1. Remote Sensing and Internet Data Sources Unit 3: Module 12, Lecture 1 – Satellites and Aerial Photography

2. Developed by: Host Updated: 1.05.05 U3-m12-s2 Sources of spatial and environmental data  Remotely sensed data (raster data)  Airphoto  Satellite  Digital data repositories - (Module 14)  On-line  Electronic media  GPS data (point data) - (Module 16)  Input of hard-copy data – (Module 16)  Digitizing (vector data)  Scanning (raster data)

3. Developed by: Host Updated: 1.05.05 U3-m12-s3 Sources of data: remote imagery  Satellite imagery  Digital imagery  Numerous satellites with different levels of resolution  SeaWIFS  SPOT  LANDSAT  AVHRR  MODIS MODIS image of Hurricane Isobel off US East Coast, September 17, 2003

4. Developed by: Host Updated: 1.05.05 U3-m12-s4 SeaWIFS image of California Fires Oct 26, 2003 SeaWIFS  1 km res  Daily  NASA

5. Developed by: Host Updated: 1.05.05 U3-m12-s5 QuickBird image of Grand Prix Fire, CA October 27, 2003 60 cm resolution natural color image

6. Developed by: Host Updated: 1.05.05 U3-m12-s6 QuickBird image of Grand Prix Fire, CA October 27, 2003 – detail view

7. Developed by: Host Updated: 1.05.05 U3-m12-s7 GOES Weather Satellite  Geostationary orbit 36,000 km above earth  East and West satellites provide complete coverage  High frequency (up to 15 min intervals)  Visible  Infrared  Water vapor

8. Developed by: Host Updated: 1.05.05 U3-m12-s8 Resolution in Satellite imagery  Satellite sensors vary in the different types of resolution  Spatial resolution = pixel size  Spectral resolution = # of bands, band width  Radiometric resolution = data intensity in band  Temporal resolution = frequency of sampling

9. Developed by: Host Updated: 1.05.05 U3-m12-s9 Pixel resolution  1 km AVHRR classification of forest land  Relatively coarse  Broad picture of landscape  Regional assessment  30 m LANDSAT classification of forest and land use  Much finer detail  Local assessment

10. Developed by: Host Updated: 1.05.05 U3-m12-s10 Spectral Resolution: Number of bands  “Bands” are regions of the electromagnetic spectrum sampled by the sensor  Visible light (RGB)  Near and far infrared  Other frequencies  More bands = more information to classify land features  Multispectral  Hyperspectral – very fine divisions of the spectrum Landsat MSS4 bands Landsat TM7 bands Quickbird4 bands Hyperspectral30-256+ bands

11. Developed by: Host Updated: 1.05.05 U3-m12-s11 Landsat Thematic Mapper bands BandSpectral range Use 1BlueBathymetric mapping/deciduous-coniferous veg 2GreenPeak vegetation – plant vigor 3RedVegetation slopes 4Near IRBiomass content/ shorelines 5Mid IRMoisture content of soil and vegetation 6Thermal IRThermal mapping/ soil moisture 7Short wave IRHydrothermally altered rocks

12. Developed by: Host Updated: 1.05.05 U3-m12-s12 Image classification  Remote sensing satellites and aircraft-borne sensors simply record information on spectral reflectance  The science of “Image Classification” makes these volumes of information useful  Goal – develop a relationship between the “spectral signature” and a classification of the landscape  Coarse: forest, ag, urban  Fine: aspen forest, corn, high-density residential

13. Developed by: Host Updated: 1.05.05 U3-m12-s13 Differences in “spectral signatures” are used to classify land features

14. Developed by: Host Updated: 1.05.05 U3-m12-s14 Common classified satellite images ClassificationSatellite/ sensor Pixel resolution USFS Forest Land coverAVHRR1 km Coastwatch Sea Surface Temp MODIS/ Aqua 1 km National Land Cover Dataset (NLCD) Landsat30 m NOAA C-CAP Land use change Landsat30 m

15. Developed by: Host Updated: 1.05.05 U3-m12-s15 Sources of data: remote imagery Aerial photography and imagery  Film technology  Oblique  Vertical  Black and White  Color Infrared - common in agriculture and forestry applications  Usually interpreted as map polygons (vector format) B & W photo Color IR Photointerpreted Oblique photo

16. Developed by: Host Updated: 1.05.05 U3-m12-s16 Sources of data: remote imagery Aerial photography and imagery  Digital imagery  Images from non- photographic sensors  Usually classified by computer algorithms  Multispectral or hyperspectral available AISA hyperspectral sensor Hyperspectral crop circles courtesy CALMIT labs, NE

17. Developed by: Host Updated: 1.05.05 U3-m12-s17 Hyperspectral data  A large number of spectral bands (30-100s)  Capable of discriminating very fine differences in color (reflectance)  Used to map aquatic veg, Chlorophyll content, turbidity, many other attributes Hyperspectral image of Kingsbury Creek – image acquired by Nebraska Space Grant for WOW

18. Developed by: Host Updated: 1.05.05 U3-m12-s18 Common aerial photography: DOQs  USGS Digital Orthophoto Quad  Natl’ Aerial Photography Program (NAPP)  Cloud-free  20000 ft altitude  B&W or CIR  Each photo 5.5 x 5.5 mi  Began in 1987  5-7 yr photography cycle  Big files!  Med resolution – 40 Mb  High res. – 117 Mb-1.3 Gb Color-infrared NAPP photo San Diego, CA

19. Developed by: Host Updated: 1.05.05 U3-m12-s19 Common aerial photography: FSA DOQs  Farm Services Administration (FSA) Color Orthophotos  1 m resolution natural color imagery  Summer – leaf on  Available in quarter quads  Available as unclassified imagery, but very good resolution FSA photo – 1:7,000 scake Houston Co, MN

20. Developed by: Host Updated: 1.05.05 U3-m12-s20 Sources of data: scanned imagery  Scanning and rectification (raster data)  Hard copies of airphotos or other images can be scanned at high resolution (600-800 dpi)  These typically need to be georectified to use with other spatial layers (correct for camera lens abberations, plane tilt, etc)  Control points (known locations on ground) are used to georectify image  ImageWarp or other software used to “stretch” image to fit control points  Image can then be used as a backdrop for other spatial data layers, or for classification

21. Developed by: Host Updated: 1.05.05 U3-m12-s21 Summary  Classified data from satellites are useful for land use planning, but the efforts involved in classification mean these are updated relatively infrequently (years)  Real-time satellite data (AVHRR, SeaWIFS, GOES) are typically unclassified, but can be interpreted visually with relatively little effort  Aerial photographs provide high resolution coverage (meter to submeter), and many on- line sources of recent photography exist