Just what is this elusive �remote sensing� we've been talking about? Try this general definition (a similar one is given on page 1 of the Introduction (click on this colored word to access it; then click on Back to return): Remote Sensing involves techniques that use sensor devices to detect and record signals emanting from target(s) of interest not in direct contact (thus, at a distance) with the sensor. Let�s break down the key words. Techniques range from simple visual interpretation of a sensed scene (which usually has both geometric [spatial] and geographic [locational] characteristics) carried out by one�s brain to methods of analysis that utilize complex algorithms applied to digitized measurements . Sensors usually refer to systems that have optico-mechanical and electronic components - commonly sophisticated but can be as basic as a film camera. Detection implies the ability of the sensor to properly respond to the signal, and to measure its quantita-tive properties. Record denotes the ability to retain the signal in a usable format that favors analysis. The signal itself can be diverse: Most commonly, it is some form of electromagnetic (EM) energy (as photons) that is expressed as radiation representing discrete wavelength intervals or bands (e.g., xrays; visible light; radio waves) within the EM spectrum. However, remote sensing is the appropriate term when applied to acoustical (listening) devices, to detectors that respond to magnetic force fields, and to instruments for seeing into human or animal bodies (such as CATscans). The �target of interest� is almost self-explanatory - the words "feature", "object", "category", and "class" are descriptive. The idea behind �not in contact� or �at a distance� is synonomous with �remote�, in that the target is removed from physically touching the sensor; as a result, distance allows a wider field of view to be sensed (typically the sensor optics bring into focus all resolvable objects in a cone of observation). Most remote sensing applications involve having the sensors look down (commonly vertically) or outward.

With this first insight in mind, consider this: Normally, we experience our world from a more or less horizontal viewpoint while living on its surface. But, under these conditions our view is usually limited to areas around our view site that fall within only a few square miles at most owing to obstructions such as buildings, trees, and topography. The total area encompassed in our vistas is considerably enlarged if we peer downward from, say, a tall building or a mountain top. This increases even more - to perhaps hundreds of square miles - as we gaze outwards from an airliner cruising above 30000 feet. From a vertical or high oblique perspective (as from a mountaintop or a skyscraper), our impression of the surface below is notably different than when we scan our surroundings from a point directly on that surface. We then see the multitude of surface features as they would appear on a thematic map in their appropriate spatial and contextual relationships. This, in a nutshell, is why remote sensing is most often practiced from platforms such as airplanes and spacecraft with onboard sensors that survey and analyze these features over extended areas from above, unencumbered by the immediate proximity of the neighborhood. It is the practical, orderly, and cost-effective way of maintaining and updating information about the world around us.

O-2: State an advantage and a disadvantage in conducting a remote sensing viewing from progressively higher altitudes. ANSWER

Until the 1960s, remote sensing was almost synonomous with 'aerial photography', as will be evident after the next few paragraphs. Now, it is most often applied to "satellite imagery", which also is implicit in "satellite remote sensing" since the chief product of such sensing is an image or a map derived therefrom. Aerial photography is still big business but with the advent of high resolution satellite image, it has an ever lowering market share. Satellite-borne remote sensors (and comparable ones mounted in aircraft) have these major advantages over aerial photography: 1) they provide worldwide coverage almost automatically; 2) they have potentially high frequency of repeat coverage; and 3) they usually are multispectral in their design, allowing quantitative manipulations of the sensed data (which are also normally acquired in digitized formats), so that the objects in the scenes can be identified and analyzed by classification programs. This chart summarizes the main benefits of satellite remote sensing:

Now consider this very important precept or thesis spelled out in bold red letters to accentuate the importance and scope of remote sensing:

Most remote sensing systems are built around cameras, scanners, radiometers, Charge Coupled Device (CCD)-based detectors, radar, etc. of various kinds. The systems are the most widely used tools (instruments) for acquiring information about Earth, the planets, the stars, and ultimately the whole Cosmos. These normally look at their targets from a distance. One can argue that geophysical instruments operating on the Earth's surface or in boreholes are also remote sensing devices. And the instruments on the Moon's and Mars' surfaces likewise fall broadly into this category. In other words, remote sensing lies at the heart of the majority of unmanned (and as important tasks during some manned) missions flown by NASA and the Russian space agency, as well as programs by other nations (mainly, Canada, France, Germany, Italy, India, China, Japan, and Brazil) to explore space, from our terrestrial surface to the farthest galaxies. NASA and other space agencies have spent more money (the principal writer [NMS] estimates this sum to be in excess of $500 billion dollars [probably a low figure) on activities that - directly or indirectly - utilize remote sensors as their primary data-gathering instruments than on those other systems operating in space (such as Shuttle/MIR/ISS and communications satellites), in which remote sensing usually plays only a subordinate role. Add to this the idea that ground-based telescopes, photo cameras, and our eyes used in everyday life are also remote sensors, then one can rightly conclude that remote sensing is a dominant component of certain scientific and technical aspects of human activity that involve looking at and characterizing objects of interest - a subtle realization since most of us do not use the term "remote sensing" in our normal vocabulary.

Having now made this "sales pitch" for the merits of remote sensing, let us turn the newly convinced to a brief look at the history of Remote Sensing (covered in further detail on page I-7ff. and elsewhere in Appendix A). This history is closely tied in some of its aspects to the Space Program, whose early highlights will be reviewed beginning with the sixth paragraph down. But first a short synopsis of early remote sensing efforts from aerial platforms above the Earth.

The practice of remote sensing can be said to have begun with the invention of photography. Close-up photography (Proximal Remote Sensing) began in 1839 with the primitive but amazing images by by the Frenchmen Daguerre and Neipce. Distal Remote Sensing from above ground began with the earliest balloon photo made above a Paris, France suburb by Honore Daumier but this historic first picture has been lost. In the 1860s during the Civil War balloonists took pictures of the Earth's surface using the newly invented photo-camera. These balloons were used for reconnaissance; legend has it that General McClelland had a battlefield photo made from such an aerial post but it has disappeared. Most photos were made from tethered balloons but later free-flying balloons provided the platform. The photo below was made from a balloon anchored above a Boston, Massachusetts neighborhood in 1860 and is the first surviving aerial photo in the world.

