Digital land records to aid farmers of UP, India

LUCKNOW, India: In an effort to prevent land disputes among farmers and to benefit them through welfare schemes, the Uttar Pradesh government has roped in the services of Remote Sensing Application Centre (RSAC) to prepare land maps and digitise records with the help of GIS.

"The government has decided to digitise existing land records and maps. For this it has roped in services of RSAC, which will use Geographical Information System to take satellite images to know the situation," a senior planning department official said. RSAC Director A N Singh said the centre would prepare satellite images of a particular area with the help of GIS technique and compare it with the existing land records. These will then be uploaded in the computer with the help of overlay method to know the exact geographical situation and land use. 

The planning department official said a pilot project in this regard has been initiated by the state government and RSAC in backward Jalaun district in Bundelkhand region after which it would be implemented across the state. The project was initiated after it was observed that due to lack of records in digital form, farmers had to face a lot of hardship in obtaining duplicate papers and maps of their land when the papers were destroyed or decayed, he said. 


Source : http://timesofindia.indiatimes.com

Remote Sensing-Electromagnetic Spectrum: Distribution of Radiant Energies

Electromagnetic radiation (EMR) extends over a wide range of energies and wavelengths (frequencies). A narrow range of EMR extending from 0.4 to 0.7 µm, the interval detected by the human eye, is known as the visible region (also referred to as light but physicists often use that term to include radiation beyond the visible). White light contains a mix of all wavelengths in the visible region. It was Sir Isaac Newton who first in 1666 carried out an experiment that showed visible light to be a continuous sequence of wavelengths that represented the different color the eye can see. He passed white light through a glass prism and got this result:

The principle supporting this result is that as radiation passes from one medium to another, it is bent according to a number called the index of refraction. This index is dependent on wavelength, so that the angle of bending varies systematically from red (longer wavelength; lower frequency) to blue (shorter wavelength; higher frequency). The process of separating the constituent colors in white light is known as dispersion. These phenomena also apply to radiation of wavelengths outside the visible (e.g., a crystal's atomic lattice serves as a diffraction device that bends x-rays in different directions).

The distribution of the continuum of all radiant energies can be plotted either as a function of wavelength or of frequency in a chart known as the electromagnetic (EM) spectrum. Using spectroscopes and other radiation detection instruments, over the years scientists have arbitrarily divided the EM spectrum into regions or intervals and applied descriptive names to them.The EM spectrum, plotted here in terms of wavelengths, is shown here.

Beneath is a composite illustration taken from the Landsat Tutorial Workbook (credited there to Lintz and Simonett, Remote Sensing of the Environment, who identify it as a modification of an earlier diagram by Robt. Colwell) that shows in its upper diagram the named spectral regions in terms of wavelength and frequency and in the lower diagram the physical phenomena that give rise to these radiation types and the instruments (sensors) used to detect the radiation. (Although the width of this second diagram scales closely to the width of the spectrum chart above it, the writer experienced difficulty in centering this second diagram on the present page; it needs some leftward offset so that the narrow pair of vertical lines coincides with the visible range in the upper diagram.)

Although it is somewhat redundant, we reproduce here still another plot of the EM Spectrum, with added items that are self-explanatory:

Colors in visible light are familiar to most, but the wavelength limits for each major color are probably not known to most readers. Here is a diagram that specifies these limits (the purple on the far left is in the non-visible ultraviolet; the deep red on the far right is the beginning of the infrared). The human eye is said to be able to distinguish thousands of slightly different colors (one estimate placed this at distinguishable 20000 color tints).

Different names for (wave)length units within intervals (those specified by types) that subdivide the EM spectrum, and based on the metric system, have been adopted by physicists as shown in this table:

(Both in this Tutorial and in other texts, just which units are chosen can be somewhat arbitrary, i.e., the authors may elect to use micrometers or nanometers for a spectral location in the visible. Thus, as an example, 5000 Angstroms, 500 nanometers, and 0.5 micrometers all refer to the same specific wavelength; see next paragraph.)

At the very energetic (high frequency and short wavelength) end are gamma rays and x-rays (whose wavelengths are normally measured in angstroms [Å], which in the metric scale are in units of 10^-8 cm). Radiation in the ultraviolet extends from about 300 Å to about 4000 Å. It is convenient to measure the mid-regions of the spectrum in one of two units: micrometers (µm), which are multiples of 10^-6 m or nanometers (nm), based on 10^-9 m. The visible region occupies the range between 0.4 and 0.7 µm, or its equivalents of 4000 to 7000 Å or 400 to 700 NM The infrared region, spanning between 0.7 and 1000 µm (or 1 mm), has four subintervals of special interest: (1) reflected IR (0.7 - 3.0 µm), and (2) its film responsive subset, the photographic IR (0.7 - 0.9 µm); (3) and (4) thermal bands at (3 - 5 µm) and (8 - 14 µm). We measure longer wavelength intervals in units ranging from mm to cm. to meters. The microwave region spreads across 0.1 to 100 cm, which includes all of the interval used by radar systems. These systems generate their own active radiation and direct it towards targets of interest. The lowest frequency-longest wavelength region beyond 100 cm is the realm of radio bands, from VHF (very high frequency) to ELF (extremely low frequency); units applied to this region is often stated as frequencies in units of Hertz (1 Hz = 1 cycle per second; KHz, MHz and GHz are kilo-, mega-, and giga- Hertz respectively). Within any region, a collection of continuous wavelengths can be partioned into discrete intervals called bands.

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Remote Sensing-Electromagnetic Spectrum:Transmittance, Absorptance, and Reflectance

Any beam of photons from some source passing through medium 1 (usually air) that impinges upon an object or target (medium 2) will experience one or more reactions that are summarized in this diagram:

Some objects are capable of transmitting the light through their bulk without significant diminution (note how the beam bends twice at the medium 1/medium 2 interface but emerges at the same angle as entry). Other materials cause the light energy to be absorbed (and in part emitted as longer wavelength radiation). Or, the light can be reflected at the same angle as it formed on approach. Commonly the nature of the object's surface (owing to microscopic roughness) causes it to be scattered in all directions.

The primary source of energy that illuminates natural targets is the Sun. Solar irradiation (also called insolation) arrives at Earth at wavelengths which are determined by the photospheric temperature of the sun (peaking near 5600 °C). The main wavelength interval is between 200 and 3400 nm (0.2 and 3.4 µm), with the maximum power input close to 480 nm (0.48 µm), which is in the visible green region. As solar rays arrive at the Earth, the atmosphere absorbs or backscatters a fraction of them and transmits the remainder.

Upon striking the land and ocean surface (and objects thereon), and atmospheric targets, such as air, moisture, and clouds, the incoming radiation (irradiance) partitions into three modes of energy-interaction response: 

(1) Transmittance (τ) - some fraction (up to 100%) of the radiation penetrates into certain surface materials such as water and if the material is transparent and thin in one dimension, normally passes through, generally with some diminution. 

