REMOTE SENSING
Remote sensing is the acquisition of information about an object or
phenomenon, without making physical contact with the object. In modern usage,
the term generally refers to the use of aerial sensor technologies to detect
and classify objects on Earth (both on the surface, and in the atmosphere and oceans)
by means of propagated signals
(e.g. electromagnetic radiation emitted from aircraft or satellites).
Remote sensing can be defined as the
collection of data about an object from a distance. Humans and many other types
of animals accomplish this task with aid of eyes or by the sense of smell or
hearing. Earth scientists use the technique of remote sensing to monitor or measure phenomena found in the Earth's lithosphere, biosphere, hydrosphere, and atmosphere.
Remote sensing of the environment by geographers is usually done with the help
of mechanical devices known as remote sensors. These gadgets have a greatly
improved the ability to receive and record information about an object without
any physical contact. Often, these sensors are positioned away from the object of
interest by using helicopters, planes, and satellites. Most sensing devices
record information about an object by measuring an object's transmission of electromagnetic energy from reflecting and radiating surfaces. These sensors are
either passive or active. Passive sensors detect energy when the naturally
occurring energy is available such as sun energy. Active sensors provide their
own energy source as radar waves and record its reflection on the target.
Remote sensing imagery has many
applications in mapping land-use
and cover, agriculture, soils mapping, forestry, city planning, archaeological investigations, military
observation, and geomorphological surveying, among other uses. For example,
foresters use aerial photographs for preparing forest cover maps, locating possible access roads, and measuring quantities
of trees harvested. Specialized photography using color infrared film has also
been used to detect disease and insect damage in forest trees.
Satellite
Remote Sensing
In the 1960s, a revolution in remote
sensing technology began with the deployment of space satellites. From their
high vantage-point, satellites have a greatly extended view of the Earth's
surface. The first meteorological satellite, TIROS-1 (Figure 3), was launched
by the United States using an Atlas rocket on April 1, 1960. This early weather
satellite used vidicon cameras to scan wide areas of the Earth's surface. Early
satellite remote sensors did not use conventional film to produce their images.
Instead, the sensors digitally capture the images using a device similar to a
television camera. Once captured, this data is then transmitted electronically
to receiving stations found on the Earth's surface. The image below (Figure 4)
is from TIROS-7 of a mid-latitude cyclone off the coast of New Zealand.
Figure 3: TIROS-1 satellite.
(Source: NASA - Remote Sensing
Tutorial)
|
Figure 4: TIROS-7 image of a
mid-latitude cyclone off the coast of New Zealand, August 24, 1964. (Source:
NASA - Looking at Earth
From Space)
|
Figure 5: Color image from GOES-8
of Hurricanes Madeline and Lester off the coast of Mexico, October 17, 1998.
(Source: NASA - Looking at Earth
From Space)
|
Today, the GOES (Geostationary
Operational Environmental Satellite) system of satellites provides most of the
remotely sensed weather information for North America. To cover the complete
continent and adjacent oceans two satellites are employed in a geostationary
orbit. The western half of North America and the eastern Pacific Ocean is monitored by GOES-10, which is directly above the equator and 135°
West longitude. The eastern half of North America and the western Atlantic are
cover by GOES-8. The GOES-8 satellite is located overhead of the equator and
75° West longitude. Advanced sensors aboard the GOES satellite produce a
continuous data stream so images can be viewed at any instance. The imaging
sensor produces visible and infrared images of the Earth's terrestrial surface
and oceans (Figure 5). Infrared images can depict weather conditions even
during the night. Another sensor aboard the satellite can determine vertical temperature profiles, vertical moisture profiles, total precipitable
water, and atmospheric stability.
Figure 6: The Landsat 7 enhanced
Thematic Mapper instrument. (Source: Landsat
7 Home Page)
In the 1970s, the second revolution
in remote sensing technology began with the deployment of the Landsat
satellites. Since 1972, several generations of Landsat satellites with their
Multispectral Scanners (MSS) have been providing continuous coverage of the
Earth for almost 30 years. Currently, Landsat satellites orbit the Earth's surface
at an altitude of approximately 700 kilometers. Spatial resolution of objects
on the ground surface is 79 x 56 meters. Complete coverage of the globe
requires 233 orbits and occurs every 16 days. The Multispectral Scanner records
a zone of the Earth's surface that is 185 kilometers wide in four wavelength
bands: band 4 at 0.5 to 0.6 micrometers; band 5 at 0.6 to 0.7 micrometers; band
6 at 0.7 to 0.8 micrometers; and band 7 at 0.8 to 1.1 micrometers. Bands 4 and
5 receive the green and red wavelengths in the visible light range of the electromagnetic spectrum. The last two bands image near-infrared wavelengths. A
second sensing system was added to Landsat satellites launched after 1982. This
imaging system, known as the Thematic Mapper, records seven wavelength bands
from the visible to far-infrared portions of the electromagnetic spectrum
(Figure 6). In addition, the ground resolution of this sensor was enhanced to
30 x 20 meters. This modification allows for greatly improved clarity of imaged
objects.
