Radar and Microwave Remote Sensing: How Radar Works

How Radar Works

A typical radar system consists of the following components:

(1) A pulse generator that discharges timed pulses of microwave/radio energy

(2) A transmitter

(3) A duplexer that alternates the signals involved between transmitted and received

(4) A directional antenna that shapes and focuses each pulse into a stream

(5) The same antenna which picks up returned pulses and sends them to a receiver that determines the time delays between their transmission and return, and converts (and amplifies) them into video signals

(6) A recording device which stores them for later processing

(7) An electronic signal processing system that analyzes the signal and prepares it for display

(8) A realtime analog display on a cathode ray tube (CRT), or a TVlike monitor, or on film exposed by a moving light beam to record the image.

Each pulse lasts only microseconds (typically there are about 1,500 pulses per second). Pulse length–an important factor along with bandwidth in setting the system resolution–is the distance traveled during the pulse generation. The duplexer separates the outgoing and returned pulses (i.e., eliminates their mutual interferences) by blocking reception during transmission and vice versa. The antenna on a ground system is generally a parabolic dish; the dish can rotate (sweep), commonly over a 360° range, or is fixed and looking outward.

Radar antennas on aircraft are usually mounted on the underside of the platform so as to direct their beam to the side of the airplane in a direction normal to the flight path. For aircraft, this mode of operation is implied in the acronym SLAR, for Side Looking Airborne Radar. A real aperture radar system (RAR), also know as brute force radar, operates with an antenna that has a discrete physical length. (Aperture is analogous to Field of View.) For a SLAR radar this is a long (about 5-6 m) antenna, usually shaped as a section of a cylinder wall. This type produces a beam of noncoherent pulses and uses its length to obtain the desired resolution (related to angular beamwidth) in the azimuthal (flight line) direction.

The antennas can be large, although weight especially is a limit to the size put on an airplane. The antenna used in the SIR-C radar system was designed to fit into the cargo bay of its host Space Shuttle. This is the antenna before it was mated to the Shuttle:



So, for many operational setups there is an upper limit to the size of the antenna. But the system spatial resolution is proportional to length of the antenna, thus getting an antenna as long as practical is an objective in designing the radar. A "trick" can increase the effective length, and hence resolution, by simulating a real aperture through electronic means for integrating the pulse echos into a composite signal. This is the approach used by Synthetic Aperture Radar (SAR), which is always associated with moving platforms carrying the system. SAR utilizes both recording and processing techniques to generate a signal that acts as though it has an "apparent" length greater than the antenna itself. At any instant the transmitted beam propagates outward within a fan-shaped plane, perpendicular to the flight line; this amounts to a "broad beam" through which targets components advance. The movement of the antenna, be it on aircraft or spacecraft, is involved in increasing the effective length simulating a real aperture by integrating the pulse echos into a composite signal. Mathematical manipulation, including Fourier Analysis, is needed to handle the pulse stream associated with the forward motion. More information on SAR is found at the Wikipedia website on SAR.
SIR-A, B, C, Radarsat, ERS-1,2, JERS-1, and Envisat are the main examples of space satellites that use SAR.

Another type of radar system is exclusive to conditions in which there is relative motion between platform and target. This system depends on the Doppler effect (apparent frequency shift due to the target’s or the radar-vehicle’s velocity) to determine azimuthal resolution. As most know, when a target is moving towards the observer, and emits a signal such as a sound, the frequency continues to rise as the target moves closer - the sound increases in pitch; movement away lowers the frequency. Doppler radar is used in the so-called Radar Guns used by the police to gauge auto speeds based on the rate of frequency shift; in this case the radar beam is continuous rather than pulsed.

For a SLAR system, there is a Doppler component in the returned beam that results in changing frequencies, which give rise to variations in phase and amplitude in the returned pulses. As coherent pulses transmitted from the radar source reflect from the ground to the advancing platform (aircraft or spacecraft), the target acts as if it were in apparent (relative) motion. The system analyzes the moderated pulses and recombines them to synthesize signals. The radar records these data for later processing by optical (using coherent laser light) or digital correlation methods; Fast Fourier Transform analysis is also applied. More about Doppler radar is provided by Wikipedia.
Main practical advantage over SAR over SLAR
Let us now consider the beam characteristics of a typical radar system, as well as the nature and interpretation of the signal returns, as displayed on film or a monitor. The following illustration describes this process


The aircraft moves forward at some altitude above the terrain in an azimuthal direction, while the pulses spread outward in the range (look) direction. Any line-of-sight from the radar to some ground point within the terrain strip defines the slant range to that point. The distance between the aircraft nadir (directly below) line and any ground target point is its ground range. The ground point closest to the aircraft flight trace, at which sensing begins, is the near range limit; at this distance the pulsed signal has the shortest roundtrip transit time. The pulsed ground point at the greatest distance normal to the flight path fixes the far range; the delay between the instant of pulse release and its return is longest at the far range.