(2) Absorptance (α) - some radiation is absorbed through electron or molecular reactions within the medium ; a portion of this energy is then re-emitted, usually at longer wavelengths, and some of it remains and heats the target; 

(3) Reflectance (ρ) - some radiation (commonly 100%) reflects (moves away from the target) at specific angles and/or scatters away from the target at various angles, depending on the surface roughness and the angle of incidence of the rays. 

Because they involve ratios (to irradiance), these three parameters are dimensionless numbers (between 0 and 1), but are commonly expressed as percentages. Following the Law of Conservation of Energy: τ + α + ρ = 1. 

A fourth situation, when the emitted radiation results from internal atomic/molecular excitation, usually related to the heat state of a body, is a thermal process. The theory underlying thermal remote sensing is treated in Section 9.

When a remote sensing instrument has a line-of-sight with an object that is reflecting solar energy, then the instrument collects that reflected energy and records the observation. Most remote sensing systems are designed to collect reflected radiation

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CRRI’s 'Hawk Eye' on Indian roads

At first sight, one can mistake this vehicle to be an ultra tech robotic remote-controlled vehicle, but it is the country's first 'Hawk Eye' that is surveying almost 50,000 kilometres of country's highways and roads to gauge their quality and see if they are fit for freight movement and travel. The database will be used to create GIS maps of highways in the country. The project is being undertaken by Central Road Research Institute (CRRI) and GIS survey and global positioning system (GPS) will help better navigation across the country. 

Mounted on a jeep, the Hawk Eye's instrumentation system includes a laser profiler along with pavement view cameras to measure road surface, collect and process digital images of pavements and other roadside elements. The vehicle has been used in countries like China, Malaysia, Singapore, South Korea and Thailand. Hawk Eye requires high-speed paved roads for operation and is sensitive to rough weather conditions like dust storms and heavy rains. It can gather data while travelling at a speed of 30 to 100 km per hour. 

An advantage Hawk Eye enjoys over other instruments with CRRI is its ability to look at the surface of the road and measure cracking areas. Any area with a crack of above three centimetres is detected by the vehicle's sensors and a preventive measure is suggested by the computers. This helps improve life of highways, which is normally around 20 years in the case of bitumen roads.


Source : http://timesofindia.indiatimes.com

UAE poised to get its first dedicated eye in the sky

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Scientists to use satellite imagery to predict disease outbreaks

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GeoEye-1 successfully launched - now see the world better than ever

USA - GeoEye-1 the super-sharp Earth-imaging satellite was launched into orbit on 6th of September from Vandenberg Air Force Base on the Central California coast. A Delta 2 rocket carrying the GeoEye-1 satellite lifted off at 11:50 a.m. Video on the GeoEye Web site showed the satellite separating from the rocket moments later on its way to an eventual polar orbit. 

The satellite makers say GeoEye-1 has the highest resolution of any commercial imaging system. It can collect images from orbit with enough detail to show home plate on a baseball diamond. The company says the satellite's imaging services will be sold for uses that could range from environmental mapping to agriculture and defense. 

GeoEye-1 was lifted into a near-polar orbit by a 12-story-tall United Launch Alliance Delta II 7420-10 configuration launch vehicle. The launch vehicle and associate support services were procured by Boeing Launch Services. The company expects to offer imagery and products to customers in the mid- to late-October timeframe.
The GeoEye-1 Satellite

GeoEye-1, designed and built by General Dynamics Advanced Information Systems, is the world's highest resolution commercial imaging satellite. Designed to take color images of the Earth from 423 miles (681 kilometers) in space and moving at a speed of about four-and-a-half miles (seven kilometers) per second, the satellite will make 15 earth orbits per day and collect imagery with its ITT-built imaging system that can distinguish objects on the Earth's surface as small as 0.41-meters (16 inches) in size in the panchromatic (black and white) mode. The 4,300-pound satellite will also be able to collect multispectral or color imagery at 1.65-meter ground resolution. While the satellite will be able to collect imagery at 0.41-meters, GeoEye's operating license from NOAA requires re-sampling the imagery to half-meter resolution for all customers not explicitly granted a waiver by the U.S. Government.

The satellite will be able to see an object the size of home plate on a baseball diamond but also map the location of an object that size to within about nine feet (three meters) of its true location on the surface of the Earth without need for ground control points. Together, GeoEye's IKONOS and GeoEye-1 satellites can collect almost one million square kilometers of imagery per day.

With the ability to revisit any location on the globe every three days, and at lesser resolution more frequently, GeoEye-1 will enable customers to receive imagery updates on a regular basis and is ideal for large-scale mapping projects. This capability will benefit a broad array of industries including national defense and intelligence, online mapping, state and local governments, environmental monitoring and land use management, oil and gas, utilities, disaster management, insurance and others. 

Source : http://geoeye.mediaroom.com/

Isro,India to launch Italian, Algerian satellites

India - Antrix Corp. Ltd, the commercial arm of India’s space agency, has won a pair of deals from Algeria and Italy to launch earth observation satellites next year on the polar satellite launch vehicle, or PSLV, its workhorse rocket. 

The contract awarded by the Algerian space agency to launch Alsat-2A, a 200kg remote sensing satellite, is the first won by Antrix from an African nation. The Algerian agency has the option to launch a second such satellite. For the Italian space agency Agenzia Spaziale Italiana, Antrix will launch a satellite named IMSAT, which will be the second Italian satellite to be boosted into space by the Indian Space Research Organisation, or ISRO, which in April 2007 launched Agile, a 352kg scientific satellite. 

The Algerian and Italian satellites, besides a 100kg satellite for Nanyang Technological University in Singapore, and Cubesat, a three-satellite package from the Netherlands, would ride piggyback on heavier Indian satellites, said K.R. Sridhara Murthi, managing director of Antrix. He didn’t disclose financial details.

Antrix is also in talks with space agencies of South Africa and Nigeria to carry out similar launches, Murthi said. “We are also looking at opportunities bigger than that—remote sensing satellites, where payloads (are) of 800kg or even higher.” 

ISRO offers the home-grown PSLV to carry satellites of up to 1,700kg into low-earth orbit at a cost that’s nearly 30% cheaper than that charged by firms such as International Launch Services, owned by Space Transport Inc. and two Russian organizations, Khrunichev State Research and Production Space Center and RSC Energia. Low-earth orbit is the region above earth between 200km and 2,000km, ideal to place earth observation or remote sensing satellites. 

India is still a fledgling competitor in the global satellite manufacturing and launch industry, which is expected to grow to $145 billion (Rs6.3 trillion) over 10 years to 2016, from $116 billion in the 10 years to 2006, according to Paris-based research firm Euroconsult.

“(ISRO’s) benchmark is with international specifications on quality, reliability and credibility of the systems. And then, you are also cost competitive,” said K. Kasturirangan, director of the National Institute of Advanced Studies, a think tank in Bangalore. “The opportunity is just growing.” 

