1. Lecture 20 – review Labs: questions Next Wed – Final: 18 March 10:30-12:20 Thursday, 12 March

2. - physical basis of remote sensing - spectra - radiative transfer - image processing - radar/lidar -thermal infrared - applications

3. What is remote sensing? Measurement from a distance Wide range of wavelengths Hazardous locales Images pixels DNs scanners, orbits image geometry, parallax resolution color vs. intensity and texture

4. The spectrum and wavelength regions Units of radiance, irradiance, spectral radiance Color mixing, RGB false color images Color is due to absorption: e -kz (Beer Law) Hue, saturation, intensity

5. Radiative transfer Sunlight, atmospheric absorption & scattering Rayleigh, Mie, Non-selective Reflection – 1 st surface (Fresnel’s Law), volume Planck function: -5 (exp(c/ T)-1)  Atmospheric windows DN = g·(  e ·r ·  i ·I toa ·cos(i)/  +  e · r·I s↓ /  + L s↑ ) + o r I cos(i)/  : Lambert’s law

6. When do you need atmospheric compensation? dark object subtraction Modtran model

7. Interaction of Energy and Matter Rotational absorption (gases) Electronic absorption Charge-Transfer Absorptions Vibrational absorption Spectra of common Earth-surface materials

8. Image processing algorithms radiometry geometry Spectral analysis Statistical analysis Modeling Algorithms: Ratioing Spectral mixture analysis max number of endmembers = n+1 shade NDVI

9. Classification – spectral similarity supervised vs. unsupervised maximum likelihood vs parallelipiped themes & land use validation confusion matrix

10. Confusion matrices Well-named. Also known as contingency tables or error matrices Here’s how they work… Training areas A B C D E F A B C D E F Classified data Col sums Row sums Grand sum All non diagonal elements are errors Row sums give “commission” errors Column sums give “omission” errors Overall accuracy is the diagonal sum over the grand total This is the assessment only for the training areas What do you do for the rest of the data? p 586, LKC 6 th 480 05000485 0 0 0 0 0 0 0 0 480 52 16 681992 0200 0 72

11. Crater counting – relative dating on the moon and Mars Forest remote sensing SMA in forest studies Shade endmember vs. canopy vs. topography What can Lidar do for forest characterization?

12. Layover Shadows Polarization Sensitivity to - dielectric - roughness Corner reflectors Interferometry

13. LiDAR

14. Thermal  ( ) T Planck’s Law: R =  ( ) c 1  -1 -5 [exp(c 2 -1 T -1 )-1] -1 Emissivity Blackbody radiation

15. What compositions can be determined in the TIR? Mostly vibrational resonance, not electronic processes therefore, relatively large molecules Silicate minerals (SiO 4 -4 ); quartz (SiO 2 ) Sulfates (SO 4 = ); sulfur dioxide (SO 2 ) Carbonates (CO 3 = ); carbon dioxide (CO 2 ) Ozone (O 3 ) Water (H 2 O) Organic molecules

16. Mauna Loa, Hawaii MASTER VNIR daytime ASTER TIR, daytime MTI TIR, nighttime