Introduction to Remote Sensing

Transcription

1 Introduction to Remote Sensing Dr. Mathias (Mat) Disney UCL Geography Office: 301, 3rd Floor, Chandler House Tel:

2 Course outline Format of the course Lectures for first part of session, practicals second part Course mark 70% for exam, 30% for write-up of final practical, to be handed in by the end of term (Weds Mar 23 rd?) First week familiarise ourselves with lab PCs and sources of image data on the web Next 3 practicals 2 weeks each (expect you to spend time on this outside Monday session) Final practical (Classification): 3 weeks, write-up 2

3 Course material Books Jensen, J. R. (2000) Remote Sensing of the Environment: An Earth Resource Perspective, 2000, Prentice Hall, New Jersey. (Excellent on RS but no image processing, around 40 from Waterstones). Jensen, J. R. (2005, 3rd ed.) Introductory Digital Image Processing, Prentice Hall, New Jersey. (Companion to above, around 40 from Waterstones) BUT mostly available online at Lillesand, T. M., Kiefer, R. W. and Chipman, J. W. (2004, 5th ed.) Remote Sensing and Image Interpretation, John Wiley, New York. (good all-round text-book around 35). Mather, P. M. (1999) Computer Processing of Remotely-sensed Images, 2nd Edition. John Wiley and Sons, Chichester. Campbell, J. B. (1996) Introduction to Remote Sensing (2nd Ed), London:Taylor and Francis. 3

4 Course material Web Tutorials ses/ Glossary of alphabet soup acronyms! Other resources NASA NASAs Visible Earth (source of data): European Space Agency NOAA Remote sensing and Photogrammetry Society UK 4

5 Format of the course Course outline Lecture 1: Introduction to EO Lecture 2: Introduction to image processing (image display/enhancement) - practical 1 Lecture 3: Introduction to electromagnetic spectrum Lecture 4: Image arithmetic: ratios and spectral indices - practical 2 Lecture 5: EM spectrum and the atmosphere, plus angular info. Lecture 6: Information extraction: spatial filtering and classification - practical 3 (assessed practical) Lecture 7: Spatial, spectral, temporal resolution and tradeoffs Lecture 8: Orbits, swaths and coverage Lecture 9: Pre-processing stages and sensor scanning mechanisms Lecture 10: Applications plus recap 5

6 Lecture outline General introduction to remote sensing (RS), Earth Observation (EO)... definitions of RS Why do we do it? Applications and issues Who and where? Concepts and terms remote sensing process, end-to-end 6

7 What is remote sensing? The Experts say "Remote Sensing is......techniques for collecting image or other forms of data about an object from measurements made at a distance from the object, and the processing and analysis of the data (RESORS, CCRS)....the science (and to some extent, art) of acquiring information about the Earth's surface without actually being in contact with it. This is done by sensing and recording reflected or emitted energy and processing, analyzing, and applying that information. 7

8 What 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 8

9 First aerial photo credited to Frenchman Felix Tournachon in Bievre Valley, Boston from balloon (oldest preserved aerial photo), 1860, by James Wallace Black. Remote Sensing Examples 9

10 Remote Sensing Examples Kites (still used!) Panorama of San Francisco, Up to 9 large kites used to carry camera weighing 23kg. 10

11 Remote Sensing Examples 11

12 Remote Sensing: scales and platforms Not always big/expensive equipment Individual/small groups Calibration/validation campaigns 12

13 Remote Sensing: scales and platforms Both taken via kite aerial photography

14 Remote Sensing: scales and platforms upscale upscale upscale Platform depends on application What information do we want? How much detail? What type of detail? 14

15 Remote Sensing: scales and platforms E.g. aerial photography From multimap.com Most of UK Cost? Time? 15

16 Remote Sensing: scales and platforms upscale Many types of satellite Different orbits, instruments, applications 16

17 Remote Sensing Examples Global maps of vegetation from MODIS instrument 17

18 Remote Sensing Examples Global maps of sea surface temperature and land surface reflectance from MODIS instrument 18

19 Remote sensing applications Environmental: climate, ecosystem, hazard mapping and monitoring, vegetation, carbon cycle, oceans, ice Commercial: telecomms, agriculture, geology and petroleum, mapping Military: reconnaissance, mapping, navigation (GPS) Weather monitoring and prediction Many, many more 19

20 EO process in summary... Collection of data Some type of remotely measured signal Electromagnetic radiation of some form Transformation of signal into something useful Information extraction Use of information to answer a question or confirm/contradict a hypothesis 20

21 Remote sensing process: I Statement of problem Data collection Data analysis Presentation of information What information do we want? Appropriate problem-solving approach? Formulate hypothesis In situ: field, lab, ancillary data (Meteorology? Historical? Other?) EO data: Type? Resolution? Cost? Availability? Pre/post processing? Analog: visual, expert interp. Digital: spatial, photogrammetric, spectral etc. Modelling: prediction & understanding Information extraction Products: images, maps, thematic maps, databases etc. Models: parameters and predictions Quantify: error & uncertainty analysis Graphs and statistics Hypothesis testing 21

22 The Remote Sensing Process: II Collection of information about an object without coming into physical contact with that object Passive: solar reflected/emitted Active:RADAR (backscattered); LiDAR (reflected) 22

23 The Remote Sensing Process: III What are we collecting? Electromagnetic radiation (EMR) What is the source? Solar radiation passive - reflected, emitted OR artificial source active - RADAR, LiDAR 23

24 Electric field (E) Magnetic field (M) Perpendicular and travel at velocity, c (3x10 8 ms -1 ) Electromagnetic radiation? 24

25 Energy radiated from sun (or active sensor) Energy 1/wavelength (1/ ) shorter (higher f) == higher energy longer (lower f) == lower energy from 25

26 Information What type of information are we trying to get at? What information is available from RS? Spatial, spectral, temporal, angular, polarization, etc. 26

27 Spectral information: vegetation very high leaf area NIR, high reflectance reflectance(%) very low leaf area sunlit soil Visible green, higher than red Visible red, low reflectance Wavelength, nm 27

28 Spectral information: vegetation 28

29 Colour Composites: spectral Real Colour composite Red band on red Green band on green Blue band on blue Approximates real colour (RGB colour composite) Landsat TM image of Swanley,

30 Change detection Temporal information Rondonia 1975 Rondonia 1986 Rondonia

31 Always 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 it spatially reference the data 31

32 Remote Sensing: What is it and why do we use it? Many monitoring issues global or regional Drawbacks of in situ measurement.. Remote sensing can provide (not always!) Global coverage Range of spatial resolutions Temporal coverage (repeat viewing) Spectral information (wavelength) Angular information (different view angles) 32

33 Why 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, consistent remote access some historical data (1960s/70s+) move to quantitative RS e.g. data for climate some commercial applications (growing?) e.g. weather typically (geo)'physical' information but information widely used (surrogate - tsetse fly mapping) derive data (raster) for input to GIS (land cover, temperature etc.) 33

34 Caveats! Remote sensing has many problems Can be expensive Technically difficult NOT direct measure surrogate variables e.g. reflectance (%), brightness temperature (Wm -2 o K), backscatter (db) RELATE to other, more direct properties. 34