At the radar antenna, the angle between a horizontal plane (essentially, parallel to a level surface) and a given slant range direction is called the depression angle β (Beta) for any point along that directional line (a mnemonic for that is "lowering your head down from staring forward when depressed"). We refer to the complementary angle (measured from a vertical plane) as the look angle (a good mnemonic is to think of looking up from staring at your feet [vertically downward]). The incidence angle at any point within the range is the angle between the radar beam direction (of look) and a line perpendicular (normal) to the surface, which can be inclined at any angle (which varies with slope orientation in non-flat topography). The depression angle decreases outward from near to far range. Pulse travel times increase outward between these limits. The duration of a single pulse determines the resolution at a given slant range. This range resolution is effectively the minimum distance between two reflecting points along the azimuthal direction that the radar can identify as separate, at that range. Range resolution gets poorer outward for a specific pulse duration. Thus the resolution increases (gets better) with increasing depression angles (it’s optimum, close-in).

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Persian rug sells for record $4.45 million at auction

PROVIDENCE, R.I. - A silk Persian rug dating from the 16th century or 17th century has sold for a record $4.45 million at auction, or about $729.87 per square inch.

The rug was sold by Christie's auction house Tuesday on behalf of the Newport Restoration Foundation. It had been expected to fetch up to $1.5 million.

The rug, which measures 7 feet, 7 inches by 5 feet, 7 inches, had been purchased by the late tobacco heiress Doris Duke in 1990. She left it to the foundation when she died.

Elisabeth Parker, head of Christie's rugs and carpets department, says there are only two other known rugs like it. She calls it an "amazing work of art" and says it has an intricate floral design and an unusually large number of colors, at 17.

Christie's says the buyer prefers to remain anonymous.

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Remote Sensing:General Principles For Recognizing Vegetation

Vegetation can be distinguished using remote sensing data from most other (mainly inorganic) materials by virtue of its notable absorption in the red and blue segments of the visible spectrum, its higher green reflectance and, especially, its very strong reflectance in the near-IR. Different types of vegetation show often distinctive variability from one another owing to such parameters as leaf shape and size, overall plant shape, water content, and associated background, e.g., soil types and spacing of the plants (density of vegetative cover within the scene). Even marine/lake vegetation can be detected. Use of remote sensing to monitor crops, in terms of their identity, stage of growth, predicted yields (productivity) and health is a major endeavor. This is an excellent example of the value of multitemporal observations, involvig several looks during the growing season, allows better crop type determination and estimates of output. Vegetation distribution and characteristics in forests and grasslands also are readily determinable.

Planet Earth is distinguished from other Solar System planets by two major categories: Oceans and Land Vegetation. The amount of vegetation within the seas is huge and important in the food chain. But for people the land provides most of the vegetation within the human diet. The primary categories of land vegetation (biomes) and their proportions is shown in this pie chart:



Global maps of vegetation biomes show this general distribution:



Remote sensing has proven a powerful "tool" for assessing the identity, characteristics, and growth potential of most kinds of vegetative matter at several levels (from biomes to individual plants). Vegetation behavior depends on the nature of the vegetation itself, its interactions with solar radiation and other climate factors, and the availability of chemical nutrients and water within the host medium (usually soil, or water in marine environments). A common measure of the status of a given plant, such as a crop used for human consumption, is its potential productivity (one such parameter has units of bushels/acre or tons/hectare, or similar units). Productivity is sensitive to amounts of incoming solar radiation and precipitation (both influence the regional climate), soil chemistry, water retention factors, and plant type. Examine the diagram below to see how these interact, keeping in mind that various remote sensing systems (e.g., meteorological or earth-observing satellites) can provide inputs to productivity estimation:



Because many remote sensing devices operate in the green, red, and near infrared regions of the electromagnetic spectrum, they can discriminate radiation absorption and reflectance properties of vegetation. One special characteristic of vegetation is that leaves, a common manifestation, are partly transparent allowing some of the radiation to pass through (often reaching the ground, which reflects its own signature). The general behavior of incoming and outgoing radiation that acts on a leaf is shown here:



Now, consider this diagram which traces the influence of green leafy material on incoming and reflected radiation.