Source : http://www.livemint.com/

Remote tribe uses high-tech protection

US - On a recent afternoon deep in the Amazon's rain forest, members of the Surui tribe, which made contact with the outside world less than 40 years ago, could not resist that urge well-known to modern man -- they Googled themselves.

Then they looked up football.

Computers with an Internet connection, video cameras, Global Positioning System devices and other high-tech gadgets are replacing bows and arrows in the small indigenous village about 1,600 miles northwest of Rio de Janeiro, which has teamed up with Google Earth to help protect its 600,000-acre reserve from illegal miners and loggers.

"Since the Surui and other indigenous people were given training tools by Google, our land has received more visibility," Chief Almir Surui said in an e-mail written in Portuguese. Surui is both the common surname and the name of the tribe. "All the information is shedding light on the invasion of our land ... and giving our people the responsibility for their own future."

The collaboration between the Mountain View, Calif., high-tech giant and one of the most remote tribes in the world is the brainchild of Almir, the first Surui to graduate from college, who traveled to California's Bay Area last year. During his visit, Almir met with officials at Google Earth, asking them to provide high-quality satellite imagery that would allow the tribe to monitor illegal loggers and raise global awareness about the destruction of the rain forest.

Last month, a small group of scientists from Google Earth Outreach, the company's philanthropic arm, traveled to Brazil to conduct a crash course for the Surui people on how to surf the Web and use map data, YouTube and blogs.

Now, armed with the satellite imagery, videos and photos, the Surui people hope to tell stories about their culture, history and traditions through the virtual world of Google Earth. The maps will also provide updates on the planned reforestation project of the 17,300 acres the tribe has lost to illegal logging.

Though the project is still in the initial stages, once the Surui are done, "it will be a very rich layer unlike anything anyone has ever seen before," said Rebecca Moore, project manager at Google Earth Outreach.

"We traveled to the Amazon rain forest expecting to be the teachers," Moore said. "But the story of the Surui as they engage with the modern world holds lessons for all of us, and if we pay attention, we may have more to learn from them than they from us."

Since the Surui tribe made its first contact with civilization in 1969 during the construction of the 2,000-mile Trans-Amazon Highway, it has been fighting for its survival, with various diseases devastating its population. While the Brazilian constitution guarantees indigenous tribes the right to live on their lands, the government lacks resources to protect them from illegal loggers and ranchers.

The technology is helping the tribe fight back.

Source : http://seattlepi.nwsource.com/

Satellite pictures tell of human rights violation

USA – The term 'the morality of altitude' was coined to apply to bomber pilots who released their payloads on victims they couldn't see, without ever feeling any sense of remorse or empathy for the people whose lives, families, homes and property they destroyed.

A new initiative by the American Association for the Advancement of Science, publisher of the journal Science could well give the term a completely opposite and far more positive meaning. 

A project launched by the AAAS aims to use satellite imagery to provide evidence of human rights atrocities that have been denied by the governments that perpetrate them. 

The AAAS worked with Human Rights Watch to produce a 130-page report on attacks on eight villages across the remote Ogaden region of eastern Ethiopia, where "before" and "after" satellite images of villages identified by Human Rights Watch were analysed by Lars Bromley of the AAAS. 

It's confronting read, but the really damning evidence lies in the 'before' and 'after' images, which show blackened fields and destroyed homes where before there were whole villages. 
HRW's Peter Bouckaert said that the project was in response to frequent denials by the Ethiopian government that the massacres were happening at all. 

"The Ethiopian authorities frequently dismiss human rights reports, saying that the witnesses we interviewed are liars and rebel supporters," Mr Bouckaert said. "But it will be much more difficult for them to dismiss the evidence presented in the satellite images, as images like that don't lie." 

Source : http://www.sbs.com.au/

Cartosat 2A can help Kerala's Waterway project - Dr. Kalam

Former President A.P.J.Abdul Kalam the other day urged the Indian Space Research Organisation (ISRO) to use its remote sensing facilities and inputs from the Cartosat-2 mapping satellite to help the Kerala government develop the Manjeswaram-Kovalam Waterway.

Kalam was addressing a function organised by ISRO and Asianet on May 31 to honour ISRO scientists instrumental for the successful PSLV-C9 launch last month.

"Chief Minister V.S.Achuthanandan spoke passionately about the waterway project when we met. I request the ISRO to use its remote sensing and Cartosat- 2 capabilities for the project," Kalam said.

Creation of waterways across the country and interlinking of rivers, he said, are among the important infrastructural necessities in developing food, economic and national security.

The ISRO, with its current and planned space infrastructure, can play a great role in bridging the rural urban divide and providing education and healthcare for all.

It has to gear itself up for providing valuable space inputs to tackle water scarcity, promote energy independence and increase agricultural productivity, he said.

The Youth Satellite the ISRO proposes to launch by the beginning of 2009 will be a platform connecting the youth of multiple countries, Kalam said.

The satellite, he said, will definitely be a `trigger for new thinking and innovation.’ Co-ordinated development of agriculture and food processing, education and healthcare, information and communication technology and infrastructure will lead to food, economic and national security, he said.

The ISRO should increase the research base in the country which is crucial for the future aspirations of the space agency, he said. World Space Vision 2050 (suggested by Kalam to the world space community) will enhance the quality of life, inspire the spirit of space exploration, expand the horizons of knowledge and ensure space security for all nations, he said. It was also essential to create a World Space Council to oversee planning, and implementation of large-scale and societal missions like energy from space, space security and deep space exploration, he said.


Source : http://www.newindpress.com/

Scope of Remote Sensing

Definition of Remote Sensing

Remote sensing can be defined as the technique of obtaining information about objects through the analysis of data collected by instruments that are not in physical contact with the objects of investigation.
Remote vs In Situ Sensing

Remote sensing differs from in situ sensing in which the measurement instrument is in contact with the object being measured. For example, it is possible to obtain information about soil temperature using either in situ or remote sensing. In situ sensing could be conducted by pushing a thermometer into the soil and recording the soil temperature at selected sample points. Interpolation techniques could be applied to the sample point data to create a continuous soil temperature surface within the study area of interest. This data collection process is expensive, difficult to replicate - especially in remote study areas, and is subject to both measurement and interpolation errors.

Similar temperature information can be collected by remote sensing using optical-mechanical scanners that are sensitive to the thermal infrared portion of the electromagnetic spectrum. Infrared imagery has often been used to assess the energy efficiency of buildings. An infrared image taken in winter when the outside temperature is cold will clearly show where heat is being lost. Windows, doors or areas with poor insulation appear as bright spots on a thermal infrared image because they are at a higher temperature than the exterior surface of well insulated walls. Airborne or satellite sensors that detect infrared radiation could be used to obtain a soil temperature map without having to rely on interpolation between sample points. This method can be cheaper than in situ sensing, and in the case of satellite imagery, can be repeated to obtain multi-temporal images.

