Surveying ii ajith sir class3
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Atmospheric windows
Certain regions of the EM spectrum are completely
absorbed by the various gases that make up the
atmosphere, so that wavelengths in these regions cannot
be used for remote sensing of the Earth's surface. The
regions of the EM spectrum which are not affected by the
Earth's atmosphere are called 'atmospheric windows'
Atmospheric Windows• Atmospheric windows define wavelength ranges in which
the atmosphere is particularly transmissive of energy. • Visible region of the electromagnetic spectrum resides
within an atmospheric window with wavelengths of about 0.3 to 0.9 µm
• Emitted energy from the earth's surface is sensed through windows at 3 to 5 µm and 8 to 14 µm.
• Radar and passive microwave systems operate through a window region of 1 mm to 1 m.
Atmospheric Windows
The dominant windows in the atmosphere are in the visible and radio frequency regions, while X-Rays and UV are very strongly absorbed and Gamma Rays and IR are somewhat less strongly absorbed.
Atmospheric transmittance
Some wavelength regions of the EM spectrum are absorbed by atmospheric gases so cannot be used for remote sensing of the Earth's surface features.
Ideal Remote Sensing System
1. A uniform energy source.
2. A non-interfering atmosphere.
3. A series of unique energy/matter
interactions at the earth’s surface.
4. A super sensor.
5. A real-time data handling system.
6. Multiple data users.
AN IDEAL REMOTE SENSING SYSTEM
System Component 1
A uniform energy source
This source would provide energy over all wavelengths, at a constant, known, high level of output, irrespective of time and place.
AN IDEAL REMOTE SENSING SYSTEM
System Component 2
A non-interfering atmosphere
This would be an atmosphere that would not modify the energy from the source in any manner, whether that energy were on its way to the Earth’s surface or coming from it.
Again, ideally, this would hold irrespective of wavelength, time, place and sensing altitude involved.
AN IDEAL REMOTE SENSING SYSTEMSystem Component 3
A series of unique energy / matter interactions at the Earth’s surface
These interactions would generate reflected and/or emitted signals that not only are selective with respect to wavelength, but also are known, invariant, and unique to each and every surface type and subtype of interest.
AN IDEAL REMOTE SENSING SYSTEM
System Component 4
A super sensor
This would be a sensor, highly sensitive to all
wavelengths, yielding spatially detailed data on the
absolute brightness (or radiance) from a scene as a
function of wavelength, throughout the spectrum.
This super sensor would be simple and reliable,
require virtually no power or space, and be accurate
and economical to operate.
AN IDEAL REMOTE SENSING SYSTEM
System Component 5
A real-time data handling system
In this system, the instant a signal over a terrain element was
generated, it would be processed onto an interpretable format and
recognized as being unique to the particular terrain element from
which it came. This processing would be performed nearly
instantaneously (‘real-time’), providing timely information.
Because of the consistent nature of the energy/matter interactions,
there would be no need for reference data in the analysis
procedure. the derived data would provide insight into the
physical - chemical – biological state of each object of interest.
AN IDEAL REMOTE SENSING SYSTEMSystem Component 6
Multiple data users
These people would have knowledge of great depth, both of their respective disciplines and of remote sensing data acquisition and analysis techniques. The same set of data would be transformed into various forms of information for different users. This information would be available to them faster, at less expense, and for larger areas than information collected in any other manner. With this information, the various users would make profound, wise decisions about how best to manage the earth resource under scrutiny, and these strategic management decisions would be implemented
– to everyone’s delight !
But…..
An ideal remote sensing system does not and cannot exist. Real remote sensing systems fall far short of the ideal at virtually every point in the sequence outlined. Let us consider some of the basic shortcomings.
Basic shortcomings common to all real remote sensing systems
The energy source. All passive remote sensing systems rely on energy that is
either reflected and/or emitted from Earth surface features. The spectral distribution of reflected sunlight and self-emitted energy is far from uniform. Solar energy levels obviously vary with respect to time and location, and different Earth surface materials emit energy to varying degrees of efficiency.
The sources of energy used in all real systems are generally nonuniform with respect to wavelength and their properties
vary with time and location.
Basic shortcomings common to all real remote sensing systems
The atmosphere. The atmosphere normally compounds the problems
introduced by energy source variation. To some extent, the atmosphere always modifies the strength and spectral distribution of the energy received by a sensor. It restricts “where we can look” spectrally – atmospheric windows - and its effects vary with wavelength, time and place.