Absorption centered at about 0.65 µm (visible red) is controlled by chlorophyll pigment in green-leaf chloroplasts that reside in the outer or Palisade leaf. Absorption occurs to a similar extent in the blue. With these colors thus removed from white light, the predominant but diminished reflectance of visible wavelengths is concentrated in the green. Thus, most vegetation has a green-leafy color. There is also strong reflectance between 0.7 and 1.0 µm (near IR) in the spongy mesophyll cells located in the interior or back of a leaf, within which light reflects mainly at cell wall/air space interfaces, much of which emerges as strong reflection rays. The intensity of this reflectance is commonly greater (higher percentage) than from most inorganic materials, so vegetation appears bright in the near-IR wavelengths (which, fortunately, is beyond the response of mammalian eyes). These properties of vegetation account for their tonal signatures on multispectral images: darker tones in the blue and, especially red, bands, somewhat lighter in the green band, and notably light in the near-IR bands (maximum in Landsat's Multispectral Scanner Bands 6 and 7 and Thematic Mapper Band 4 and SPOT's Band 3).

Identifying vegetation in remote-sensing images depends on several plant characteristics. For instance, in general, deciduous leaves tend to be more reflective than evergreen needles. Thus, in infrared color composites, the red colors associated with those bands in the 0.7 - 1.1 µm interval are normally richer in hue and brighter from tree leaves than from pine needles.

These spectral variations facilitate fairly precise detecting, identifying and monitoring of vegetation on land surfaces and, in some instances, within the oceans and other water bodies. Thus, we can continually assess changes in forests, grasslands and range, shrublands, crops and orchards, and marine plankton, often at quantitative levels. Because vegetation is the dominant component in most ecosystems, we can use remote sensing from air and space to routinely gather valuable information helpful in characterizing and managing of these organic systems.

The ability to distinguish different types of vegetation was brought home to the writer (NMS) through a simple study using a densitometer to examine multispectral images of a strip of agricultural land near the Choptank River in the eastern shore of Maryland. These images were part of an experiment by my "boss", Dr. Warren Hovis, at Goddard. He had built a multispectral sensor to fly on an aircraft that would simulate images made by the same four bands on the ERTS-1 (Landsat-1) Multispectral Scanner (MSS). Here are the images:



The relative gray levels are plotted as a four band histogram for each of the numbered features in the above image. It should be evident that there are real differences in these band signatures among the vegetation and other features present; thus Mixed Hardwoods have different relative "brightness" patterns from Soybeans, from Old Hay, etc..



This discrimination capability implies that one of the most successful applications of multispectral space imagery is monitoring the state of the world's agricultural production. This application includes identifying and differentiating most of the major crop types: wheat, barley, millet, oats, corn, soybeans, rice, and others.

This capability was convincingly demonstrated by an early ERTS-1 classification of several crop types being grown in Holt County, Nebraska. This pair of image subsets, obtained just weeks after launch, indicates what crops were successfully differentiated; the lower image shows the improvement in distinguishing these types by using data from two different dates of image acquisition:



This is a good point in the discussion to introduce the appearance of large area croplands as they are seen in Landsat images. We illustrate with imagery that covers the two major crop growing areas of the United States.

The first scene is part of the Great or Central Valley of California, specifically the San Joaquin Valley. Agricultural here is primarily associated with such cash crops as barley, alfalfa, sugar beets, beans, tomatoes, cotton, grapes, and peach and walnut trees. In July of 1972 most of these fields are nearing full growth. Irrigation from the Sierra Nevada, whose foothills are in the upper right, compensates for the sparsity or rain in summer months (temperatures can be near 100° F). The eastern Coast Ranges appear at the lower left. The yellow-brown and blue areas flanking the Valley crops are grasslands and chapparal best suited for cattle grazing. The blue areas within the croplands (near the top) are the cities of Stockton and Modesto.



The second Landsat image is in the Wheat Belt of the Great Plains. The image below is of western Kansas in late August. Most of the scene consists of small farms, many of section size (1 square mile). The principal crop is winter wheat which is normally harvested by June. Spring wheat is then planted, along with sorghum, barley, and alfalfa. This scene is transitional, with nearly all of the right side being heavily planted, but the left side (the High Plains, at higher elevations) contains some unplanted farms and cropfree land, some used for grazing.




Still another example of winter wheat in early growth is this scene in southwestern Australia, east of Perth. Some of the wheat fields are quite large - 5 km (3 miles) or more on a side. The prevailing color is tan but with a faint red cast, implying initial growth. There is a sharp line dividing many fields from the mallee scrub (dark brown) growing on soils derived from Precambrian rocks. This line marks an electrified rabbit fence, keeping these "pests" from nibbling on the wheat and other crops being grown.



Many factors combine to cause small to large differences in spectral signatures for the varieties of crops cultivated by man. Generally, we must determine the signature for each crop in a region from representative samples at specific times. However, some crop types have quite similar spectral responses at equivalent growth stages. The differences between crop (plant) types can be fairly small in the Near-Infrared, as shown in these spectral signatures (in which other variables such as soil type, ground moisture, etc. are in effect held constant). In this illustration, the curves have been offset to make it easier to see each plot; if plotted to the same actual values, they would almost superimpose.