There are many types of remote sensors. The human eye and ear are passive remote sensors. Passive remote sensors do not emit energy but simply detect energy that is reflected or propogated back to the sensing device. The eye detects energy in the visible portion of the electromagnetic spectrum while the ear detects sounds sound energy. The photographic camera, used in daylight without flash, is a passive remote sensor as are airborne or spaceborne multispectral scanners such as Landsat MSS, Thematic Mapper or SPOT that have been widely used in environmental remote sensing. Active remote sensors emit energy and then detect the emitted energy that gets reflected back off an object. The photographic camera used with a flash, radar and laser systems used by police to catch speeders, sonar used for bathymetric mapping, and spaceborne radar systems are examples of active remote sensors. In the context of environmental remote sensing, passive remote sensing systems have been the most widely used but active remote sensing systems such as radar offer significant advantages in that they can penetrate clouds and so do not require clear sky conditions.
Stages in the Remote Sensing Process

The remote sensing process involves two stages: data collection and data analysis. Data collection is accomplished through the use of remote sensing devices that record data on photographic film or as digital data. The resulting data provide a synoptic view of a portion of the Earth's surface. but require analysis and interpretation in order to provide meaningful information. This can involve either visual interpretation, which has been the predominant mode of airphoto interpretation, or digital analysis, which has been widely used in processing satellite imagery. Remote sensing remains both an art and a science because there is often no obvious, well-documented choice of data collection devices or analysis and interpretation techniques that are best suited to a particular applied problem situation.
Electromagnetic Radiation (EMR)

Energy is the capacity to do work. Energy can take many forms such as light, heat or sound, and can be transmitted between objects through three processes: conduction, convection and radiation. Conduction requires that the objects be in direct physical contact. Energy is transferred from the high energy object to the low energy object until both objects are at the same energy level. For example, a hot water bottle warms a bed until the hot water bottle and the bed reach the same temperature, after which no further energy transfer occurs. Convection occurs in liquids and gases. Convection is based on currents that distribute energy throughout the volume of liquid or gas. Radiation does not require physical contact or the existence of a liquid or gas.

Environmental remote sensing systems focus on electromagetic energy which is a dynamic form of energy caused by the oscillation or acceleration of an electrical charge. All objects that have a temperature higher than absolute zero (0o K or -273.16o C) emit electromagnetic energy. The wavelength of the emitted energy is a function of temperature.
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GEOLOGIC APPLICATIONS of REMOTE SENSING

GEOLOGIC APPLICATIONS:

STRATIGRAPHY AND STRUCTURE

General Background

The synoptic character of Landsat and other large-area coverage satellite remote sensors is proving especially favorable in geologic mapping and other geologic applications. Land cover/use, agriculture, urban monitoring, and similar non-geologic activities related to human endeavors for the most part relate to areal coverage at smaller scales. Geologic phenomena typically are spread over wider scenes, so that the ability to see the "regional picture" is a powerful attribute of space imagery.
This view is somewhat tempered by the "interference" of vegetative cover in trying to single out geologic features. Aerial photography and space imagery work especially well on geologic subjects when the scenes they portray are minimally covered with vegetation. Early spring pictures are usually optimal (before leafing). Also, best results obtain when rocks are exposed (outcrop), are covered with little soil or are bare, and are not buried by alluvial deposits. The best images seen in this Section are in the western United States and arid regions elsewhere in the world.

Geologists have used aerial photographs for decades to serve as databases from which they can do the following:

1. Pick out rock units (stratigraphy)

2. Study the expression and modes of the origin of landforms (geomorphology)

3. Determine the structural arrangements of disturbed strata (folds and faults)

4. Evaluate dynamic changes from natural events (e.g., floods; volcanic eruptions)

5. Seek surface clues (such as alteration and other signs of mineralization) to subsurface deposits of ore minerals, oil and gas, and groundwater.

6. Function as a visual base on which a geologic map is drawn either directly or on a transparent overlay.

With the advent of space imagery, geoscientists now can extend that use in three important ways:

1) The advantage of large area or synoptic coverage allows them to examine in single scenes (or in mosaics) the geological portrayal of Earth on a regional basis

2) The ability to analyze multispectral bands (especially hyperspectral data sets) quantitatively in terms of numbers (DNs) permits them to apply special computer processing routines to discern and enhance certain compositional properties of Earth materials

3) The capability of merging different types of remote sensing products (e.g., reflectance images with radar or with thermal imagery) or combining these with topographic elevation data and with other kinds of information bases (e.g., thematic maps; geophysical measurements and chemical sampling surveys) enables new solutions to determining interrelations among various natural properties of earth phenomena.

While these new space-driven approaches have not yet revolutionized the ways in which geoscientists conduct their field studies, they have proven to be indispensable techniques for improving the geologic mapping process and carrying out practical exploration for mineral and energy resources on a grand scale.

The very first practical use of ERTS-1 (Landsat-1) imagery in any discipline was the drawing by Dr. Paul D. Lowman, Jr, of a geologic structures map superimposed on the first color composite image, based in part on already known field information and in part on his interpretation of this scene. He is a geologist at Goddard Space Flight Center, and an expert on space photography (he prepared Section 12 on Astronaut Imagery in this Tutorial). The image was of the central California coast around Monterey Bay, acquired 3 days after launch.



This map confirmed predictions from his studies of astronauts' photos that Landsat would be an efficient tool for recognizing faults and other known structural trends in small-scale imagery. In spite of lower resolutions, these images excel in portraying regional geologic settings and are easily enhanced by digital processing.

We now consider several examples of geologic applications using these new approaches. We concentrate initially on how Landsat Thematic Mapper (TM) data for a local region in Utah are manipulated to identify different rock types, map them over a large area using supervised classification, and correlate their spatial patterns with independent information on their structural arrangement. Next, our focus changes to examination of geologic structures, particularly lineaments, as displayed in regional settings in the U.S., Canada, and Africa. Then, in Section 5, we will look at how space-acquired data fit into current methods of exploring for mineral and hydrocarbon deposits by considering a case study of a mineralized zone in Utah and at a large-area Landsat scene in Oklahoma. In Section 17, we will return to a geologic theme by examining landforms at regional scales, (so-called Mega-geomorphology), as a prime example in considering how remote sensing is used in basic science studies.

Most geologic maps are also stratigraphic maps, that is, they record the location and identities of sequences of rock types according to their relative ages. The fundamental rock unit is the formation (abbreviated as Fm or fm), defined simply as a distinct mappable set of related rocks (usually sedimentary) that has a specific geographic distribution. A formation typically is characterized by one or two dominant types of rock materials.