The importance of these effects is a function of the wavelengths involved, the sensor used, and the intended application. Eliminating, or compensating for, atmospheric effects via some form of calibration is particularly important in those applications which involve repetitive observations of the same geographical area.
Basic shortcomings common to all real remote sensing systems
The energy / matter interactions at the Earth’s surface.
Remote sensing would be simple if every material reflected and/or emitted energy in a unique, known way. Although spectral response patterns (signatures) play a central role in detecting, identifying, and analysing Earth surface materials, the spectral world is full of ambiguity.
Radically different material types can have great spectral similarity, making differentiation difficult. Furthermore, our understanding of the energy/matter interactions for Earth surface features is at an elementary level for some materials and virtually non-existent for others.
Basic shortcomings common to all real remote sensing systems
The sensor.
An ideal “super sensor” does not exist. No single sensor
is sensitive to all wavelengths and all real sensors have
detectors with fixed limits of spectral sensitivity. They
also have a limit on how small an object on the Earth’s
surface can be and still be “seen” by a sensor as being
separate from its surroundings.
This limit, called the spatial resolution of a sensor, is an
indication of how well a sensor can record spatial detail.
Basic shortcomings common to all real remote sensing systems
The choice of a sensor for any given task always involves tradeoffs.
Photographic systems generally produce images of very fine spatial resolution, but they lack the broad spectral sensitivity obtainable with non-photographic systems.
These requirements often dictate the type of platform from which a sensor can be operated. Platforms can vary from stepladders to space stations. Depending on the sensor/platform combination needed for a particular application, the acquisition of remote sensing data can be a very expensive endeavour.
Basic shortcomings common to all real remote sensing systems
The data-handling system. The capability of remote sensors to generate data far
exceeds our capacity to handle these data. This is generally true whether we consider “manual” image interpretation procedures or computer assisted analyses. Subsequently, the task of preparing data requires considerable thought, instrumentation, time, experience, and ground (and atmospheric) reference data.
While much data handling can be done by computers, personal intervention in data processing is and will continue to be essential to the productive application of remote sensor data.
Basic shortcomings common to all real remote sensing systems
The multiple data users. A thorough understanding of the problem at hand is
paramount to the productive application of any remote sensing methodology. Also, no single combination of data acquisition and analysis procedures will satisfy the needs of all data users.
Whereas the interpretation of aerial photography has been used as a practical resource management tool for nearly a century, newer forms of remote sensing have had relatively few satisfied users until recently. Increasing numbers of users, however, are becoming aware of the potentials, as well as the limitations, of remote sensing techniques.
Characteristics of Actual Remote Sensing Systems
fluctuating energy source atmosphere ambiguous and similar spectral response patterns
for features true sensor time lag few users
Data Acquisition
Recording Methods
1. Photographic 2. Digital
Photographic Data Acquisition
Remotely sensed photographic data are produced by directly recording the radiation from an object onto photographic film. The range of wavelengths which may be detected by photographic devices is limited by the sensitivities of the film and filter(s) being used in the camera. The spectral sensitivity of photographic film can range from ultraviolet to near infrared wavelengths.
Photographic data
Multi-band cameras, which simultaneously record multiple photographic impressions of an object, may be used to simulate a multi-spectral image. Such cameras use varying film and filter combinations to record different spectral regions in each photograph.
Photographic data
Remote sensing devices which record data photographically require that the film be recoverable for processing. Such devices can be carried by aircraft or retrievable spacecraft (such as the Space Shuttle).
Examples of data collected this way are aerial photography, the Large Format Space Camera imagery and Shuttle Imaging Radar scenes
Photographic data
Advantages of photographic imagery are the technical simplicity of its processing and interpretation. Both these factors tend to mean that it is available at a lower cost than digitally recorded data.
However, unlike digital scanners, photographic devices can only directly detect radiation in the visible and near infrared range of the electromagnetic spectrum so such data are affected by cloud cover.
Digital Data Acquisition Data need to be in digital, or numeric, form to
be processed by a computer. In a digital image, colours are represented by numbers. A grid pattern is used to record the colours in the image, each cell being assigned one or more colour numbers.
A digital image
The 'colour' of each grid cell is represented by a number.
digital image
Any picture, photograph or map can be digitised. Automatic scanning devices, which operate in a similar manner to the satellite scanning systems, can be used in a laboratory to convert coloured or black and white maps, pictures or photographs, into digital images for processing by computer.