NOTE: Each image throughout this Tutorial will have a caption that is accessed simply by placing your mouse on the lower right portion of the image.

It is a little known fact that the first aerial photo taken from a rocket was made by the Swede Alfred Nobel (of Nobel Prize fame) in 1897. Here is the picture he obtained of the Swedish landscape:

Perhaps the most novel platform at the beginning of the 20th century was the famed Bavarian pigeon fleet that operated in Europe. Pigeons at the ready are shown here, with a prized 1903 picture taken of a Bavarian castle beneath (the irregular objects on either side are the flapping wings).

O-3: What is an obvious disadvantage in using this primitive pigeon system? ANSWER

In 1906 interest in getting a panoramic view of the destruction in San Francisco, California right after the catastrophic earthquake prompted an ingenious effort by "flying" cameras on kites. Here is the resulting composite photo of part of the city along and in from the wharves in San Francisco Bay.:

Ever since the legend of Icarus in ancient Greek mythology, humans have been possessed by the urge to emulate the birds and fly themselves. One can point to the famed flight of Wilbur and Orville Wright on 1904 as the first real triumph of making us airborne through use of combustible fuel - in a sense this can be singled out as the first tiny step into Space.

Aerial photography became a valuable reconnaissance tool during the First World War and came fully into its own during the Second World War. Both balloons and aircraft served as platforms.

The possibility of conducting "aerial" photography from space hinges on the ability to use rockets to launch the equipment, either up some distance to then fall back to Earth or into Earth orbit. Page I-7 in the Introduction describes the earliest successes. Rocketry can be traced back to ancient times when the Chinese used solid materials, similar to their firecracker powders, to provide the thrust. In the 19th Century, the noted French science fiction writer, Jules Verne, conceived of launching a manned projectile to the Moon (in his book "From the Earth to the Moon", which formed the inspiration for this writer's [NMS] first presented science paper on rocketry to his high school Science Club). In the first half of the 20th Century, a leader in rocketry was Robert Goddard (1889-1945) after whom Goddard Space Flight Center (where this Tutorial is based) was named. Below is a 1926 photo of Dr. Goddard with one of his first liquid fuel rockets (the motor is on the top of this 10 foot vehicle [it would break free from the frame holding it up]).

The logical entry of remote sensors into space on a routine basis began with automated photo-camera systems mounted on captured German V-2 rockets, launched out of White Sands, NM. These rockets also carried geophysical instruments in their nose cones, which were returned to Earth by parachute. (The writer [NMS] during his Army service at Fort Bliss, El Paso, TX in 1946-47 was doubly privileged. First he was part of a group of GIs assigned to search for a missing instrument package in its nose cone. Then, in Spring 1947, as a Post newspaper reporter, he interviewed Dr. Wernher von Braun - the guru of post WWII rocketry - and was present during a V-2 launch. Little did I realize then that Space would become my career.) Below is an example of one of the first photo pictures returned from a V-2 firing, along with a list of specific localities recognizable in this view covering 800000 square miles of the western U.S. and showing the Earth's curvature:

Both American and Soviet rockets pushed into outer space - reaching various suborbital altitudes - but for more than 10 years had insufficient thrust to achieve orbit. The modern Space program is held by many historians to truly have begun with the launch and orbit of Sputnik I by the Soviets on October 4, 1957 (like many noted events that stick in one's memory, the writer recalls vividly exactly where he was as the news was read over a radio while he was eating breakfast in a cafeteria in Casper, Wyoming at the start of a day of geological field work). Here is a full scale model of the first Sputnik (about the size of a basketball, weighing 83 kg [182 lb], with radio and one scientific instrument), on display at the National Air and Space Museum in Washington, D.C.:

This tiny satellite was hurled into space by the Semiorka rocket, seen below. Its presence, which was a huge propaganda coup, was revealed by a steady series of beeps from its radio. An interesting perspective on the world-stunning effects of this pioneering launch can be read at this Web site.

The Soviet program was led by Sergei Korolev. Korolov's identity was kept secret for almost 30 years. Not only did he plan and supervise the launch of mankind's first satellite, but his rocket wizardry was behind the launch of the first living animal, the first dogs returned from space to Earth, the first cosmonaut (Yuri Gagarin), and the first human space walker (see below). He wished his Soviet nation would set its aim to be first on the Moon, but Poliburo bureacracy thwarted this endeavor. Korolev is one of the titans of the modern space program:

Several larger Sputniks soon followed, each with scientific payloads. The U.S. launched its first orbiting satellite, Explorer 1 on January 31, 1958, followed shortly by the Vanguard series (see page Intro-1a) for more details. For now, here is a photograph of the actual Explorer 1:

Explorer 1 was at the top of a Jupiter-C rocket. The satellite itself (it looks more like a probe) was 203 cm (80 inches) long and weighed just over 66 kilograms (30 pounds). It achieved a highly elliptical orbit: Perigee (point closest to the Earth) = 363 kilometers(224 miles) ; Apogee (farthest point) = 2552 km (1575 miles). The payload was simple but very effective: a cosmic-ray detector which responded to the suspected (but not yet sensed) circum-terrestrial stream of trapped charged particles from both the solar wind and cosmic radiation.

This is a good moment to honor three of the "Titans" of the U.S. space program. In the picture below Wehrner von Braun - the leader of the Nazi V-2 program who joined and led the U.S. rocket program (especially the Saturn V) after World War II is on the right, James Van Allen (Univ. of Iowa), is in center, and William Pickering, first Director of the Jet Propulsion Laboratory (who died March 16, 2004 at the age of 93) is on the left; they are holding a life size model of America's first satellite, Explorer 1, which Van Allen developed to explore the particles and radiation around the Earth (and in so doing, discovered the radiation belts that bear his name):

Thus began the Space Race. While the bulk of launches since 1957 have been unmanned satellites, the real prize from the prestige viewpoint was putting living creatures into orbit in space. The Soviets won that effort by orbiting the dog Laika but without returning him to Earth. This Russian "stray" was trained beforehand and survived for several hours once in orbit, only to die from overheating.