The term "formation" is most commonly associated with strata, namely layers of sediments that have hardened into sedimentary rocks. Under most conditions, sediments are laid down in horizontal or nearly so layers on sea floors, lake bottoms, and transiently in river beds. Here is a typical set of sedimentary layers exposed in a road cut (note that the layers have been cut and slightly offset by a break which is termed a "fault"):



If we see sedimentary rocks inclined at more than a few degrees from the horizontal, we should suspect that these are involved in displacements from their original horizontal state by forces (tectonic) that cause the rocks to bend and curve (folds) or break (faults). Here is a roadcut along a Maryland highway that is passing through the fold belt of the Appalachians.



As we shall see later in this Section, inclined layers can produce curved structures called anticlines (uparched) and synclines (downwarched), Here is an example of the latter:



Any given formation is developed and emplaced over some finite span of geologic time. We can approximate its age by the fossils (evidence of past life) that were incorporated within the soft layers (which become strata or beds) during the time in which these life forms existed. Age dating by determining the amounts of radioactive elements and their decay-daughter products can usually produce even more accurate age estimates. Another, less precise, approach to fixing the age (span) of a rock unit is to note its position in the sequence of other rock units, some of whose ages are independently known. We can correlate the units with equivalent ones mapped elsewhere that have had their ages worked out. This method tends to bracket the time in which the sedimentary formation was deposited but erosional influences may lead to uncertainties. The association of sedimentary layers with specific time intervals constitutes the field of stratigraphy. Igneous and metamorphic rocks also have time significance and are treated as rock units on geologic maps (which show all stratigraphic and crystalline units in a legend).

Remote-sensing displays, whether they are aerial photos, space-acquired images, or classification maps, show the surface distribution of the multiple formations usually present and, under appropriate conditions, the type(s) of rocks in the formations. The formations show patterns that depend on their proximity to the surface, their extent over the surveyed area, their relative thicknesses, their structural attitude (horizontal or inclined layers), and their degree of erosion. Experienced geologists can recognize some rock types just by their appearance in the photo/image. They identify others types from their spectral signatures. Over the spectral range covered by the Landsat TM bands, the types and ages of rocks show distinct variations at specific wavelengths. This is evident in the following spectral plots showing laboratory-determined curves obtained by a reflectance spectrometer for a group of diverse sedimentary rocks (collected from their field formations [named from a geographic locality near where the fromationas unit was first described) from Wyoming:



2-1 From these spectra, predict the general color of these four rock units: Niobrara Fm; Chugwater Fm; Frontier Fm; Thermopolis Fm. ANSWER

2-2: What spectrally distinguishes the Mowry Fm from the Thermopolis Fm (both dark in the field); the Jelm Fm from the White River Conglomerate? ANSWER

Several of these spectral signatures are composite, in that more than one mineral substance is present. Spectra of individual minerals can be quite different, as shown in the next illustration:



The Near-IR wavelength interval from 2.0 to 2.4 µm is especially sensitive to absorption of radiation; note that all four minerals share a negative peak (trough) around 2.32 µm. But a word of caution: there are several thousand mineral species known from Earth. The spectra of many of these can be similar, making field and even laboratory distinction of just what mineral species is being looked at difficult to assess (X-ray diffraction patterns are usually better identifiers). When remote sensing is involved in onsite surveys, knowledge beforehand of the particular minerals likely in the sensed scene is usually a benefit. Thus, when an area is being surveyed for specific mineral content (as in exploration for ore minerals; Section 5), this information aids in identifying the mineral species being mapped. This is also true for rock types; whenever possible, field spectra such as shown above for Wyoming formations are gathered as Ground Truth (Section 13) so that their diagnostic characteristics can be used to calibrate the image analysis (as an example, referring to the rock spectra above, the averaged reflectances of the 6 non-thermal TM bands can be calculated for, say, the Muddy Sandstone; this knowledge could then be fed into a computer classification as a discriminator function which looks for all pixels with similar multiband values).

A common way of mapping formation distribution is to rely on training sites at locations within the photo/image. Geologists identify the rocks by consulting area maps or by visiting specific sites in the field. They then extrapolate the rocks' appearance photographically or by their spectral properties across the photo or image to locate the units in the areas beyond the training sites (in effect, the supervised classification approach).

In doing geologic mapping from imagery, we know that formations are not necessarily exposed everywhere. Instead they may be covered with soil or vegetation. In drawing a map, a geologist learns to extrapolate surface exposures to underneath covered areas, making logical deductions as to which hidden units are likely to occur below the surface. In working with imagery alone, these deductions may prove difficult and are a source of potential error. Also, rock ages are not directly determined from spectral data - only material types are determined, so that identifying a particular formation requires some independent information (knowledge of a region's rock types and their sequence).

In exceptional instances, such as those to be shown on the next three pages, when geologic strata are turned on their side (from folding; discussed on page 2-5) so that the successive geologic units are visible as a sequence, the changes within and between each discrete unit can be measured in terms of some spectral property, as for example, variations in the reflectance of a given band, or a ratio of bands. When plotted as shown below the result are tracings that resemble (analogously) those made from well logging of such properties as electric resistivity, permeability, magnetic intensity and other geophysical parameters. Here are two figures, the top showing the succession of sedimentary strata exposed along the Casper Arch in central Wyoming; the bottom being reflectance "logs" derived from spectral traverses along one of the lines in the upper image:


                             


In the lower diagram, the bottom unit is the Permian Phosphoria Formation, extending upward from the Triassic Chugwater Formation to the Frontier sandstone (Cretaceous) at the top. On the right the left tracing is of TM Band 3 (red), with 0% reflectance on the right base extending to 70% on the left, and the right tracing, for Band 1, goes from 0% on the left to 50% on the right.

Before looking at some specific examples of the use of space imagery for geologic structure analysis, this is a good point to introduce one particular advantage of having space observing systems that can repetitively cover the same large regions over the four seasons. Two Landsat images appear below: one taken during the southern Winter in South Africa; the other during the height of Spring. The area includes Johannesburg, some of the gold mines in the Witwatersrand district, and the Pilanesburg pluton (near the top). In the wintertime, some of the underlying rock units fail to show distinctly because the entire scene has its vegetation (mostly grasslands) dormant. But with plant reawakening in Spring, different units have different vegetation types and these variably modify the colors displayed, thus revealing the more complex structures in the region.





This geobotanical phenomenon (the differential distribution of various plant types as a function of soils developed on different rock types) is sometimes used as a mapping or prospecting device (example; the element Selenium is associated with Uranium; certain plants thrive on Selenium enrichment and are thus indicators of subsurface Uranium enrichment).

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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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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:


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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.

