Integration of sensors for photogrammetry and remote sensing 8 th semester, MS 2005

Course overview

1. Introduction to RS 2. Image geometry, georeferencing, orthorectification 3. Platforms for optical sensors 4. Microwave sensing 5. Digital airborne cameras, ALS

Introduction to remote sensing

• • • • RS principle – – Electromagnetic energy Sources of energy Interaction with atmosphere and Earth surface Ideal and real RS system – – Optical sensors for RS Photographic systems • • Digital sensors Multispectral sensors Hyperspectral sensors

Remote sensing principle

• Information about an object, area, or phenomenon is obtained through the analyses of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation (Lillesand) • Specifically: Data collected by sensors detecting elmg. energy that are operated from airborne or spaceborne platforms • Goal: inventory, mapping, and monitoring earth resources

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Remote sensing principle

(6) (7) (5) (3) Reference data 1.

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sources of energy propagation though the atmosphere interaction with Earth surface re-transmition through the atmosphere sensing systems sensing products interpretation and analysis information products users (8) (9)

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Energy interactions in the atmosphere

– – Scattering unpredictable diffusion of radiation by particles in the atmosphere Rayleigh scatter – ‘blue sky’, haze in imagery – – Absorption mainly due to water vapor, carbon dioxide, and ozone atmospheric windows

Energy interactions with Earth surface features

E I ( λ)=E R ( λ)+E A ( λ)+E T ( λ) E I ( λ) – incident energy E R ( λ) – reflected energy E A ( λ) – absorbed energy E T ( λ) – transmitted energy • • Reflectance specular diffuse (most interesting in RS)

Energy interactions with Earth surface features

Spectral reflectance: р λ = E R ( λ)/ E I ( λ)

Detection of elmg. energy

• • – – – photographically light-sensitive film (silver halide crystals) range 0.3 – 0.9 µm (from UV to near IR) high spatial resolution and geometric fidelity – – – electronically CDDs (charge coupled devices), A-to-D signal conversion broad spectral range electronic storage, transmittance, and processing of data • • Sensing systems active passive

Ideal RS system

• • • • • • uniform energy source noninterfering atmosphere unique energy interactions at earth’s surface features sensor highly sensitive to all wavelengths with high spatial resolution real-time data processing advanced data users

RS projects

• • • • • Definition of the problem Evaluation of the potential of RS techniques for solving the problem Appropriate data acquisition (including reference data needed) Data processing and interpretation procedures Quality evaluation of results

Examples of RS applications

• • • • Mapping (DEM, topographic mapping) Environmental engineering Geology • • • Hydrology (soil moisture, glacier dynamics monitoring) Agriculture (crop type mapping, crop monitoring) Forest engineering (species identification) Disaster management (burn mapping, floods) Examples Advantege of multispectral, multiresolution, multitemporal data

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Multispectral scanners

Range of sensing 0.3 - 14 µm (UV, visible, near-IR, mid-IR and thermal IR), several narrow bands Across-track scanner Along-track scanner A.

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Oscillating mirror Detectors IFOV GSD Angular field of view Swath A.

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Linear array of detectors Focal plane of the image Lens GSD

Example: LANDSAT’s TM Band No.

Wavelength Interval (µm) Spectral Response Resolution (m) 1 2 3 4 5 6 7 0.45 - 0.52

0.52 - 0.60

0.63 - 0.69

0.76 - 0.90

Blue-Green Green Red Near IR 1.55 - 1.75

Mid-IR 10.40 - 12.50 Thermal IR 2.08 - 2.35

Mid-IR 30 30 30 30 30 120 30

Example: LANDSAT’s TM

Hyperspectral imaging

• • • Range of sensing from visible to mid-IR Hundreds of detectors measuring in narrow bands, typically 0.4 – 2.4 µm AVIRIS (Airborne Visible/Infrared Imaging Spectrometer)

Hyperspectral imaging

Hyperspectral imaging

Spectral libraries

http://speclib.jpl.nasa.gov/

Links

• – Remote sensing tutorials: NASA http://rst.gsfc.nasa.gov/Front/tofc.html

– Ohio university http://dynamo.phy.ohiou.edu/tutorial/tutorial_files/frame.htm

– Canada centre for RS http://www.ccrs.nrcan.gc.ca/ccrs/learn/tutorials/fundam/chapter1/chapter1 _1_e.html

• Remote sensing glossary http://www.casde.unl.edu/vn/glossary/intro.htm

http://www.ldeo.columbia.edu/res/fac/rsvlab/glossary.html