With trepidations owing to the loss of Laika, the Soviet program stilled opted to put a man (a cosmonaut) into orbit. That achievement was garnered by Yuri Gagarin on April 12, 1961. Here he is with comrades as he prepared to enter Vostok I:

The U.S. space program, now behind in the manned race, succeeded in placing Alan Shepard into suborbital flight (15 minute duration) on May 5, 1961. He rode a small capsule named Freedom, part of the Mercury series of flights. Below is a Mercury launch and a photo of Shepard's capsule after it reached the Atlantic Ocean and was retrieved by helicopter:

John Glenn, an intrepid U.S. Marine Corps pilot, won the honor of being the first American (astronaut) to fully orbit Earth on February 20, 1962.

Glenn later gained fame as a U.S. Senator and as the first senior astronaut to return to space, 36 years later, on the Space Shuttle (STS-95) on October 29, 1998.

Up to this point we have talked in general historical terms about what is often termed "space flight", and we will elaborate further on the topic on this and the next page, and obviously throughout the Tutorial. But, some of you may wish to get further insight into the mechanics and history of operating men and machines in space. Now may be an appropriate time, by reading through The Basics of Space Flight as prepared by staff of the NASA/Cal Tech's Jet Propulsion Laboratory (JPL) and clicking on Table of Contents, then reading all or selectively choose what is of personal interest.

America's space race sprung into high gear with the dramatic speech by President John F. Kennedy on May 25, 1961 commiting the U.S. to land on the Moon before the end of that decade. The American Space Program sprinted rapidly to world leadership because of that challenging goal, which was met with the landing of the Eagle module on the lunar surface in July 20 of 1969.

But, after this invaluable historical diversion, let us return to our consideration of how remote sensing contributed to the overall exploration of Earth, the planets, and the Universe beyond. After the launch of Sputnik in 1957, putting film cameras on orbiting spacecraft (both manned and unmanned) became possible. The first cosmonauts and astronauts used hand-held cameras to document selected regions and targets of opportunity as they orbited the globe. Sensors tuned to obtain black and white TV-like images of Earth flew on meteorological satellites in the 1960s. Other sensors on those satellites made soundings or measurements of atmospheric properties at various heights. The '60s also recorded the orbiting of the first communications satellites.

O-4: On TV, you are most likely to encounter a satellite remote sensing product of what kind (hint: think local news)? ANSWER

As an operational system for collecting information about Earth on a repetitive schedule, remote sensing matured in the 1970s, when instruments flew on Skylab (and later, the Space Shuttle) and on Landsat (early on, called ERTS), the first satellite dedicated to mapping natural and cultural resources on land and ocean surfaces. A radar imaging system was the main sensor on Seasat, launched in June, 1978. In the 1980s, a variety of specialized sensors - Coastal Zone Color Scanner (CZCS), Heat Capacity Mapping Mission (HCMM), and Advanced Very High Resolution Radiometer (AVHRR) among others - orbited primarily as research or feasibility programs. The first non-military radar system was JPL's Shuttle Imaging Radar (SIR-A) on the Space Shuttle in 1982. Other nations soon followed with remote sensors that provided similar or distinctly different capabilities. By the 1980s, Landsat had been privatized and a widespread commercial use of remote sensing had taken root in the U.S., France, Russia, Japan and other nations. Much of this growth was, and is still being, driven by the increasing awareness that Earth's environments are in peril from man's activities and misuses.

O-5: Where might you have seen a Landsat image before? ANSWER

The chief advantage of remote sensing from satellites over relying on aerial surveys is that a satellite is ALWAYS UP THERE whereas with aerial surveying each flight day requires considerable preparation. But the disadvantage is that coverage is usually only "infrequent" with days to a week or two between the next repeat coverage over an area owing to the orbital constraints that govern the spacing of the tracks followed by a satellite. And, with this firm cycle of coverage spacing out the time of repeat, often the satellite will pass over an area when it is cloudy, so that cloudfree conditions are serendipitous; aerial coverage can be programmed to fly only when conditions are near-optimal.

It is generally agreed that Landsat set the stage for the advent of these other satellite systems in that it demonstrated the power and versatility of multispectral imagery for observing the Earth for purposes of monitoring its natural and manmade features over time, from which the many applications of remote sensing have now become important in managing our planet's "health" and the utilization of its resources. Since 1972, six Landsats have been orbited successfully (Landsat-8 did not fly as once scheduled; plans to send it into orbit are still being evaluated). Here is the history of this highly successful program:

Preview of Remote Sensing Principles

So, how is remote sensing actually done from such satellites as Landsat, or for that matter, from airplanes or on the ground? To repeat the essence of the definition above, remote sensing uses instruments that house sensors to view the spectral, spatial and radiometric relations of observable objects and materials at a distance, typically from above them, or in astronomy, by looking out. Geophysics (mainly gravity, magnetic, and seismic surveys; also external fields) is considered by many to be a form of remote sensing. But, except for three pages in the Introduction that summarize doing geophysics measurements from space, we will confine our study in this Tutorial mainly to methods and applications of spaceborne sensors that produce images and thematic maps. Most sensing modes are based on sampling of photons (quantum particles that have a wide range of energies; a specific photon will have some energy value that has its own unique corresponding frequency in the electromagnetic (EM) spectrum.. Here is a simple EM Spectrum Chart, with different wavelength intervals named according to common usage in remote sensing (the wavelength units are in micrometers (µm); a micrometer is 1/1,000,000 of a meter.