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Water Harvesting Systems in India

Tankas
Tankas (small tank) are underground tanks, found traditionally in most Bikaner houses. They are built in the main house or in the courtyard. They were circular holes made in the ground, lined with fine polished lime, in which raiwater was collected. Tankas were often beautifully decorated with tiles, which helped to keep the water cool. The water was used only for drinking. If in any year there was less than normal rainfall and the tankas did not get filled, water from nearby wells and tanks would be obtained to fill the household tankas. In this way, the people of Bikaner were able to meet their water requirements. The tanka system is also to be found in the pilgrim town of Dwarka where it has been in existence for centuries. It continues to be used in residential areas, temples, dharamshalas and hotels.


Khadin
A khadin, also called a dhora, is an ingenious construction designed to harvest surface runoff water for agriculture. Its main feature is a very long (100-300 m) earthen embankment built across the lower hill slopes lying below gravelly uplands. Sluices and spillways allow excess water to drain off. The khadin system is based on the principle of harvesting rainwater on farmland and subsequent use of this water-saturated land for crop production.



First designed by the Paliwal Brahmins of Jaisalmer, western Rajasthan in the 15th century, this system has great similarity with the irrigation methods of the people of Ur (present Iraq) around 4500 BC and later of the Nabateans in the Middle East. A similar system is also reported to have been practised 4,000 years ago in the Negev desert, and in southwestern Colorado 500 years ago.







Vav / vavdi / Baoli / Bavadi

Traditional stepwells are called vav or vavadi in Gujarat, or baolis or bavadis in Rajasthan and northern India. Built by the nobility usually for strategic and/or philanthropical reasons, they were secular structures from which everyone could draw water. Most of them are defunct today.

The construction of stepwells date from four periods: Pre-Solanki period (8th to 11th century CE); Solanki period (11th to 12th century CE); Vaghela period (mid-13th to end-14th century CE); and the Sultanate period (mid-13th to end-15th century CE).
Sculptures and inscriptions in stepwells demonstrate their importance to the traditional social and cultural lives of people.

Stepwell locations often suggested the way in which they would be used. When a stepwell was located within or at the edge of a village, it was mainly used for utilitarian purposes and as a cool place for social gatherings. When stepwells were located outside the village, on trade routes, they were often frequented as resting places. Many important stepwells are located on the major military and trade routes from Patan in the north to the sea coast of Saurashtra. When stepwells were used exclusively for irrigation, a sluice was constructed at the rim to receive the lifted water and lead it to a trough or pond, from where it ran through a drainage system and was channelled into the fields.

A major reason for the breakdown of this traditional system is the pressure of centralisation and agricultural intensification.


Source:Making Water Everybody's Business

Ahar Pynes
This traditional floodwater harvesting system is indigenous to south Bihar.
In south Bihar, the terrain has a marked slope -- 1 m per km -- from south to north. The soil here is sandy and does not retain water. Groundwater levels are low. Rivers in this region swell only during the monsoon, but the water is swiftly carried away or percolates down into the sand. All these factors make floodwater harvesting the best option here, to which this system is admirably suited.

An ahar is a catchment basin embanked on three sides, the 'fourth' side being the natural gradient of the land itself. Ahar beds were also used to grow a rabi (winter) crop after draining out the excess water that remained after kharif (summer) cultivation.
Pynes are articifial channels constructed to utilise river water in agricultural fields. Starting out from the river, pynes meander through fields to end up in an ahar. Most pynes flow within 10 km of a river and their length is not more than 20 km.

The ahar-pyne system received a death-blow under the nineteenth-century British colonial regime. The post-independent state was hardly better. In 1949, a Flood Advisory Committee investigating continuous floods in Bihar's Gaya district came to the conclusion that "the fundamental reason for recurrence of floods was the destruction of the old irrigational system in the district."

Of late, though, some villages in Bihar have taken up the initiative to re-build and re-use the system. One such village is Dihra.
It is a small village 28 km southwest of Patna city. In 1995, some village youths realised that they could impound the waters of the Pachuhuan (a seasonal stream passing through the village that falls into the nearby river Punpun) and use its bed as a reservoir to meet the village's irrigation needs. Essentially, this meant creating an ahar-pyne system
After many doubts, the village powers-that-be gave the go-ahead. Money was collected and work began in May 1995. After a month of shramdaan (voluntary labour) the villagers completed their work mid-June.
Their efforts have borne fruit. By 2000 AD, the ahar was irrigating 80 ha of land. The people grow two cereal crops and one crop of vegetables every year. The returns from the sale of what they produce are good. The village is no longer a poor one.



