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Lecture 1

HYPERLINK "http://www.gis.unbc.ca/courses/geog432/lectures/lect20/index.php"

Introduction

Assumed Concepts & Topics

from Geog205 and Geog300:

Scale, map projections and the UTM system

Generalization

Raster and vector systems

GIS themes or layers

The electromagnetic spectrum

Remote Sensing

"... is the collection of information about an object without direct contact (from a distance) "

The analogue unit of data collection is the photograph (aerial or from space), from a camera.

The digital unit is the pixel, created by using a scanner.

Scale is a function of distance from the object, system quality and resolution.

Analogue or digital systems can generate various images along the electromagnetic spectrum.

All photographs are also 'images' but digital images are not photographs.

A digital image processing system must be RASTER, but may also have vector capabilities.

VECTOR systems may have some raster options, such as image display.

Traditional uses of remote sensing are interpretation, location & updating

Digital applications are classification & feature extraction

Milestones in the History of Remote Sensing

1839

Invention of photography

1910s

First use of aerial photography (World War I: photo interpretation)

1920s

Development of photogrammetry for mapping

1940s

Military use of radar (World War II)

1950s

Use of colour photography and infra-red

1962

Term 'remote sensing' first appeared

1970s

Launch of first weather satellites (Nimbus, Tiros)

1972

Launch of Landsat 1 (named ERTS-1) and multispectral sensor (MSS)

1982

Landsat 4 and 'the next generation sensor': Thematic Mapper (TM)

1985

Unix workstations and improved PCs enabling widespread use of digital imagery and GIS

1986

SPOT-1 satellite (France)

1990s

Other satellites: e.g. India, Japan, USSR; airborne spectrometers (e.g. CASI)

1995

Other satellites: e.g. India, Japan, USSR; airborne spectrometers (e.g. CASI)

2000

(?) High resolution private sector satellites

Lecture 2


ELECTROMAGNETIC SPECTRUM

1. Summary of the Electromagnetic Spectrum

The EM spectrum describes the range of wavelengths of energy that can be recorded using remote sensing. This includes shorter wavelengths that are reflected energy and medium-longer wavelengths that are emitted energy.

The unit of measurement is the nanometre (nm) and also the micrometre. 1 micrometre = 1000 nanometres 1 metre = 1 million micrometres

The major portions of the EM spectrum used in remote sensing for mapping and GIS applications are:

a. Visible wavelengths (.4-.7 micrometres or 400-700 nm)

Blue .4 to .5 (400 to 500 nm) Green .5 to .6 (500 to 600 nm) Red .6 to .7 (600 to 700nm)

b. Near infrared .7 - 1.3mu and Mid IR (1.3 -3mu)

Wavelengths up to about 1.1 micrometres can be captured using photography; longer wavelengths REQUIRE a scanner, which can be used for all wavelengths. Energy in the near & mid IR is non-visible, reflected.

c. Far IR (Thermal) 3 - 14mu

In these wavelengths, we record energy emitted from the earth

d. Microwave (including radar) 1mm - 1 metre

2. Spectral Resolution

The width of each portion of the EM spectrum captured by a scanner defines the spectral resolution of the system. A small width equals a finer resolution.

3. Spectral Signatures

Spectral signature graphs, show the relative amount of reflection or emission from an object across different wavelengths. Every object varies in the amount of energy reflection, otherwise for example they would all appear to be black, white or a shade of gray on colour film if there were equal reflection in red, green and blue (RGB) wavelengths.

4. Spatial Resolution

Spatial resolution is a measure of the size of the pixels. This determines the precision or scale of the data. Remote sensing data generally varies from 1 metre to 1km (and in some cases, such as weather satellites, 10-100km). As with vector GIS, data collected at one scale is not usually suitable for analysis or mapping at another very different scale.

Remote sensing data and raster GIS data assume or give the impression that a pixel has one uniform value across its width. This may be true for a small pixel or a homogenous cover, such as a large lake, or field, but often we need to know the nature of geographic data and understand that what we are seeing is an average value for a variable forest or a mixture of different surface covers.