This term EM Spectrum refers to the distribution of radiant energy as a function of wavelengths (distance in length units between successive wave crests in an oscillating sine wave [the mathematical propagation form in which light travels], which for radiation is the trace of a forward moving photon as it revolves 360° through one cycle) or their inverse, frequencies (for a sine wave oscillation, number of cycles per second) presented usually as a chart or diagram with highest frequencies (shortest wavelengths) at one end and lowest frequencies (longest wavelengths) at the other. Radiation may be continuous (no break in the range of wavelengths), its plot consisting of a sequence of all wavelengths over a spectral range whose low and frequencies are at some beginning and end values. It can also be discrete, i.e., photon energies are associated with specific, generally narrow wavelength intervals, with radiation outside these intervals being absent (these discontinuous intervals are representative of energies released when atomic or molecular species are excited in specific ways [determined by quantum physics]). Thus, chemical elements, when excited by thermal or electrical energy, give off EM radiation at discrete (particular) wavelength values unique to each element species; these may appear as lines in a spectrogram made by dispersing the radiation using a prism or diffraction grating. (The writer did his Ph.D. thesis work using an optical emission spectrograph to determine element distribution changes in the course of rock weathering into soils.)

One type of a continuous spectrum is the blackbody radiation (BBR) emitted by all bodies whose temperature is above absolute zero. A given BBR spectral plot, characterized by a total spectral interval fixed on end points of specific wavelengths, is determined by the thermal state of the object sensed. For any specific temperature, the plot curve has a characteristic peak intensity. BBR curves for three stars of differing surface temperatures illustrate this type of radiation; note that as temperatures increase the radiation intensity also increases and the peak wavelength decreases.

Note: to convert Angstroms to the more common micrometer unit (µm), multiply by 10^-4, or 1/10000

To synposize these last ideas about electromagnetic radiation, consider this diagram:

Photons are emitted from a hot source (the Sun, an electric light, etc). The spectral curve for this condition or mode is like the above BBR curves. Now this light passes through a target, in this instance a cloud containing atoms and molecules. On the right is an absorption spectrum in which the black lines are at wavelengths characteristic of elements or molecules that absorb some of the photons of specific energies (proxied by their characteristic wavelengths). At the same time, some of these photons cause atoms and molecules in the cloud to be excited such that they give off (emit) radiation at particular wavelengths, as shown in the bottom spectrum.

Most remote sensing data consist of receiving and measuring reflected and/or emitted radiation from different parts of the electromagnetic spectrum. Those parts of the spectrum most commonly sampled are the ultraviolet, visible, reflected infrared, thermal infrared, and microwave segments. Multispectral (or the closely related multiband) data consist of sets of electromagnetic radiation that individually extend over (usually narrow) intervals of continuous wavelengths within some finite parts of the spectrum; thus a sensor may detect radiation in the red, the green, and the blue part of the visible spectrum - each a discrete set, either overlapping or with gaps. Each interval makes up a band or channel identified by a color (if in the visible), a descriptive label (e.g., Near IR), or a specified range of wavelengths. The data are utilized by computer-based processing to produce images of scenes (Earth's surface and atmosphere; planets; cosmological features) or to serve as digital inputs to analytical programs (see Section 1 for a thorough examination of imaging techniques and categories of analysis).

An image (or picture, a term used mainly with photographs) is produced by radiation from point to point in an array of sampling areas making up a scene (for example, ground points) will vary depending on the reflectance, absorptance or emittance response of the various features/materials are different within an interval, and different again when other bands are examined. The sampling area (any immediate point, usually a few to a few tens of meters on a side, found somewhere in the scene) produces some level of sensed radiation that can be recorded, played back, and used to assign a gray level tone or color value adduced to a display that is a photo or electronic image monitor (e.g., a TV screen) in some position within the two-dimensional display. The relative location of each sampled area in the actual scene is reproduced along X and Y coordinates at corresponding points (forming an array of spatially distinct points called pixels (picture elements) in the display. The variations in tone (black to white) or color give rise to a picture that resembles or approximates the actual scene.

Multiband data collected by one sensor will usually show notable differences from one band to the next. The band to band response in terms of photon energy variations as a function of wavelength or frequency and of the magnitude or intensity of radiation at any sampling point in a scene can be connected to become the spectral signature for a given feature or class of materials. Different features/classes (leaves, soil, rock, buildings, etc) have differing and normally distinctive signatures. In practice, many sampling points (areas on the ground) contain more than one substance or feature, so that each such class contributes its own spectral signature data to the composite for the area - this gives rise to what is termed the mixed pixel (thus, most pixels contain inputs from several different materials and objects).

For the signature shown, the target is a field of actively growing crops - the main components are thus vegetation, soil, and moisture. The detailed spectral signature for this composite of materials is shown in the lower right. Some fraction of the incoming solar radiation is reflected towards a sensor above (on an aircraft or spacecraft). While it is now possible for a sensor system to almost duplicate the signature using the mode called hyperspectral remote sensing, in this example the broadband mode, initially the normal configuration for obtaining reflectance measurements and still in common use, is illustrated here. Thus the sensor employs bandpass filters to break the reflected radiation into discrete intervals (bands)of continuous wavelengths, each consisting of a segment of the EM spectrum (red, green, infrared, etc.). The radiation consists of photons that impign upon a plate that converts the photon energy to a voltage (photoelectric effect). At the instant of sampling this radiation, each band will have some voltage value (indicated on the dials). Assuming proper calibration of each band (channel), this voltage is a measure of the reflectance from the target composited for each spectral interval. The resulting values represent a fair approximation of the spectral signature. However, even these few values may be sufficiently distinct to establish the identity of the target. Obviously, the more bands (and narrower bandwidths), the better is the discrimination.

This topic "spectral signatures" is important, and worthy of additional discussion. Implied above is the fact that spectral signatures of different materials and classes can be quite varied and distinctive. Here are two signature plots for the three most general categories of classes - Rock/soil; Vegetation; Water - found in nature. The first plot extends only through the wavelength range of 0.4 to 1.2 µm; the second goes out to 2.6 µm (the finer detail - peaks and troughs - in the plot is smoothed out.

It should be obvious that there are fundamental differences in the signatures. Water has a low reflectance in the visible and almost no response at longer wavelengths. Rock/soil can have varying reflectance levels in the visible from high (white sandstone) to low (basalt) and also has strong reflectance beyond 1.2 µm. (A rock class curve may show notable absorption troughs at particular intervals in these longer wavelengths; these may be specific and narrow enough to serve as indentifiers of individual rock types.) Vegetation shows a small peak in the green region of the visible spectrum; actually, there appears to be a trough in the red owing to chlorophyll absorption. Vegetation produces a strong reflectance response between about 0.7 and 1.2 µm - this is diagnostic.