Bengal's Inundation Channel
Bengal once had an extraordinary system of inundation canals. Sir William Willcocks, a British irrigation expert who had also worked in Egypt and Iraq, claimed that inundation canals were in vogue in the region till about two centuries ago. Floodwater entered the fields through the inundation canals, carrying not only rich silt but also fish, which swam through these canals into the lakes and tanks to feed on the larva of mosquitoes. This helped to check malaria in this region. According to Willcocks, the ancient system of overflow irrigation had lasted for thousands of years. Unfortunately, during the Afghan-Maratha war in the 18th century and the subsequent British conquest of India, this irrigation system was neglected, and was never revived.

According to Willcocks, the distinguishing features of the irrigation system were:

1.) the canals were broad and shallow, carrying the crest waters of the river floods, rich in fine clay and free from coarse sand;

2.) the canals were long and continuous and fairly parallel to each other, and at the right distance from each other for purposes of irrigation;

3.) irrigation was performed by cuts in the banks of the canals, which were closed when the flood was over.
Dungs or Jampois
Dungs or Jampois are small irrigation channels linking rice fields to streams in the Jalpaiguri district of West Bengal.


Cheruvu
Cheruvu are found in Chitoor and Cuddapah districts in Andhra Pradesh. They are reservoirs to store runoff. Cheruvu embankments are fitted with thoomu (sluices), alugu or marva or kalju (flood weir) and kalava (canal).




Kohli Tanks
The Kohlis, a small group of cultivators, built some 43,381 water tanks in the district of Bhandara, Maharashtra, some 250-300 years ago. These tanks constituted the backbone of irrigation in the area until the government took them over in the 1950s. It is still crucial for sugar and rice irrigation. The tanks were of all sizes, often with provisions to bring water literally to the doorstep of villagers.



Bhanadaras
These are check dams or diversion weirs built across rivers. A traditional system found in Maharashtra, their presence raises the water level of the rivers so that it begins to flow into channels. They are also used to impound water and form a large reservoir.
Where a bandhara was built across a small stream, the water supply would usually last for a few months after the rains.
They are built either by villagers or by private persons who received rent-free land in return for their public act
Most Bandharas are defunct today. A very few are still in use.



Phad
The community-managed phad irrigation system, prevalent in northwestern Maharashtra, probably came into existence some 300-400 years ago. The system operated on three rivers in the Tapi basin - Panjhra, Mosam and Aram - in Dhule and Nasik districts (still in use in some places here).


The system starts with a bandhara (check dam or diversion-weir) built across a rivers. From the bandharas branch out kalvas (canals) to carry water into the fields. The length of these canals varies from 2-12 km. Each canal has a uniform discharge capacity of about 450 litres/second. Charis (distributaries) are built for feeding water from the kalva to different areas of the phad. Sarangs (field channels) carry water to individual fields. Sandams (escapes), along with kalvas and charis, drain away excess water. In this way water reaches the kayam baghayat (agricultural command area), usually divided into four phads (blocks).



The size of a phad can vary from 10-200 ha, the average being 100-125 ha. Every year, the village decides which phads to use and which to leave fallow. Only one type of crop is allowed in one phad. Generally, sugarcane is grown in one or two phads; seasonal crops are grown in the others. This ensures a healthy crop rotation system that maintains soil fertility, and reduces the danger of waterlogging and salinity.

The phad system has given rise to a unique social system to manage water use.




Kere
Tanks, called kere in Kannada, were the predominant traditional method of irrigation in the Central Karnataka Plateau, and were fed either by channels branching off from anicuts (chech dams) built across streams, or by streams in valleys. The outflow of one tank supplied the next all the way down the course of the stream; the tanks were built in a series, usually situated a few kilometres apart. This ensured a) no wastage through overflow, and b) the seepage of a tank higher up in the series would be collected in the next lower one.





The Ramtek model has been named after water harvesting structures in the town of Ramtek, Maharashtra. A scientific analysis revealed an intricate network of groundwater and surface waterbodies, intrinsically connected through surface and underground canals. A fully evolved system, this model harvested runoff through tanks, supported by high yielding wells and structures like baories, kundis, and waterholes. This system, intelligently designed to utlise every raindrop falling in the watershed area is disintegrating due to neglect and ignorance.

Constructed and maintained mostly by malguzars (landowners), these tanks form a chain, extending from the foothills to the plains, conserving about 60-70 per cent of the total runoff. Once tanks located in the upper reaches close to the hills were filled to capacity, the water flowed down to fill successive tanks, generally through interconnecting channels. This sequential arrangement generally ended in a small waterhole to store whatever water remained unstored.

