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

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

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

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

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

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

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

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

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

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

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?

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

LiDAR

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

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

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