark green color scale light green to white

63 Band combinations Each spectral band represents a grey-scale image. Three of these bands can be assigned to the display colors red (R), green (G) and blue (B) to obtain a full-color image. 3 bands, each = 24 bit color depth (16.7 million colors) blue green red

64 False-color images using 7 Landsat bands Landsat bands "m Blue "m Green "m Red "m Near IR "m Mid-IR "m Thermal IR "m Mid-IR

65 Maximizing spectral contrast Green, dense For vegetation:! Brown or sparse " Reflectance is highest in NIR and Red Infrared Red Infrared lowest in RED! " The ratio NIR / RED gives therefore the highest contrast and the best way to quantify changes! " This ratio changes under stress!!!

66 Spatial Image Filtering " " " Filtering manipulates the image elements Filters are applied using kernels composed of size and weights Most common in image analysis: high-pass and low-pass filters

67 High-pass filter (sharpening) " " " " Used to enhance high-frequency variations Disadvantage: enhances noise as well Kernel typically has high central value surrounded by (partially) negative weights; sum is 0 or higher Special case: Laplacian (2 nd derivative) filter

68 Low-pass filter (smoothing) " " " " Used to suppress high-frequency variation and noise Kernel with small positive values. Simplest case: all values (1 / kernel size) and thus sum equals 1 Kernel size (3x3, 5x5,!) determines degree of smoothing Special case: Gaussian filter (or Gaussian blur ) 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9 1/9

69 Classification methods Albertz, 2001 Unsupervised classification Supervised classification Clustering, (Segmentation)... Parallelepiped, Minimum distance, Maximum likelihood...

70 Department of Geography Feature space: NIR vs RED

71 Common classification algorithms: overview Parallelepiped Minimum Distanz Maximum Likelihood

72 Type 1 (commission) and type 2 (omission) error Referenz/ ground truth Klassifikation Klassifikation Referenz/ ground truth A B C D " A B C D " Incorrectly classified classification # reference -" Type 1: Commission Error Pixels were included in the class, although they should not have been -" Type 2: Omission Error Pixels were not included in the class, although they should have been

73 Producer s Accuracy (PA) Referenz/ ground truth A B C D " Klassifikation Klassifikation A B C The producer needs to know how well her/his classification (e.g. for class A) matches with the reference Referenz D " PA == Omission Error How many pixels should have been in the class but were not Producer s Accuracy = count of correctly classified pixels in class count of pixels in same reference class = = (~83.3%)

74 User s Accuracy (UA) Referenz/ ground truth A B C D " UA Klassifikation Klassifikation A B C The user needs to know how well a class (e.g. A) matches with the reality Referenz D " == Commission Error How many pixels are in the class but should not have been User s Accuracy = count of correctly classified pixels in class count of all pixels in that class = = 1 (100%)

75 Overall/ Total Accuracy (OA) Referenz/ ground truth A B C D " A Klassifikation Klassifikation B C D Referenz " Overall Accuracy = Count of correctly classified pixels Grand total (total pixel count) = = (~90.6%)

76 Accuracy metrics OA, PA, UA Referenz/ ground truth A B C D " UA [%] A If I classified a pixel as forest (A), there is in 100% of the cases indeed forest at that location Klassifikation Klassifikation B C I have captured 100% of the existing arable land (C) with my classification Referenz D " But: 20% of the classified arable land (C) pixels has another land cover in reality PA [%] OA [%] But: 16.67% of the existing forest (A) was not captured

77 Department of Geography Radialdeformation Image taken on Sept. 23, 2001 from an altitude of 3,300 feet using a Leica/LH systems RC30 camera

78 Zentralperspektive und orthogonale Kartenprojektion Quelle: Bundesamt für Landestopographie swisstopo und Grundlagen Luftbildmessung 2

79 Stereoskopische Parallaxe und Höhenmessung b g Flugbasis h g Flughöhe über Grund H 1, H 2 Bildhauptpunkt R Kopfpunkt Objekt S Fusspunkt Objekt ps=ps 1 +ps 2 Absolute Parallaxe für S #p 1, #p 2 Parallaxdifferenz Objekt, LB1, 2 #p=#p 1 +#p 2 Parallaxdifferenz im Luftbildpaar b p Photobasis f Brennweite #h Objekthöhe R 1, R 2 Kopfpunkt im Luftbild 1, 2 S 1, S 2 Fusspunkt im Luftbild 1, 2 pr 1, pr 2 Absolute Parallaxe für R #P Parallaxdifferenz in der Bezugsebene S (Gelände) Parallaxe für R: pr = pr 1 -pr 2 Parallaxe für S: ps = ps 1 -ps 2 Parallaxedifferenz #p: pr-ps #h=(#p*h g )/pr