The presence of the Ramtek ridge in the middle, having a steep slope on both sides, results in quick runoffs and little percolation. This might have led the residents of the southern plains of the Ramtek hills to construct different types of water conservation structures (like tanks) where they could trap the maximum





Zings
Zings are water harvesting structures found in Ladakh. They are small tanks, in which collects melted glacier water.
Essential to the system is the network of guiding channels that brings the water from the glacier to the tank. As glaciers melt during the day, the channels fill up with a trickle that in the afternoon turns into flowing water. The water collects towards the evening, and is used the next day.
Souce: http://www.rainwaterharvesting.org/Rural/Traditional2.htm#zing

Electromagnetic Spectrum: Spectral Signatures

For any given material, the amount of solar radiation that it reflects, absorbs, transmits, or emits varies with wavelength. When that amount (usually intensity, as a percent of maximum) coming from the material is plotted over a range of wavelengths, the connected points produce a curve called the material's spectral signature (spectral response curve). Here is a general example of a reflectance plot for some (unspecified) vegetation type (bio-organic material), with the dominating factor influencing each interval of the curve so indicated:



This important property of matter makes it possible to identify different substances or classes and to separate them by their individual spectral signatures, as shown in the figure below.



For example, at some wavelengths, sand reflects more energy than green vegetation but at other wavelengths it absorbs more (reflects less) than does the vegetation. In principle, we can recognize various kinds of surface materials and distinguish them from each other by these differences in reflectance. Of course, there must be some suitable method for measuring these differences as a function of wavelength and intensity (as a fraction [normally in percent] of the amount of irradiating radiation). Using reflectance differences, we may be able to distinguish the four common surface materials in the above signatures (GL = grasslands; PW = pinewoods; RS = red sand; SW = silty water) simply by plotting the reflectances of each material at two wavelengths, commonly a few tens (or more) of micrometers apart. Note the positions of points for each plot as a reflectance percentage for just two wavelengths:


source:

History of Remote Sensing: Color & False Color Composites

The process by which natural and false color composites made from three multispectral intervals or bands by projection and superposition is discussed. One important conclusion is that when a spectral band which contains reflectances in the wavelength interval between 0.7 - 1.1 µm is projected through a red filter, healthy, active vegetation will appear bright (light tones) in a black and white image or red in a false color image.


We can use color-filtered b & w pictures as transparencies projected through color filters and superposed (registered) to produce color composites. To do this, imagine this setup: Pick any scene containing many features and classes of differing colors. First, replace the prints with positive transparencies (tonally analogous to prints). Work with three b & w transparencies, each representing its spectral band. Shine white light through each one mounted in its own lamp projector (total of three) on to a screen. Project the blue band b & w transparency through a blue filter, the green through green, and the red through red. Blue features on the ground are clear areas in the blue spectral band. When the blue band transparency is projected through the blue filter, the blue features will be blue on an observing screen, likewise the green band projects green objects through its filter as green, and red as red. Co-register (line up) the three projections by superimposing several distinctive patterns that are common within the photographed scene. The result will be a simulated natural color image. with any red, green, and blue objects or classes showing in the projected image as these colors respectively. Other colors present are additive mixes of two or more primaries (e.g., yellow is a mix of red and green; orange is a mix of more red and some green; white is an equal mix of all three primaries, and black is simply the absence of any colored light of any wavelength). The colors that result from combinations of blue, green, and red (the primaries) are indicated in this additive color diagram.



This way of combining individual colors to produce a composite is called color additive viewing. The filters and films used do not necessarily have to correspond by wavelength. For example, a green spectral band image can be projected through a blue filter and a red band through a green filter. We can modify the color assignments in making an image if one of the transparencies is the infrared band and is projected through a red filter; the result is one version of a what is called a false color (IR) composite. This procedure and the resulting colors that are produced are indicated in this diagram:


The setup is a variant of the one described in the first paragraph in which a true color image is formed. In the above figure, each of the four geometric designs in its large framing square is either clear (shown as white inside its boundaries) and will pass all light impinging on it or is shaded gray inside which means no light passes through. Each is located in the same position in all three squares. The three squares are now mounted each in its own light projector (for instance a 35 mm or a lantern slide type). Light passing through a clear design is projected on a screen surface; each projector is moved until designs with equivalent shapes are registered (superimposed). A color filter is placed in front of the lens of each projector: blue for square A, green for B, and red for C. As seen on the screen, the diamond will show as blue, the inverted triangle as green, and the + as red. The combination of green and red for the circle design (in B and C) produces yellow (in the color additive process).

To relate this to how it applies to multispectral photography, consider square A as representing a scene in which only green light is reflected from objects (not too fanciful - the Emerald City in the "Wizard of Oz" was all green). Square B represents a reflected red light only scene; square C represents a scene containing only objects that give off only infrared light. (As we shall see, the Landsat MSS band equivalents would be A = MSS Band 4 (ground imaged by a green filter, so that only green objects would be bright [clear in A]), B = Band 5 (red filter), and C = MSS Band 6 or 7 (imaged by an IR filter). The screen receiving the projected light would show a blue object, a green object, a red object, and a yellow object, corresponding to real world objects imaged by green, blue, and infrared filters; the projected objects would be separated in the superposition, corresponding to their initial separations in the square.