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Electromagnetic Spectrum: Distribution of Radiant Energies

Electromagnetic radiation (EMR) extends over a wide range of energies and wavelengths (frequencies). A narrow range of EMR extending from 0.4 to 0.7 µm, the interval detected by the human eye, is known as the visible region (also referred to as light but physicists often use that term to include radiation beyond the visible). White light contains a mix of all wavelengths in the visible region. It was Sir Isaac Newton who first in 1666 carried out an experiment that showed visible light to be a continuous sequence of wavelengths that represented the different color the eye can see. He passed white light through a glass prism and got this result:



The principle supporting this result is that as radiation passes from one medium to another, it is bent according to a number called the index of refraction. This index is dependent on wavelength, so that the angle of bending varies systematically from red (longer wavelength; lower frequency) to blue (shorter wavelength; higher frequency). The process of separating the constituent colors in white light is known as dispersion. These phenomena also apply to radiation of wavelengths outside the visible (e.g., a crystal's atomic lattice serves as a diffraction device that bends x-rays in different directions).

The distribution of the continuum of all radiant energies can be plotted either as a function of wavelength or of frequency in a chart known as the electromagnetic (EM) spectrum. Using spectroscopes and other radiation detection instruments, over the years scientists have arbitrarily divided the EM spectrum into regions or intervals and applied descriptive names to them.The EM spectrum, plotted here in terms of wavelengths, is shown here.



Beneath is a composite illustration taken from the Landsat Tutorial Workbook (credited there to Lintz and Simonett, Remote Sensing of the Environment, who identify it as a modification of an earlier diagram by Robt. Colwell) that shows in its upper diagram the named spectral regions in terms of wavelength and frequency and in the lower diagram the physical phenomena that give rise to these radiation types and the instruments (sensors) used to detect the radiation. (Although the width of this second diagram scales closely to the width of the spectrum chart above it, the writer experienced difficulty in centering this second diagram on the present page; it needs some leftward offset so that the narrow pair of vertical lines coincides with the visible range in the upper diagram.)





Although it is somewhat redundant, we reproduce here still another plot of the EM Spectrum, with added items that are self-explanatory:



Colors in visible light are familiar to most, but the wavelength limits for each major color are probably not known to most readers. Here is a diagram that specifies these limits (the purple on the far left is in the non-visible ultraviolet; the deep red on the far right is the beginning of the infrared). The human eye is said to be able to distinguish thousands of slightly different colors (one estimate placed this at distinguishable 20000 color tints).



Different names for (wave)length units within intervals (those specified by types) that subdivide the EM spectrum, and based on the metric system, have been adopted by physicists as shown in this table:



(Both in this Tutorial and in other texts, just which units are chosen can be somewhat arbitrary, i.e., the authors may elect to use micrometers or nanometers for a spectral location in the visible. Thus, as an example, 5000 Angstroms, 500 nanometers, and 0.5 micrometers all refer to the same specific wavelength; see next paragraph.)

At the very energetic (high frequency and short wavelength) end are gamma rays and x-rays (whose wavelengths are normally measured in angstroms [Å], which in the metric scale are in units of 10-8 cm). Radiation in the ultraviolet extends from about 300 Å to about 4000 Å. It is convenient to measure the mid-regions of the spectrum in one of two units: micrometers (µm), which are multiples of 10-6 m or nanometers (nm), based on 10-9 m. The visible region occupies the range between 0.4 and 0.7 µm, or its equivalents of 4000 to 7000 Å or 400 to 700 NM The infrared region, spanning between 0.7 and 1000 µm (or 1 mm), has four subintervals of special interest: (1) reflected IR (0.7 - 3.0 µm), and (2) its film responsive subset, the photographic IR (0.7 - 0.9 µm); (3) and (4) thermal bands at (3 - 5 µm) and (8 - 14 µm). We measure longer wavelength intervals in units ranging from mm to cm. to meters. The microwave region spreads across 0.1 to 100 cm, which includes all of the interval used by radar systems. These systems generate their own active radiation and direct it towards targets of interest. The lowest frequency-longest wavelength region beyond 100 cm is the realm of radio bands, from VHF (very high frequency) to ELF (extremely low frequency); units applied to this region is often stated as frequencies in units of Hertz (1 Hz = 1 cycle per second; KHz, MHz and GHz are kilo-, mega-, and giga- Hertz respectively). Within any region, a collection of continuous wavelengths can be partioned into discrete intervals called bands.
 

Referring to the Phenomenology diagram (fourth illustration above): That chart indicates many of the atomic or molecular mechanisms for forming these different types of radiation; it also depicts the spectral ranges covered by many of the detector systems in common use. This diagram indicates that electromagnetic radiation is produced in a variety of ways. Most involve actions within the electronic structure of atoms or in movements of atoms within molecular structures (as affected by the type of bonding). One common mechanism is to excite an atom by heating or by electron bombardment which causes electrons in specific orbital shells to momentarily move to higher energy levels; upon dropping back to the original shell the energy gained is emitted as radiation of discrete wavelengths. At high energies even the atom itself can be dissociated, releasing photons of short wavelengths. And photons themselves, in an irradiation mode, are capable of causing atomic or molecular responses in target materials that generate emitted photons (in the reflected light process, the incoming photons that produce the response are not necessarily the same photons that leave the target).

Most remote sensing is conducted above the Earth either within or above the atmosphere. The gases in the atmosphere interact with solar irradiation and with radiation from the Earth's surface. The atmosphere itself is excited by EMR so as to become another source of released photons. Here is a generalized diagram showing relative atmospheric radiation transmission of different wavelengths.



Blue zones (absorption bands) mark minimal passage of incoming and/or outgoing radiation, whereas, white areas (transmission peaks) denote atmospheric windows, in which the radiation doesn't interact much with air molecules and hence, isn't absorbed. This next plot, made with the AVIRIS hyperspectral spectrometer (see page page 13-9), gives more a more detailed spectrum, made in the field looking up into the atmosphere, for the interval 0.4 to 2.5 µm (converted in the diagram to 400-2500 nanometers).


Most remote sensing instruments on air or space platforms operate in one or more of these windows by making their measurements with detectors tuned to specific frequencies (wavelengths) that pass through the atmosphere. However, some sensors, especially those on meteorological satellites, directly measure absorption phenomena, such as those associated with carbon dioxide, CO2 and other gaseous molecules. Note in the second diagram above that the atmosphere is nearly opaque to EM radiation in part of the mid-IR and almost all of the far-IR region (20 to 1000 µm). In the microwave region, by contrast, most of this radiation moves through unimpeded, so radar waves reach the surface (although raindrops cause backscattering that allows them to be detected). Fortunately, absorption and other interactions occur over many of the shorter wavelength regions, so that only a fraction of the incoming radiation reaches the surface; thus harmful cosmic rays and ultraviolet (UV) radiation that could inhibit or destroy certain life forms are largely prevented from hitting surface environments.

I-10: From the first atmospheric absorption figure, list the four principal windows (by wavelength interval) open to effective remote sensing from above the atmosphere.