Figure 7: SPOT false-color image of
the southern portion of Manhatten Island and part of Long Island, New York. The
bridges on the image are (left to right): Brooklyn Bridge, Manhattan Bridge,
and the Williamsburg Bridge. (Source: SPOT Image)
SPOT (Satellite Pour l'Observation
de la Terre) satellite program has launched five satellites since 1986. Since
1986, SPOT satellites have produced more than 10 million images. SPOT
satellites use two different sensing systems to image the planet. One sensing
system produces black and white panchromatic images from the visible band (0.51
to 0.73 micrometers) with a ground resolution of 10 x 10 meters. The other
sensing device is multispectral, capturing green, red, and reflected infrared
bands at 20 x 20 meters (Figure 7). SPOT-5, which was launched in 2002, is much
improved from the first four versions of SPOT satellites. SPOT-5 has a maximum
ground resolution of 2.5 x 2.5 meters in both panchromatic mode and
multispectral operation.
Figure 8: Radarsat image acquired on
March 21, 1996, over Bathurst Island in Nunavut, Canada. This image shows
Radarsat's ability to distinguish different types of bedrock. The light shades on this image (C) represent areas of limestone, while the darker regions (B) are composed of sedimentary siltstone. The very dark area marked A is Bracebridge Inlet which
joins the Arctic Ocean.
(Source: Canadian Centre for Remote Sensing)
Radarsat-1 was launched by the
Canadian Space Agency in November, 1995. As a remote sensing device, Radarsat
is quite different from the Landsat and SPOT satellites. Radarsat is an active
remote sensing system that transmits and receives microwave radiation. Landsat
and SPOT sensors passively measure reflected radiation at wavelengths roughly
equivalent to those detected by our eyes. Radarsat's microwave energy
penetrates clouds, rain,
dust, or haze and produces images regardless of the sun's illumination allowing
it to image in darkness. Radarsat images have a resolution between 8 to 100 meters. This sensor has found important applications in crop monitoring, defense surveillance, disaster monitoring, geologic
resource mapping, sea-ice mapping and monitoring, oil slick detection, and
digital elevation modeling (Figure 8).
Figure 9: Yankee stadium in
Brooklyn, New York. Baseball stadiums have an obvious shape that can be easily
recognized even from vertical aerial photographs. (Source: Google
Earth)
Most people have no problem
identifying objects from photographs taken from an oblique angle. Such views
are natural to the human eye and are part of our everyday experience. However,
most remotely sensed images are taken from an overhead or vertical perspective
and from distances quite removed from ground level. Both of these circumstances
make the interpretation of natural and human-made objects somewhat difficult.
In addition, images obtained from devices that receive and capture electromagnet wavelengths outside human vision can present views that are
quite unfamiliar.
To overcome the potential
difficulties involved in image recognition, professional image interpreters use
a number of characteristics to help them identify remotely sensed objects. Some
of these characteristics include:
- Shape: this characteristic alone may serve to identify many objects. Examples include the long linear lines of highways, the intersecting runways of an airfield, the perfectly rectangular shape of buildings, or the recognizable shape of an outdoor baseball diamond (Figure 9).
- Size: noting the relative and absolute sizes of objects is important in their identification. The scale of the image determines the absolute size of an object. As a result, it is very important to recognize the scale of the image to be analyzed.
Figure 10: Black and white aerial
photograph of natural coniferous vegetation (left) and adjacent apple orchards
(center and right). (Source: PhysicalGeography.net)
- Image Tone or Color: all objects reflect or emit specific signatures of electromagnet radiation. In most cases, related types of objects emit or reflect similar wavelengths of radiation. Also, the types of recording device and recording media produce images that are reflective of their sensitivity to particular range of radiation. As a result, the interpreter must be aware of how the object being viewed will appear on the image examined. For example, on color infrared images vegetation has a color that ranges from pink to red rather than the usual tones of green.
- Pattern: many objects arrange themselves in typical patterns. This is especially true of human-made phenomena. For example, orchards have a systematic arrangement imposed by a farmer, while natural vegetation usually has a random or chaotic pattern (Figure 10).
- Shadow: shadows can sometimes be used to get a different view of an object. For example, an overhead photograph of a towering smokestack or a radio transmission tower normally presents an identification problem. This difficulty can be over come by photographing these objects at sun angles that cast shadows. These shadows then display the shape of the object on the ground. Shadows can also be a problem to interpreters because they often conceal things found on the Earth's surface.
- Texture: imaged objects display some degree of coarseness or smoothness. This characteristic can sometimes be useful in object interpretation. For example, we would normally expect to see textural differences when comparing an area of grass with a field corn. Texture, just like object size, is directly related to the scale of the image.
Remote sensing is not only used for
target discrimination but also to monitor any natural and artificial changes on the earth.
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