 Let's see what a false color image looks like in the ground scene that follows. First we show the area, grassland and a field with natural shrub cover (left), as it appears in natural color in this aerial oblique view:



The companion photo below is a notably different color version (typical false color rendition) in which various kinds of vegetation display in several tones of red, pink, or yellow (the latter two may indicate a degree of stressed or unhealthy vegetation).



This type of projected color combination, yielding the false color IR composite, is ordinarily used to emphasize a property of healthy vegetation in which incident light in the range of 0.7 - 1.1 µm reflects strongly from the internal cells of plants, giving rise to bright tones in the color IR film.

I-17: What kinds or types of materials/classes are associated with the white, the blue, the dark red, and the light red features noted in this photo. Account for the color. ANSWER

When this film combination is used in military reconnaissance photography, weapons camouflage - simulating vegetation - is not red in a false-color scene, because the high near-IR reflectance from healthy vegetation is absent (synthetic fabric in clothing is not bright in the near-IR; if made usually from cotton or other organic, this too is at best a subdued bright). Or, in a more familiar mode, color IR photos (and sensor-derived images that include an IR band) show a football field with natural grass in bright red as compared with artificial turf (Astroturf) in a dark, non-red tone.

Lets re-examine the square frames example above by conducting another experiment. Instead of the clear areas passing light, let them reflect light back towards a film camera. The gray areas reflect no light (ignore the areas outside the designs). The objective of this experiment is to determine what gray level wil be recorded on visible or infrared-sensitive film when the clear designs are (1) assigned a color (including IR), and then (2) viewed through some highly sensitive filter (one that passes onlylight of its stated color).

So, going back to the diagram, if we look at the reflecting diamond design in A through a green filter (the Landsat MSS case), we ask what color(s) it can have that will pass through the filter, be recorded as very dark on a film negative, and then expressed as a white (or light gray, since the filter may also pass some adjacent wavelengths) diamond shape on a positive black and white print. The obvious answer is green. If the objects in A are any color other than green, the filter would not pass these wavelengths and the diamond would be very dark in the print. The same argument holds for B and C. C, for example, has two designs that reflect the light. When that light is passed by an infrared filter, if light tones for these designs in a b & w photo-print are the result, this would mean that both designs are brightly reflected in the IR and not in the visible range. Going back to the square A case: if the diamond were white in actual fact, not green, and a green filter were used, then only the green component of the visible spectrum would pass and the resulting print would be a low to moderate gray. (The fact that it is white would emerge from the lightness of tone in each square print [provided one was made with a blue filter].) If the diamond were black, no light would pass and the print would be dark.


Primary Author: Nicholas M. Short, Sr.

source

Malaysia takes steps to claim 100 isles, reefs

 
Malaysia - Government agencies are working feverishly to gather information on islands and marine features which could be eyed by neighbouring countries.

This comes in the wake of the International Court of Justice's decision to award Pedra Branca to Singapore.

The court, however, handed ownership of Middle Rocks to Malaysia, while the status of South Ledge will be decided based on whose territorial waters it is in.

As the claim of ownership extends beyond islands to marine features such as reefs and even rock formations visible only during low tides, the number could exceed thousands.

A maritime expert, however, said the most urgent were about 100 islands, reefs, rocks and other marine features lying in the South China Sea, Straits of Malacca and off Sabah.

Among the claimants are China, Vietnam and Indonesia, a source said.

He said agencies were collecting data on these islands and marine features.

He added that the data collection was crucial. "If other countries start to claim our property, we will be in better position as we would have the data."
He said some of the islands were significant while others were not.

It is learnt that the National Remote Sensing Centre had also started zooming in on some of the islands and marine features at the southern coast of the peninsula to gather information on them."We will expedite the data gathering," the source said.

Using satellites, the Remote Sensing agency will also collect data from Malaysia's other coastal areas.

Foreign Ministry officials are also engaged in discussions to look into the implications of the ICJ's decision on Pedra Branca.

These include the right of passage of Malaysian fishermen going to the waters around Middle Rocks and South Ledge.

As of now, fishermen have been advised to stay away until the government studies the implications of the decision. Top officials from the Fisheries Department have also been in meetings with officials from other agencies to discuss the implications, especially the impact on fishermen. They are looking at ways that would benefit fishermen.

However, Fish Merchant Association president Goi Hong Kee said his members were not affected by the decision as most fishermen did not go near Pedra Branca.

"Those who go there usually fish illegally and sell it to Singapore," he said.

However, he said the association welcomed guidelines from the authorities.

