Remote sensing

3,892 views

Published on

Basic introduction to remote sensing

Published in: Education
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
3,892
On SlideShare
0
From Embeds
0
Number of Embeds
5
Actions
Shares
0
Downloads
35
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Remote sensing

  1. 1. Remote sensing what is it? • Observation from a distance – Aerial photographs- very detailed – Satellite images – global view
  2. 2. Oblique aerial photograph • Viewed from an oblique angle: looking sideways • Looks natural, easy to understand, useless for measurement purposes
  3. 3. Vertical aerial photograph • Viewed straight down giving a “map view” • Difficult to understand at first. Can be used as a basis of mapping, after image has been rectified
  4. 4. Aerial photographs: 1995 and 1972
  5. 5. Map derived form aerial photographs • Visible features are “digitised” by tracing around them on a computer screen. • This creates the points lines and polygon symbols which build up into the map
  6. 6. Stereo-photography 3-d visualisation • Overlapping aerial photographs can be used to build 3-d stereoscopic visual models. These can be used to map out contours and heights of features Photo 1 Photo 2 Plane travels at constant altitude above sea level. Height above ground varies with topography overlap 60% of image
  7. 7. Stereoscopic reconstruction of overlapping areas • A stereoscope is used to view the overlapping areas simultaneously and the brain builds a 3-d model of the landscape where the images overlap. Left eye Right eye Photo 1 Photo 2 overlap
  8. 8. Digital manipulation of aerial photographs • 3-d models can also be built by “digitally draping” photographs over a digital elevation model of the landscape.
  9. 9. Satellite Remote Sensing • Satellites give a higher viewpoint and give unrestricted coverage of the whole globe Link to Gateway Remote sensing video http://gateway.rac.ac.uk/mod/resource/view.php?id=3937
  10. 10. Satellite orbits • Geostationary orbit: above the equator, 35,000 km height, orbital period 24 hours. Satellites appear fixed in sky • Low Earth orbit, usually polar, orbital period can be less than 1 hour. Satellites seen to move across sky
  11. 11. Geo-stationary meteorological satellite: Meteosat • Geostationary orbits, approx 33,000 km elevation over the equator. Satellite takes 24hours for one orbit, the earth rotates once in that time, so the satellite appears stationary in the sky Infra-red image from 0600 March 8, 2000 “Ground” position of satellite
  12. 12. Polar orbiting meteorological satellites • Polar orbiting satellites cover the whole globe, but move, so there are long time intervals between one image and the satellites next return
  13. 13. Earth observation satellites: Landsat 7 image (30m resolution) • Earth observation satellites are designed to view the surface of the globe. Some are designed for view the oceans, others, like the Landsat series, observe the land
  14. 14. 2004 tsunami: Aceh province, Sumatra
  15. 15. Land classification; spectral signatures: Using SPOT images • Simultaneous Multi-spectral images can be used to classify landcover. • The reflectance of certain landcover types are measured on each image to build up a signature of that type of cover. This is then searched for over the whole image
  16. 16. Land classification; spectral signatures: Using SPOT images • Simultaneous Multi-spectral images can be used to classify landcover. • The reflectance of certain landcover types are measured on each image to build up a signature of that type of cover. This is then searched for over the whole image
  17. 17. Land classification; spectral signatures: Using SPOT images • Simultaneous Multi-spectral images can be used to classify landcover. • The reflectance of certain landcover types are measured on each image to build up a signature of that type of cover. This is then searched for over the whole image
  18. 18. Land classification; spectral signatures: Using SPOT images • Simultaneous Multi-spectral images can be used to classify landcover. • The reflectance of certain landcover types are measured on each image to build up a signature of that type of cover. This is then searched for over the whole image
  19. 19. Remote sensing images: Harnhill farm
  20. 20. Aerial photograph, 2011 Spatial resolution approx. 0.2m
  21. 21. GoogleMaps aerial photo, Spatial resolution approx. 1.5m
  22. 22. Scanned Aerial photograph, 1995 Spatial resolution approx. 2.0m
  23. 23. Landsat 5 image, circa 2001 Spatial resolution approx. 30m
  24. 24. LIDAR image, 2011 Spatial resolution approx. 1.0m Vertical resolution approx. 0.001m
  25. 25. Landsat 5: 30m resolution • Landsat 5 image of Gloucestershire (Landsat 6 crashed on take off. Landsat 7 is current satellite, Landsat 8 has just been launched) Cheltenham Gloucester Harnhill Swindon
  26. 26. Ikonos: 1m resolution • Commercial panchromatic image at 1m resolution. On the original image people can be seen walking in Horse Guards Parade and the spokes of the London Eye are visible
  27. 27. Ikonos agricultural image 1m resolution • Another Ikonos image showing the detail available in an agricultural image, here form Montana • How useful is this for farmers?
  28. 28. RADARSAT classified image of Flevoland, NL • Radar, “active remote sensing”, sees through clouds and in the dark. This addresses some of the major problems with “Passive remote sensing” which measures reflected sunlight. • The images are very difficult to interpret
  29. 29. RADARSAT-Mozambique floods • Shuttle borne radar image of the Mozambique floods • Radar is good at detecting the edge of water bodies, which it can “see” through cloud cover
  30. 30. Remote sensing summary • Aerial photography gives us a controllable, highly detailed view of the Earth • Satellite imagery gives global, unrestricted views which are repeated a frequent intervals • The references to actions such as “interpretation” and “classification” lead on to things we can do with a geographic information system (GIS) using remote sensing as a source of data

×