Backscattering (scattering of photons in all directions above the target in the hemisphere that lies on the source side) is a major phenomenon in the atmosphere. Mie scattering refers to reflection and refraction of radiation by atmospheric constituents (e.g., smoke) whose dimensions are of the order of the radiation wavelengths. Rayleigh scattering results from constituents (e.g., molecular gases [O2, N2 {and other nitrogen compounds}, and CO2], and water vapor) that are much smaller than the radiation wavelengths. Rayleigh scattering increases with decreasing (shorter) wavelengths, causing the preferential scattering of blue light (blue sky effect); however, the red sky tones at sunset and sunrise result from significant absorption of shorter wavelength visible light owing to greater "depth" of the atmospheric path as the Sun is near the horizon. Particles much larger than the irradiation wavelengths give rise to nonselective (wavelength-independent) scattering. Atmospheric backscatter can, under certain conditions, account for 80 to 90% of the radiant flux observed by a spacecraft sensor.

Remote sensing of the Earth traditionally has used reflected energy in the visible and infrared and emitted energy in the thermal infrared and microwave regions to gather radiation that can be analyzed numerically or used to generate images whose tonal variations represent different intensities of photons associated with a range of wavelengths that are received at the sensor. This sampling of a (continuous or discontinuous) range(s) of wavelengths is the essence of what is usually termed multispectral remote sensing.

Images made from the varying wavelength/intensity signals coming from different parts of a scene will show variations in gray tones in black and white versions or colors (in terms of hue, saturation, and intensity in colored versions). Pictorial (image) representation of target objects and features in different spectral regions, usually using different sensors (commonly with bandpass filters) each tuned to accept and process the wave frequencies (wavelengths) that characterize a given region, will normally show significant differences in the distribution (patterns) of color or gray tones. It is this variation which gives rise to an image or picture. Each spectral band will produce an image which has a range of tones or colors characteristic of the spectral responses of the various objects in the scene; images made from different spectral bands show different tones or colors.

This point - that each spectral band image is unique and characteristic of its spectral makeup - can be dramatically illustrated with views of astronomical bodies viewed through telescopes (some on space platforms) equipped with different multispectral sensing devices. Below are four views of the nearby Crab Nebula, which is now in a state of chaotic expansion after a supernova explosion first sighted in 1054 A.D. by Chinese astronomers (see Section 20 - Cosmology - for other examples). The upper left illustration shows the Nebula as sensed in the high energy x-ray region; the upper right is a visual image; the lower left was acquired from the infrared region; and the lower right is a long wavelength radio telescope image.










By sampling the radiation coming from any material or class under observation over a range of continuous (or intermittent, in bands) spectral interval, and measuring the intensity of reflectance or emittance for the different wavelengths involve, a plot of this variation forms what is referred to as a spectral signature, the subject of the next page's discussion.

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The Concept of Remote Sensing

If you have heard the term "remote sensing" before you may have asked, "what does it mean?" It's a rather simple, familiar activity that we all do as a matter of daily life, but that gets complicated when we increase the scale at which we observe. As you view the screen of your computer monitor, you are actively engaged in remote sensing.



A physical quantity (light) emanates from that screen, whose imaging electronics provides a source of radiation. The radiated light passes over a distance, and thus is "remote" to some extent, until it encounters and is captured by a sensor (your eyes). Each eye sends a signal to a processor (your brain) which records the data and interprets this into information. Several of the human senses gather their awareness of the external world almost entirely by perceiving a variety of signals, either emitted or reflected, actively or passively, from objects that transmit this information in waves or pulses. Thus, one hears disturbances in the atmosphere carried as sound waves, experiences sensations such as heat (either through direct contact or as radiant energy), reacts to chemical signals from food through taste and smell, is cognizant of certain material properties such as roughness through touch (not remote), and recognizes shapes, colors, and relative positions of exterior objects and classes of materials by means of seeing visible light issuing from them. In the previous sentence, all sensations that are not received through direct contact are remotely sensed.

I-1 In the illustration above, the man is using his personal visual remote sensing device to view the scene before him. Do you know how the human eye acts to form images? If not, check the answer. ANSWER

However, in practice we do not usually think of our bodily senses as engaged in remote sensing in the way most people employ that term technically. A formal and comprehensive definition of applied remote sensing * is:

Remote Sensing in the most generally accepted meaning refers to instrument-based techniques employed in the acquisition and measurement of spatially organized (most commonly, geographically distributed) data/information on some property(ies) (spectral; spatial; physical) of an array of target points (pixels) within the sensed scene that correspond to features, objects, and materials, doing this by applying one or more recording devices not in physical, intimate contact with the item(s) under surveillance (thus at a finite distance from the observed target, in which the spatial arrangement is preserved); techniques involve amassing knowledge pertinent to the sensed scene (target) by utilizing electromagnetic radiation, force fields, or acoustic energy sensed by recording cameras, radiometers and scanners, lasers, radio frequency receivers, radar systems, sonar, thermal devices, sound detectors, seismographs, magnetometers, gravimeters, scintillometers, and other instruments.

I-2 To help remember the principal ideas within this definition, make a list of key words in it. ANSWER

This is a rather lengthy and all-inclusive definition. Perhaps two more simplified definitions are in order: The first, more general, includes in the term this idea: Remote Sensing involves gathering data and information about the physical "world" by detecting and measuring signals composed of radiation, particles, and fields emanating from objects located beyond the immediate vicinity of the sensor device(s). The second is more restricted but is pertinent to most of the subject matter of this Tutorial: In its common or normal usage (by tacit implication), Remote Sensing is a technology for sampling electromagnetic radiation to acquire and interpret non-contiguous geospatial data from which to extract information about features, objects, and classes on the Earth's land surface, oceans, and atmosphere (and, where applicable, on the exteriors of other bodies in the solar system, or, in the broadest framework, celestial bodies such as stars and galaxies).

 What is the meaning of "geospatial"? Are there any differences in meaning of the terms "features", "objects", and "classes"?

 Applied Remote Sensing involves the detecting and measuring of electromagnetic energy (usually photons) emanating from distant objects made of various materials, so that the user can identify and categorize these objects - usually, as rendered into images - by class or type, substance, and spatial distribution. Generally, this more conventional description of remote sensing has a specific criterion by which its products point to this specific use of the term: images much like photos are a main output of the sensed surfaces of the objects of interest. However, the data often can also be shown as "maps" and "graphs", or to a lesser extent, as digital numbers that can be input to computer-based analysis, and in this regard are like the common data displays resulting from geophysical remote sensing. As applied to meteorological remote sensing, both images (e.g., clouds) and maps (e.g., temperature variations) can result; atmospheric studies (especially of the gases in the air, and their properties) can be claimed by both traditionalists and geophysicists.

All of these statements are valid and, taken together, should give you a reasonable insight into the meaning and use of the term "Remote Sensing" but its precise meaning depends on the context in which it is spoken of.