Source : http://www.nst.com.my/Current_News/

GIS and Everyday Life

THERE'S a silent revolution going on in the midst of much gloom in the field of Information Technology. It's a revolution that touches our lives in myriad ways- from the rates we pay for utility services to the quality of our roadways. It can help prevent famine, blight, and pestilence. It can influence the speed with which emergency vehicles react to our calls and how quickly criminals are put behind bars. It has played an influential role in the scheduling and fighting of wars and then rebuilding war-torn communities. It is being used for applications as far flung as finding offending taxpayers, developing pizza delivery routes, and setting up insurance rates. The basis for this silent revolution is a technology called Geographic Information Systems - or GIS.

GIS is a system of hardware, software and procedures designed for support, capture, management, manipulation, analysis, modeling and display of spatially referenced data, for solving complex planning and management problems.

Application areas

The application of GIS is vast. A person with the following background is an ideal candidate for learning and working in the GIS field.

*Natural resource management

*Environmental planning

*Local & municipal authorities

*Emergency management

*Military commands

*Transportation management

*Socio-Economic development

*Urban development

*Public utility works

*Business applications

Students of civil engineering, geography, geology, sociology, soil science, surveying, urban planning, and remote sensing can opt for this field. GIS facilitates students by providing better career opportunities in these fields and makes them acquainted with both conventional and digital environment subjects. Further, the increasing demand for latest GIS technology like Mobile Mapping, Internet GIS, Application Development, high-end GIS database structure and Virtual GIS is attracting students from electronic & communication, electrical, mechanical and computer engineering background to enter this field.

Study centres and qualification

The Institute of Remote Sensing at Anna University, Chennai, has a four-year engineering degree program (B.E) in Geo-informatics apart from masters programs in Remote Sensing and M.E in Geo- informatics. Students who have secured a higher second class in 10+2 can appear for the common entrance exam to be admitted into the undergraduate courses. Many institutes in India offer a postgraduate diploma in the subject and also in remote sensing.

The Indian Institute of Remote Sensing (IIRS), Dehradun has trained more than 4200 professionals in the field of remote sensing and GIS. The Centre for Spatial Information Technology in JNTU, Hyderabad is another professional body involved in GIS education. Many colleges like IIT Mumbai, IIT Kanpur, Roorkee University, REC Warangal offer GIS at the Masters level. Selection for these programmes is through a common entrance test and almost all institutes permit students from multi-disciplinary subjects for their Masters level programme.

However, some of the institutes take only students with an Electronic and Computer Science background, Geo Science and B.A/B.Sc in geography.

Apart from the regular courses, many institutes like the Indian Space Research organisation (ISRO), National Remote Sensing Agency (NRSA), Space Application Centre, Ahmedabad, Survey Training Institute, Hyderabad, Geological Survey of India conduct short term and long-term courses and workshops at nominal fees.

Software available

Many GIS software packages are available in the market and of course the nature of how GIS works in a particular organisation is software dependent.

The most commonly used and high demand GIS packages are ArcInfo, ArcView, AutoCAD Map, MapInfo, Geomedia, CARIS GIS, SICAD and others.

Job opportunities

Many public and private companies employ GIS graduates at various levels. Public sector organisations like ISRO, NRSA, National Informatics Centre (NIC) and other governmental departments like urban developmental authorities and municipal bodies employ GIS trained personnel based on their requirements.


However, the percentage of jobs in private companies is higher than in the government departments.

One can also pursue research in the field as a number of research companies use the technology in different areas.

The different GIS

job levels are:

GIS digitisers/GIS operators

This is an entry-level job where the conversion of paper map into digital environment takes place using CAD related software. The minimum eligibility criterion is a diploma in any discipline with basic knowledge of CAD/GIS. Prior experience is not required in many cases.

GIS technicians/GIS engineers

The candidate should have a fair amount of working knowledge in any GIS software and good mapping knowledge with 1-2 years of experience.

GIS analysts

These professionals should have a basic knowledge of mapping and good skills in GIS applications and software with a few years of experience. Their duty is to examine the company's project specifications and development of new GIS tools, wherever necessary. A fair share of GIS programming skills is an advantage.

Business development managers

The candidate should have a minimum of 2-5 years of experience with good knowledge about GIS business activities. He should have worked in domestic and international market with proven abilities and an excellent track record.

GIS project manager

The candidate should have a minimum of 5 years of experience in GIS project management, database design and development, GIS application development and familiarity with current versions of GIS products. He should have a good GIS background and high qualifications.

Consultant

A consultant's primary duties are the complete analysis, design and documentation of a project. He has to work closely with business development/sales and project managers in drafting responses to RFP/tenders and preparing project proposals.


Future prospects

The scene in India for GIS is bright at present and also augurs well for the future. As per a latest report, the growth of GIS market in India was Rs 79 crores in the year 1999-2000. The exports from India are expected to reach US$150 million by year 2005. Also the increasing job opportunities in public and private sectors gives hope for the development of GIS usage in the country.

source:http://www.hinduonnet.com/thehindu/jobs/0201/05090021.htm