Presentation on theme: "Dr. Mathias (Mat) Disney UCL Geography Office: 113, Pearson Building"— Presentation transcript:
1GEOGG141/ GEOG3051 Principles & Practice of Remote Sensing (PPRS) 1: Introduction to Remote Sensing Dr. Mathias (Mat) DisneyUCL GeographyOffice: 113, Pearson BuildingTel:
2Format Component 1 (GEOGG141 only) Component 2 (GEOGG141 & GEOG3051) Mapping principles (Dowman, Iliffe, Haklay, Backes, Smith, Cross)Understanding the geometry of data acquisitionOrbits, geoids and principles of geodesyComponent 2 (GEOGG141 & GEOG3051)Radiometric principles (Disney)Understanding the what we measure and howRadiative transfer (GEOGG141 only – Reading Week)Resolution, sampling and practical tradeoffsPre-processing and ground segmentActive remote sensing (LIDAR, RADAR…)
3Miscellaneous Remote Sensing at UCL NERC National Centre for Earth Observation (NCEO)Involvement in several themes at UCLEarth Sciences: (Wingham, Laxman et al.)Carbon Geography (Lewis, Mat Disney et al.)Solid Earth: GE (Ziebart)More generallyMSSL: e.g. imaging (Muller), planetary, astro, instrumentsUK prof. body - Remote Sensing and Photogrammetry Society
4Reading and browsing Remote sensing Campbell, J. B. (2006) Introduction to Remote Sensing (4th ed), London:Taylor and Francis.Harris, R. (1987) "Satellite Remote Sensing, An Introduction", Routledge & Kegan Paul.Jensen, J. R. (2006, 2nd ed) Remote Sensing of the Environment: An Earth Resource Perspective, Prentice Hall, New Jersey. (Excellent on RS but no image processing).Jensen, J. R. (2005, 3rd ed.) Introductory Digital Image Processing, Prentice Hall, New Jersey. (Companion to above) BUT some available online atJones, H. and Vaughan, R. (2010, paperback) Remote Sensing of Vegetation: Principles, Techniques, and Applications, OUP, Oxford. Excellent.Lillesand, T. M., Kiefer, R. W. and Chipman, J. W. (2004, 5th ed.) Remote Sensing and Image Interpretation, John Wiley, New York.Mather, P. M. (2004) Computer Processing of Remotely‑sensed Images, 3rdEdition. John Wiley and Sons, Chichester.Rees, W. G. (2001, 2nd ed.). Physical Principles of Remote Sensing, Cambridge Univ. Press.Warner, T. A., Nellis, M. D. and Foody, G. M. eds. (2009) The SAGE Handbook of Remote Sensing (Hardcover). Limited depth, but very wide-ranging – excellent reference book.GeneralMonteith, J. L. and Unsworth, M. H. (1990) ”Principles of Environmental Physics”, 2nd ed. Edward Arnold, London.Hilborn, R. and Mangel, M. (1997) “The Ecological Detective: Confronting models with data”, Monographs in population biology 28, Princeton University Press, New Jersey, USA.
5Browsing Moodle & www.geog.ucl.ac.uk/~mdisney/pprs.html Web Tutorials Glossary :Other resourcesNASANASAs Visible Earth (source of data):European Space Agency earth.esa.int (eg Image of the week….)NOAAIKONOS:QuickBird:
6TodayGeneral introduction to remote sensing (RS), Earth Observation (EO)definitions of RSConcepts and termsremote sensing process, end-to-endRadiation I
7What is remote sensing? The Experts say "Remote Sensing (RS) is...” “The science technology and art of obtaining information about objects or phenomena from a distance (i.e. without being in physical contact with them”But not the whole story:Tend to use Earth Observation (EO). To distinguish from?Domains (atmosphere, terrestrial, ocean, cryosphere, biosphere etc)But also astronomy, planetary remote sensing etc.
8What is remote sensing (II)? The not so experts say "Remote Sensing is...”Advanced colouring-in.Seeing what can't be seen, then convincing someone that you're right.Being as far away from your object of study as possible and getting the computer to handle the numbers.Legitimised voyeurism(more of the same from
9Remote Sensing Examples Kites (still used!) Panorama of San Francisco, 1906.Up to 9 large kites used to carry camera weighing 23kg.
11Remote Sensing: scales and platforms Both taken via kite aerial photography
12Remote Sensing: scales and platforms upscaleupscalePlatform depends on applicationWhat information do we want?How much detail?What type of detail?