Lecture 3


DIGITAL DATA FORMATS & SYSTEMS

1. Raster data

Scanner input signal

Signal quantification

Mapping a continuous value into a discrete digital value

Digital grid/array arrangement of images

Values in the image

2. Statistical summary

Image histograms

Histogram transforms

Linear stretch

Histogram Equalization enhancement Data are partitioned into DNrange classes such that an equal number of pixels fall into each class. Greatest contrast is seen among pixels with the greatest frequency of occurrence in the image

Root/Logarithmic enhancement Useful for skewed Gaussian distributed DNs. The transfer function is logarithmic in shape

Piecewise linear stretch

Density slicing & pseudo-colour enhancements

DN thresholding

3. Data Storage Formats

Band Sequential (BSQ)

Band Interleaved by Line (BIL)

Band Interleaved by Pixel (BIP)

Run-length encoding

Desktop formats: tiff, gif, jpeg, pbm, pcx, sun raster, tga, xpm (X Window)

Lecture 4


1. Types of Platforms & Sensors

Platform: the satellite carrying the remote sensing device Sensor: the remote sensing device recording wavelengths of energy

Refer to class handouts for listing of sensor types and their qualities.

Satellite orbits can be one of two kinds: a. Sun-synchronous: the satellite passes over & captures imagery at the same time of day. b. Geostationary: the satellite orbits with the earth & is permanently over the same location.

Satellites & sensors designed for terrestrial mapping & earth resource monitoring are generally sun-synchronous, while weather satellites are geostationary.

The main ones (with date of first launch) to be discussed here are:

1972 Landsat Multispectral sensor (MSS)

1982 Landsat Thematic Mapper (TM)

1986 SPOT High Resolution Video (HRV)

1995 IRS (India) - (LISS)

1979 NOAA Advanced Very High Resolution Radiometer (AVHRR)

Some other satellites: 1974 GOES (Geostationary Orbital Environmental Satellite) 1978 Nimbus 1995 RADARSAT

2. Orbit & Sensor Characteristics

LANDSAT TM

SPOT HRV

Launch

1982

1986

Altitude

705 km

832 km

Attitude (polar)

8.2 degrees

8.7 degrees

Equatorial time

9.45 am

10.30 am

Swath width

185km

60km

Repeat coverage

16 days

26 days

Sensor

Thematic Mapper(TM)

High Resolution Visible (HRV)

Number of detectors

100

6000/3000

Advantages

#bands, swath size

higher resolution, #'looks'

Bands

7

1 + 3

Scanner type

Mirror (7 cycles/second

Pushbroom

3. Satellite Sensor Web Sites

Landsat (NASA) http://geo.arc.nasa.gov/sge/landsat/landsat.html

Spot Image (French Satellite) http://www.spot.com

IRS (Indian Remote Sensing) http://www.belspo.be/telsat/irs/irpg_001.htm

NOAA (Meteorological Satellite) http://www.ngdc.noaa.gov/

Russian Remote Sensing Satellites http://www.eds.dofn.de/www_eds/ids/ids_rss/irre01.htm

RADARSAT (Radar Sensor) http://www.rsi.ca

New 1 metre Satellites : IKONOS 1 http://www.flatoday.com/space/explore/stories/1998b/120398c.htm

Quickbird 1 http://www.digitalglobe.com/company/spacecraft

Orbimage http://www.orbimage.com

CCRS Data Search Site : http://ceocat.ccrs.nrcan.gc.ca/cgi-bin/

Lecture 5


DATA ACQUISITION & DISPLAY

In Canada most remote sensing data are ordered from Radarsat, Inc.: http://www.rsi.ca Scenes are previewed via the Canada Centre for Remote Sensing web site: http://ceocat.ccrs.nrcan.gc.ca

1. Acquiring Data

In multispectral sensing, data are captured at several wavelengths; Users first decide what scale (resolution) data are required ... and in conjunction : Which bands are suitable to determine which sensor is best.

Data are then ordered based on:

Location (lat/long or path/row) Data format (BIL, BIP, BSQ) Extent: whole scene or quadrant or portion Number of bands: whole or subset

Data may either be captured by a nadir looking sensor =Whiskbroom (e.g. Landsat), or a directable or pushbroom sensor (e.g. SPOT)

The table below lists database size and image extent:

Sensor Extent

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