The question arises as to the ability of remote sensors to identify the various types of each general class (e.g., distinguishing silty from clear water; limestone from a soil; grasslands from woods). This is possible if good approximations of spectral signatures for each specific material type can be gained. To illustrate this, consider these spectral signatures obtained with a high spectral resolution field spectrometer that looked at the various species involved:

Most vegetation types had similar spectral signatures; the chief difference is in the percent reflectance (only the wheat stubble shows a notable difference). All vegetation signatures are clearly distinct from that of dirt.

The signatures shown above all came from using spectrometers capable of measuring variations in spectral response intensities over narrow intervals (resolution of a micrometer or so; these plots are what is called a hyperspectral curve). Most sensors flown so far in space have much coarser spectral resolution. Landsat is illustrative. When the reflectance values for the four bands of the Multispectral Scanner (MSS) are plotted, this crude approximation of a spectral signature results:

The principles set forth in the paragraphs above relating to multispectral remote sensing are considered in additional detail again in the Introduction.

Next, to familiarize you with some of the principal types of image products that are used to monitor and document the Earth's surface, we will now present an example of multispectral images and then a sequence of space images of an area of the United States that occupied centerstage during February of 2002:

We will illustrate these ideas by showing images representing 4 of the 7 bands acquired by the Thematic Mapper (TM), the main sensor on Landsats 4 through 7. Each image was constructed from numerical values called Digital Numbers (DNs) which correlate with the intensity of reflected or emitted radiation averaged for the spectral interval (Band) displayed; the DNs in this case range from 0 to 255 in whole number increments. Levels of gray in the resulting image range from black (DN = 0) to white (DN = 255) with shades of dark gray to very light gray associated with increasing DN values. The scene, a subset of a full Landsat TM image, shows the western shore of the Keweenaw Peninsula of northern Michigan (for this and other related images, link onto the Michigan Technological University Web site). Wavelength intervals (in micrometers) are shown; check the captions (cursor on lower right) for more information.

For Bands 1, 4, and 7, the darker (gray scale) tones in these black and white renditions represent low (intensity) reflectances whereas light tones are high reflectances. In band 6 what is measured is emitted radiation which becomes more intense (leading to lighter to white tones) with higher temperatures. Starting with Band 1, pick out certain features (a pattern of usually uniform gray tones), without concern about their identities, and find the gray tones at equivalent points in the other three images - this will give you a feel for how reflectances (or emittances in Band 6) vary as a function of wavelengths used to monitor features/classes.

Combinations of any 3 of the 7 bands on TM can be registered spatially and then each assigned to one of the three primary colors: blue, green, red to yield what is called a color composite. This can be done photographically using color filters or in a computer display in which the colors are determined by the assignment (using an image processing program) of a given band to one of three color guns in the monitor (and the remaining two bands each to the remaining colors). For the TM, the most frequently used combination is Band 2 = blue; Band 3 = Green; Band 4 = red, giving the standard false color version in which most of the reds and off-reds are the color signatures of vegetation. This is present here as a larger subset showing nearly all of the Keweenaw Peninsula:

Below is another combination applied to a smaller section of this last image using Bands 3, 3, 1 as Red, Green, and Blue (RGB) to simulate natural color. In this image, for the "fun of it" locate where this subset is in the image above and try to identify (give them names, like water, town) features you recognize.

This brief primer on the appearance of individual multispectral bands and on making color composites from combinations of three bands (or other variables) from one (or perhaps two or more) sensors designed to scan the target (Earth's surface; a galaxy, etc.) should help you to interpret images from various sensors and sources to follow in this Overview.

The MSS was the key sensor of Landsats 1, 2, and 3 and was also on 4 and 5 to retain continuity of image types for those doing multitemporal studies. However, on 1-3 there was a second sensor, almost forgotten today since it did not prove to have the versatility of the MSS. This was the RBV, or Return Beam Vidicon, a television camera that produced images much like early home television systems. One Landsat 1, there were three bands, two in the visible (red and green) and one in the near-Infrared. The resolution was, like the MSS, 80 meters. The RBV was seldom used on Landsat 2. The RBV on Landsat 3 was panchromatic (single image covering 0.5 to 0.75 µm), imaged in four quadrants, and had 30 meter resolution. This allowed merger of MSS and RBV images to give an effective higher (30 m) resolution. The "hallmark" of an RBV image is a series of small crosses ("+") called reseau marks regularly spaced as an aid in geometric (spatial) corrections. RBV pictures are hard to find on the Internet or textbooks. Here is one example showing the Grand Canyon, imaged by the first RBV:

From space the extent and width of the Grand Canyon is made obvious. But space imagery cannot capture the grandeur of this geologic wonder, as is evidenced in this ground photo.

Having surveyed some basic principles and examples of remote sensing and its products, we now move on to the aforementioned sequence of various types of imagery that relate to a major event in Utah during February, 2002. This should help you appreciate the advantages of both multiplatform, multisensor, and multitemporal data and imagery. We turn to The Salt Lake City, Utah region, site of the 2002 Winter Olympics (which were underway when this subsection was being prepared, thus accounting for why this subject was chosen).

To set the Salt Lake City area into a large, i.e., regional context, look first at this Daytime Thermal image made by the Heat Capacity Mapping Mission (HCMM):

The Great Salt Lake is the dark, elongate feature in the upper right quadrant. It is dark because, thermally, it is cool and in conventional thermal images cold features tend to be dark gray to black and warm in light gray to white. The mountain chains show up moderately dark because they are cooler - at higher altitudes - than lowlands and basins. Next to the Great Salt Lake just to its lower right is Salt lake City. The dark vertical area to SLC's right is the Wasatch Range - home of many Olympic events. The east-west chain of mountains to its east is the Uinta Mountains. Many elongate dark features, running mostly up-down in the image, are individual mountains that make up the Basin and Range tectonic and geomorphic provinces.