13Remote Sensing: scales and platforms upscaleMany types of satelliteDifferent orbits, instruments, applications
14Remote Sensing Examples IKONOS-2 image of VeniceRemote Sensing ExamplesGlobal maps of vegetation from MODIS instrument
15Remote sensing applications Environmental: climate, ecosystem, hazard mapping and monitoring, vegetation, carbon cycle, oceans, iceCommercial: telecomms, agriculture, geology and petroleum, mappingMilitary: reconnaissance, mapping, navigation (GPS)Weather monitoring and predictionMany, many more
16EO process in summary..... Collection of data Some type of remotely measured signalElectromagnetic radiation of some formTransformation of signal into something usefulInformation extractionUse of information to answer a question or confirm/contradict a hypothesis
17The Remote Sensing Process: II Collection of information about an object without coming into physical contact with that objectPassive: solar reflected/emittedActive:RADAR (backscattered); LiDAR (reflected)
18The Remote Sensing Process: III What are we collecting?Electromagnetic radiation (EMR)What is the source?Solar radiationpassive – reflected (vis/NIR), emitted (thermal)OR artificial sourceactive - RADAR, LiDAR
19Electromagnetic radiation? Electric field (E)Magnetic field (M)Perpendicular and travel at velocity, c (3x108 ms-1)
20Energy radiated from sun (or active sensor) Energy 1/wavelength (1/)shorter (higher f) == higher energylonger (lower f) == lower energyfrom
21Information What type of information are we trying to get at? What information is available from RS?Spatial, spectral, temporal, angular, polarization, etc.
22Spectral information: vegetation Wavelength, nm40060080010001200reflectance(%)0.00.10.20.30.40.5very high leaf areavery low leaf areasunlit soilNIR, high reflectanceVisible green, higher than redVisible red, low reflectance
24Colour Composites: spectral ‘Real Colour’ compositeGreen band on greenRed band on redBlue band on blueApproximates “real” colour (RGB colour composite)Landsat TM image of Swanley, 1988
25Colour Composites: spectral ‘False Colour’ composite (FCC)NIR band on redred band on greengreen band on blue
26Colour Composites: spectral ‘False Colour’ compositeNIR band on redred band on greengreen band on blue
27Colour Composites: temporal ‘False Colour’ compositemany channel data, much not comparable to RGB (visible)e.g. Multi-temporal databut display as spectralAVHRR MVC 1995AprilAugustSeptember
28Temporal information Change detection Rondonia 1975 Rondonia 1986
29Colour Composites: angular ‘False Colour’ compositemany channel data, much not comparable to RGB (visible)e.g. MISR -Multi-angular data (August 2000)0o; +45o; -45oReal colour composite (RCC)Northeast Botswana
30Always bear in mind.....when we view an RS image, we see a 'picture’ BUT need to be aware of the 'image formation process' to:understand and use the information content of the image and factors operating on itspatially reference the data
31Why do we use remote sensing? Many monitoring issues global or regionalDrawbacks of in situ measurement …..Remote sensing can provide (not always!)Global coverageRange of spatial resolutionsTemporal coverage (repeat viewing)Spectral information (wavelength)Angular information (different view angles)
32Why do we study/use remote sensing? source of spatial and temporal information (land surface, oceans, atmosphere, ice)monitor and develop understanding of environment (measurement and modelling)information can be accurate, timely, consistentremote accesssome historical data (1960s/70s+)move to quantitative RS e.g. data for climatesome commercial applications (growing?) e.g. weathertypically (geo)'physical' information but information widely used (surrogate - tsetse fly mapping)derive data (raster) for input to GIS (land cover, temperature etc.)
33Caveats! Remote sensing has many problems Can be expensive Technically difficultNOT directmeasure surrogate variablese.g. reflectance (%), brightness temperature (Wm-2 oK), backscatter (dB)RELATE to other, more direct properties.
34Colour Composites: polarisation ‘False Colour’ compositemany channel data, much not comparable to RGB (visible)e.g. Multi-polarisation SARHH: Horizontal transmitted polarization and Horizontal received polarizationVV: Vertical transmitted polarization and Vertical received polarizationHV: Horizontal transmitted polarization and Vertical received polarization
35What sort of parameters are of interest? Back to the process....What sort of parameters are of interest?Variables describing Earth system....
36Information extraction process Analogue image processingMulti:spectral, spatial, temporal, angular, scale, disciplinaryVisualisationAncillary info.: field and lab measurements, literature etc.Image interpretationTone, colour, stereo parallaxSize, shape, texture, pattern, fractal dimensionHeight/shadowSite, associationPrimary elementsSpatial arrangementsSecondary elementsContextPresentation of informationMulti:spectral, spatial, temporal, angular, scale, disciplinaryStatistical/rule-based patternsHyperspectralModelling and simulationAfter Jensen, p. 22
37Example: Vegetation canopy modelling Develop detailed 3D modelsSimulate canopy scattering behaviourCompare with observations
38Output: above/below canopy signal Light environment below a deciduous (birch) canopy38
39LIDAR signal: single birch tree Higher densityAllows interpretation of signal, development of new methods39
40EO and the Earth “System” AtmosphereEO and the Earth “System”External forcingCryosphereGeosphereBiosphereHydrosphereFrom Ruddiman, W. F., Earth's Climate: past and future.
41Example biophysical variables After Jensen, p. 9
42Example biophysical variables Good discussion of spectral information extraction:After Jensen, p. 9