Thus, as the above comments suggest, some technical purists arbitrarily stretch the scope or sphere of remote sensing to include other measurements of physical properties from sources "at a distance" that are more properly included in the general term "Geophysics". (Geophysics has a scientific connotation: it is pertinent to the study of the physical properties of Earth and other planets. It likewise has an applied connotation: it is the technology often used to search for oil and gas and for mineral deposits.) This latter is especially conducted through such geophysical methods as seismic, magnetic, gravitational, acoustical, and nuclear decay radiation surveys. Magnetic and gravitational measurements respond to variations in force fields, so these can be carried out from satellites. Remote sensing, as defined in this context, would be a subset within the branch of science known as Geophysics. However, practitioners of remote sensing, in its narrower meaning, tend to exclude these other areas of Geophysics from their understanding of the meaning implicit in the term.

Still, space systems - mostly on satellites - have made enormous contributions to regional and global geophysical surveys. This is because it is very difficult and costly to conduct ground and aerial surveys over large areas and then to coordinate the individual surveys by joining them together. To obtain coherent gravity and magnetic data sets on a world scale, operating from the global perspective afforded by orbiting satellites is the only reasonable alternate way to provide total coverage.

One could argue that Geophysics deserves a Section of its own but in the remainder of this Tutorial we choose to confine our attention almost entirely to those systems that produce data by measuring in the electromagnetic radiation (EMR) spectrum (principally in the Visible, Infrared, and Radio regions). We will reserve our treatment of Geophysics to three pages near the end of this Introduction. There you are given examples of the use of satellite instruments to obtain information on particles and fields as measured inside and around the Earth; in Sections 19 and 20 (Planets and Cosmology) there will also be some illustrations of several types of geophysical measurements.

One mode of remote sensing not treated in the Tutorial is acoustic monitoring of sound waves in atmospheric and marine environments. For example, volcanic eruptions or nuclear (testing) explosions can be detected by sensitive sound detectors. Sonar is used to track submarines and surface ships in the oceans. Sound through water are also involved in listening to marine animals such as whales and porpoises.

It may seem surprising to realize that going to the doctor can involve remote sensing. Most obvious, on a miniature scale, is listening to a heartbeat using the stethoscope. But in the field of modern medical technology, powerful, often large, instruments such as CATscans and Magnetic Resonance Imaging, are now almost routinely used for non-invasive subskin investigation of human tissue and organs. This is indeed another major application of remote sensing that will be surveyed on pages I-26c through I-26e.

The traditional way to start consideration of what remote sensing is and means is to set forth its underlying principles in a chapter devoted to the Physics on which remote sensing is founded. This will be done in the next 5 pages. The ideas developed may seem arcane. These pages contain the "technical jargon" that remote sensing specialists like to banty about. With this caveat in mind, work through the pages, try to understand the esoteric, and commit to memory what seems useful.

* The term "remote sensing" is itself a relatively new addition to the technical lexicon. It was coined by Ms Evelyn Pruitt in the mid-1950's when she, a geographer/oceanographer, was with the U.S. Office of Naval Research (ONR) outside Washington, D.C.. It seems to have been devised by Ms Pruitt to take into account the new views from space obtained by the early meteorological satellites which were obviously more "remote" from their targets than the airplanes that up until then provided mainly aerial photos as the medium for recording images of the Earth's surface. No specific publication or professional meeting where the first use of the term occurred is cited in literature consulted by the writer (NMS). Those "in the know" claim that it was verbally used openly by the time of several ONR-sponsored symposia in the 1950s at the University of Michigan. The writer believes he first heard this term at a Short Course on Photogeology coordinated by Dr. Robert Reeves at the Annual Meeting of the Geological Society of America in 1958.

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GIS and Satellite Remote Sensing for Arachaeology

Satellite remote sensing can provide a variety of useful data for this type of research. A variety of sources for such data exist, and while the data can be expensive and require extensive digital image processing, they provide a synoptic view which is not available from aerial photography. It takes over 200 aerial mapping photos to cover the same area as a single satellite image. We have used a variety of satellite data for this project, including older Landsat MSS from the late 1970's and more modern French SPOT and Radarsat data. New systems with 1 meter spatial resolution are now commercially available, but have not been used in this project due to cost (donations are welcome!).

The image at right is a SPOT image overlaid on a digital elevation model of the area.

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Landsat Multi Spectral Scanner (MSS)

Early in the project the only available remote sensing data was Landsat MSS, with 80 meter spatial resolution. Two images were acquired and digital image processing was conducted to generate color composite images and vegetation maps.

The following images are from the 22 March, 1973 image of the region shown in false color infrared.

This first image shows the entire study area, with the pasture lands shown in pink, forest in darker reds, urban in gray, and water in black.

.. The image at right show the northern part of the study area. Al left you see the southern part of the research area. The banding is an artifiact of the data, and was later removed using digital image processing techniques. You can clearly see the river and gravel mines along it.

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French SPOT Data

The commercial civil remote sensing system developed by the French is called SPOT. It has a spatial resolution of 20 meters for multi-spectral data, which records information in three bands of the spectrum, and a 10 meter spatial resolution for a panchromatic band. The resolution of these images available from space can provide significant improvements in the utility of these data for regional archaeological and environmental applications, especially (as in France) where the field size is very small. Accurate modern landcover maps can be produced using SPOT satellite imagery. Below is a SPOT image which has been classified into a landuse/landcover map of the area. Yellows are pasture, greens are forest, blues are water, and reds are urban. This image was then combined with a shaded relief map (derived from a Digital Elevation Model) to produce a realistic representation of the landscape.

This is a SPOT land use/landcover image draped over the Digital Elevation Model from the project GIS database. Click on the image to see the larger version. You can clearly see the river valley down the center of the image, and the Morvan mountains at the top. The confluence of the Arroux and the Loire is at the bottom of the image. The red line at bottom is the modern highway from the town of Digoin at the confluence of the rivers, and Paray-le-Monial at right. The larger blue areas along the river are the lakes that the gravel mines leave after they remove the materials.

This is a color infrared SPOT 20 meter image, acquired 9 Nov. 1986, showing Mt. Beuvray, site of the ancient city of Bibracte, at upper left. The Arroux river valley flows from top to bottom on the right side. The city of Autun, ancient Augustodunum Aedorum, is at top right.

A close-up view of Mt. Beuvray, 9 Nov. 1986.

SPOT Satellite landcover map-modern vegetation

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RADARSAT imagery

Canadian RADARSAT-1 satellite imagery has been acquired of the region on 4 November, 1998 with a spatial resolution of 8 meters. This system is different from SPOT or Landsat in that it is an active radar system that sends its own burst of electromagnetic radiation down to the ground which is then bounced off the surface and recorded on the satellite. Analysis of the Radarsat is ongoing. This system can operate day or night and through cloud cover. It provides a different and new way of visualizing the area. This image is C band, HH, descending orbit, right look, FINE mode. Inc. angle 39-42 degrees.

.. Radarsat image of Mont Beuvray, site of Bibracte at left, and Autun at right.

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Additional new imagery has been acquired, including NASA ASTER data. These will added soon.

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source:http://www.informatics.org