But, at the outset it is instructive for comparative reasons to look at the most common type of earth-surface image available prior to the Space Age: a black and white aerial photo. Here is a 1:62500 scale (see Section 10) photo of part of North Salt Lake City:

To put the "venue" of this great sports event into context with its surroundings, at a regional scale, we'll start with one of the typical aerial oblique photos taken by the astronauts on a Space shuttle mission; read the caption (click on picture) for a general description.

Now, we introduce you to a characteristic unmanned satellite image: Shown first are the four individual band images made by the Landsat 1 Multispectral Scanner (MSS; 79 m resolution) presenting a view of north-central Utah taken just 15 days after launch of ERTS-1 (the name given this satellite before it and its successors were renamed the Landsat series), on August 7, 1972. The bands are identified in the caption (note: these are somewhat degraded in quality because they were scanned from a 35 mm slide; the two IR band images are also not well-balanced in gray tones owing to rather poor tonal stretching as those in the image-processing lab were still learning how to generate good quality photo prints).

The scene below is a an early (Summer of 1972) Landsat (ERTS-1) false color image (185 km [110 miles] on a side) that helped to generate widespread interest in using satellites to monitor the Earth's surface. Images of this type are made by sensors that receive reflective light which is split into several Bands (made by subdividing both the visible and the near infrared spectrum into narrower wavelength intervals), each with different tonal intensities (gray levels) in its image. Three of the band images are then recombined (registered) photographically (or by a computer program) using red, green, and blue filters (this idea, treated very briefly here but in detail in the Introduction. The combination of bands and filters can vary, giving rise to color composites that differ in colors associated with different features depending on the band/filter pairing.

The version shown here is a composite made by projecting the MSS Band 4 (green wavelength interval) through a blue filter, 5 (red band) through green, and 7 (near-IR band) through red. The right side of the image is bright red, which is the normal color for thick forests and grasslands as rendered in a standard false color image in which we associate red with healthy vegetation that is usually very bright (high reflectance, appearing in light tones) in the near-infrared (see page I-13 in the Introduction Section for the explanation of color response and assignment). This widespread red area coincides with the high Wasatch Mountains that run east of the block-fault mountains and deserts (gray-tan tones) of western Utah. Other reds in small patches mark the farmlands of the desert plains whose potential inspired Brigham Young to settle his group in this "promised land". The Great Salt Lake occupies part of the upper scene. Lake Utah (bluer because of silt) is to its south. We challenge you to find the metropolitan area of Salt Lake City in this image.

O-6: This is a good moment to begin to associate locations and features within a space image such as Landsat with their counterparts on a map. Using a U.S. Atlas or a state map, fit the Landsat image to its equivalent map area. In addition to places mentioned above, also find these features: The small cities of Ogden, Orem, and Provo; Park City; Utah Lake; the Bingham Open Pit Copper mine in the Oquirhh Mountains; large areas devoted to agriculture; heavily forested lands; desert flats. Also, in your atlas, if it is nearly new, the shape of the Great Salt Lake may differ from that in the image; why? Finally, why is the central part of Salt Lake City (which appears as a long darker blue strip) so narrow, when the greater area of the city and suburbs seems to appear reddish? ANSWER

Below this image, we place a subscene (part of the total area covered; the image was made using a subset of data points sampled by the MSS) image of the same area made from a Landsat-7 image acquired in the late 1990s. Landsat-7 had a different version of the Thematic Mapper called the ETM+ (or Enhanced Thematic Mapper) which included a separate 15-meter resolution panchromatic mapper and improved the thermal band resolution from 120 to 60 meters. For the moment, just look it over and try to note any conspicuous differences between it and the corresponding area in the Landsat-1 image. We will take this comparison up again a few paragraphs later.

One obvious difference is a sharp tonal discontinuity along a straight, sharp boundary that is evident in the Great Salt Lake; this results from a cutoff of water circulation by the Union Pacific railroad causeway built as a pile of rocks. The area to its north is mostly saline silt deposits; these accumulate in the water owing to the railroad barrier. Beyond the tracks, the water to the south is relatively clear (less silt). The silt tones show up as blue tones since Landsat-7 has a blue band which imparts a higher reflectance of the silt rendering it in lighter tones (note in the four Landsat-1 images that this tonal lightening also is expressed in the green band).

In case you had difficulty in pinpointing the city, this next view should help. It is a Landsat-5 Thematic Mapper (TM; 30 m resolution) natural color image of the immediate urban area. However, Salt Lake City is noted for its many trees and grass lawns, so in this subscene "green" tends to mask out the street patterns and gray tones associated with industrial complexes. The image also demonstrates the improvement in detail that has transpired in the later Landsats owing to this new sensor.

These images, of course, are vertical (straight down) views. To acquaint you with looking at Earth this way, we draw upon a more familiar viewing vantage by showing this near-horizontal aerial view of the city and the Wasatch Front to its east.

As will be repeatedly demonstrated throughout the Tutorial, space imagery (in digital format) can be combined through specialized computer processing with co-registered digitized elevation data to produce what is known as a perspective view (as though you were approaching the scene in a low flying aircraft and looked ahead; much like the above aerial photo). Here is a Landsat-5 perspective of the Wasatch Front with much of Salt Lake City in the foreground.

Another Landsat perspective view from a different direction shows the location of the principal Olympics venue sites both in Salt Lake City and around Park City in the mountains to its east:

The Wasatch Mountains show up as even more imposing in this perspective view of Salt Lake City made from Shuttle Radar Topography Mission (SRTM) data, in which the vertical elevations have been exaggerated (often the custom when relief [difference in elevation] warrants emphasis):

To get a more intimate feel for the downtown part of Salt Lake City, here is two high resolution images made by the IKONOS satellite (see next page). The first, in color, shows much of the downtown (at 4 m resolution), including part of the University of Utah. The second depicts, at 1 m resolution how city blocks in this town tend to be square; the two large buildings in it can be located near the left center edge of the first image.

We can zero in on the Olympics infrastructure that has been home to more than 2500 international athletes. Again, a high resolution IKONOS color images, taken in the summer of 2001 just before the Games in February, 2002, shows the main facilities within SLC.

The part of the Olympics that includes downslope sking lies in the Wasatch Range near Park City, just east of Salt Lake City. Here is an IKONOS view which shows the ski trails, and housing to the west:

Leaning on your new found familiarity with Salt Lake City, try to find some of the features shown in the above images in this very different-appearing image. This scene was obtained during the SIR-C radar mission carried out by astronauts in 1994. Each of the three radar bands (C, L, X) were assigned color used to generate this "false color" composite (see page 8-7 ). The image is oriented with the top boundary running NE-SW; the Great Salt Lake is the black area below the top.

So far in our excursion in and around Salt Lake City, we have treated you to what might be called "pretty pictures". But, now is a good time to stress the practical use or applications of space imagery. One such use comes under the term "change detection" - determining what features or conditions in a scene have been introduced, modified, or expanded over short to long time periods. Scroll upwards now to the two Landsat scenes (full Ls-1 and subset Ls-7). There are at least three major features or categories that are different (changed) in the lower Ls-7 scene representing a time span of 27 years. Do this before advancing to the next four scenes.

Urban population is one change that you would expect of this lengthy time period. The United States has increased its citizens considerably since 1972. The West, in particular, is experiencing a population boom, both from increased childbirth and from the influx of people from the eastern U.S as well as Mexicans who have emigrated from their native country. Salt Lake City shares this trend, as is evident from this pair of Landsat images. To estimate the extent of the growth, look for street patterns in each image - the major clue is the spread of buildings as the suburbs expand away from the mountains.

The most noticeable area of growth occurs in the middle of these images (the 2001 image shows urban/suburban sections of the city in a grayish tone; this is probably due to that image being taken at a different time of the year). Note the large, irregular "scar" in light brown in the lower left quadrant of each image. This is the Bingham Canyon copper mine, located in the Oquirhh Mountains. This is the largest open pit mine in the world (note the increase in peripheral size in the 2001 image). You will see this mine again in an enlarged image subset at the bottom of Page 5-4.

The second pair of Change Detection images focuses on the southern end of the Great Salt Lake. Significant differences between the 1972 and 2001 Landsat images occur at several places.

In the 1972 scene, the peninsula of land near the bottom center is tied to the shore with all land exposed. The most obvious modification noted in the 2001 image is that the peninsula at the southern end of the Lake has become isolated (into Antelope Island) owing to the lake surface level's rise since 1972. At first, this seems counterintuitive since the ultimate fate of lakes is for them to dry up (some as rapidly as a few thousand to 20000 years). But this is not always a uni-directional process. Changes in climate from dry to wet and reverse can have measurable effects over spans of decades. In 1963, the Great Salt lake had shrunk from the hundred year average of 4200 square miles to a value of ~950 square miles. This shrinkage was the consequence of a continuing drought that began in the 1950s. By 1972, the area covered by the lake had extended to about 2500 miles². At that time there was still a land bridge to Antelope Island. By 2001, that bridge was inundated, restoring the peninsula to island status. Elsewhere in the subscenes being compared, a tongue of sandy land on the southeast corner of the lake was resubmerged by (actually before) 2001 and the lowlands adjacent to a mountain outlier in the southwest corner have become partially covered with shallow water that supports vegetation.

The Summer Olympics of 2004 are being held at the end of August in the country where a "miniature" scale Olympics was first put on almost 2400 years ago: Greece. The 2004 Olympics will be held in and around Athens, at six venues shown in this SPOT-5 image. Below it is an IKONOS small area (but high resolution) image of the main facilities at the Sports Complex (1).

Now, let's leave the specificity of a single scene (both large and small areas of coverage), used to introduce you to some of the ways in which satellite imagery can depict the Earth's surface, and return to the more general overview of what Remote Sensing is all about and can do in practical ways. In addition to regional and local scale coverage, sensing from satellites allows images to be created that can envisage the full Earth or entire continents, relying either on single looks from geostationary satellites or mosaics constructed from numerous individual scenes.. Here, for example, is the quasi-natural color view of the 48 continental U.S landmass (Courtesy Earthsat Corp, Rockville, MD) made from summer AVHRR (see page 14-2) imagery. Notice the regionally variable distribution of vegetative cover (green). (Examples of mosaics are found in Section 7 and elsewhere.)

The Tutorial will draw extensively on the Landsat satellites for the images of the Earth's surface you will see in the coming Sections, in part because there are so many outstanding scenes acquired since 1972 but also in part because the writer (NMS) spent most of his career at NASA Goddard working on data from these satellites. The RST also utilizes imagery from a variety of sensors operating from land and sea satellites launched by U.S. government and private U.S. industry and by governments and commercial firms in other countries. Most of these observe in the visible, near infrared and thermal infrared spectral intervals, but images from several radar systems are also included as examples of common space data sets.

Listed here are the principal (non-commercial) remote sensing spacecraft flown by the U.S. and other nations (identified in parentheses) along with the launch date (if more than one in a series, this date refers to the first one put successfully into orbit. These fall naturally into three Groups based on their principal applications: Land, Meteorology, Oceanography. However, many of the satellites provide useful information for more than one Group:

Group 1 - Primarily Land Observers: Landsat (1-7) (1973); Seasat (1978); HCMM (1978); RESURS (Russia) (1985); IRS(1A-1D) (India) (1986); ERS (1-2) (1991); JERS (1-2) (Japan) (1992); Radarsat (Canada) (1995); ADEOS (Japan) (1996); Terra (1999); Proba/Chris (2001)

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(Note 1: SIR-A (1981), SIR-B (1984), and SIR-C (1994) are radar systems flown on Space Shuttles; a Laser Altimeter also flew on Shuttle)

Group 2 - Primarily Meteorological Observers: TIROS (1-9) (1960); Nimbus (1-7) (1964); ESSA (1-9) (1966); ATS(g) (1-3) (1966); DMSP series I (1966); the Russian Kosmos (1968) and Meteor series (1969); ITOS series (1970); SMS(g) (1975); GOES(g) series (1975); NOAA (1-5) (1976); DMSP series 2 (1976); GMS (Himawari)(g) series (Japan) (1977); Meteosat(g) series (Europe) (1978); TIROS-N series (1978); Bhaskara(g) (India) (1979); NOAA (6-14) (1982); Insat (1983); ERBS (1984); MOS (Japan) (1987); UARS (1991); TRMM (U.S./Japan) (1997); Envisat (European Space Agency) (2002); Aqua (2002)

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(Note 2: g = geostationary) (Note 3: Nimbus also observed general land features; e.g., Nimbus 6 carried SCMR, an experimental sensor designed to obtain information on surface composition)

Group 3 - Major use in Oceanography: Seasat (1978); Nimbus 7 (1978) included the CZCS, the Coastal Zone Color Scanner that measures chlorophyll concentration in seawater; Topex-Poseidon (1992); SeaWiFS (1997)

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(Note 4: NSCAT, the NASA Scatterometer, developed at JPL and launched in 1996 by a Japanese rocket, was designed mainly for oceanographic studies but has provided valuable information applicable to meteorology and land observations.)

Commercial Satellites designed to produce imagery useful to the above Groups started to operate by the mid 1980s. Among the growing number of these privately owned satellites are: SPOT (France) (1986); Resurs-01 series (Russia) (1989; became commercial in the 1990s); Orbview-2 (U.S.)(1997) SPIN-2 (Russia)(1998); IKONOS (U.S) (1999); Quickbird (U.S) (2001); Resource21 (first 4 satellites yet to be launched); EROS A (ImageSat International; Israel) (2000).

A very good review of most of the major satellites dedicated to earth observations and their characteristics, with links (some of which no longer work [404 Not Found]) to parent Web sites, can be called up from these two sites: National Air and Space Museum and University of Wisconsin.

It helps to picture the dazzling array of operational satellites by looking graphically at launch dates and lifetime of some of those in the above list (primarily land observing satellites) through the year 1996; others since 1997 are listed on a bar chart found on the second page of the Overview.:

This impressive list convinces us that remote sensing has become a major technological and scientific tool for monitoring planetary surfaces and atmospheres. In fact, the budgetary expenditures on observing Earth and other planets, since the the space program began, now exceed $150 billion. Much of this money has been directed towards practical applications, largely focused on environmental and natural resource management. The Table below, put together in 1981 by the writer, summarizes the principal uses in six disciplines.

All of these applications are valid today, and many others have been devised and tested, some of which we introduce in other Sections of this Tutorial. The literature on remote sensing theory, instrumentation, and applications is now vast, including a number of journals and reports of numerous conferences and meetings. The great improvements in computer-based image processing, especially personal computers that handle large amounts of remote sensing data, have made robotic and manned platform observations accessible to universities, resource-responsible agencies, small environmental companies, and even individuals. Geographic Information Systems (GIS) provide an exceptional means for integrating timely remote sensing data with other spatial types of data. The GIS approach (explained in Section 15) stores, integrates, and analyzes information that has a practical value in many fields concerned with decision-making in resource management, environmental control, and site development.

The need for monitoring terrestrial systems that observe, quantify and map changing land use, search for and protect natural resources, and track interactions within the biosphere, atmosphere, hydrosphere, and geosphere has become a paramount concern to managers, politicians, and the general citizenry in developed and developing nations. This need has led to a mammoth international program to use a variety of technologies, centered on observation systems from space, to improve our ability to oversee and regulate the systems that govern Earth's effective operations. Among names associated with this concept are the International Geosphere and Biosphere Programme (IGBP) (a synopsis of which is found at this United Nations Environomental Program site) and the International Global Change Program (IGCP). These programs cover a range of research and applications that embrace primarily climate studies, oceanography, and terrestrial environment monitoring. National programs include organizations that mainly make ground measurements but the current availability of suitable satellites flown by several countries leads to a symbiotic integration of space observations and ground measurements. This diagram depicts some of the primary topical activities, as described by their acronyms.

The United States has been the kingpin in these efforts. Its chief role has been in providing many of the versatile satellites that make the critical land, sea, and air measurements on a global scale. The program began in the early 1990s under the name Mission to Planet Earth; that program was renamed Earth Science Enterprise. ESE involves many federal agencies as well as some private organizations. NASA's role, located primarily at Goddard Space Flight Center, is to operate the Earth Observing System (EOS) program which will plan, build, and launch a number of satellites, a list of these being found at this NASA Headquarters site.

Closely allied to these and other programs is a new field of the geosciences called Earth System Science. Many Universities are now offering courses and even majors in this new field of natural science.

When these various programs are examined closely, as will be done throughout Section 16 of the Tutorial, two principal areas of emphasis underlie the goals and means of IGBP and ESE: 1) the concept of Global Change, which recognizes that the Earth's natural systems are constantly modifying, with various diverse aspects such as atmospheric temperatures, air and water pollutants, and land cover interacting in often complex ways to alter environments; and 2) Global Climate, which is often the most important single component of the Earth System in controlling the changes over time and in different regions of the Earth. These modifications may be cyclical or unidirectional but generally take place slowly (almost imperceptibly over short time spans) and thus require extended, repeated coverage over years to decades using a variety of observational means (of which satellites are proving the most facile). These two Logos give URLs (which you must access separately) for these specific U.S. programs .

These programs will last well into the first decade of the 21st Century. Starting in 1998, several major platforms launched with broad complements of sensors supported by continuing operation of current sensor systems. The programs will have far-reaching impact on all nations and at least an indirect effect on all people on our planet, as they address problems and concerns tied to the environment and to resources. When coupled and integrated with other major data management and decision making approaches, GIS, ESE, and EOS should evolve into highly efficient implements for continuous gathering and processing of key elements of knowledge required to administer the complex interactions between nature and human endeavors.

If you want a preview of how some scientists apply remote sensing to monitor mankind's influence on the environment, then go to the Home Page recently added to the Internet by The Consortium for International Earth Science Information Network.

Now, on to the second page of the Overview that treats mostly the activities of remote sensing in space applications during the last 20 years.

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Primary Author: Nicholas M. Short, Sr.