Remote Sensing

Remote Sensing Tutorial

1. Introduction and History

The technology of modern remote sensing began with the invention of the camera more than 150 years ago. Although the first, rather primitive photographs were taken as “stills” on the ground, the idea and practice of looking down at the Earth’s surface emerged in the 1840s when pictures were taken from cameras secured to tethered balloons for purposes of topographic mapping. Perhaps the most novel platform at the end of the last century is the famed pigeon fleet that operated as a novelty in Europe. By the first World War, cameras mounted on airplanes provided aerial views of fairly large surface areas that proved invaluable in military reconnaissance. From then until the early 1960s, the aerial photograph remained the single standard tool for depicting the surface from a vertical or oblique perspective.

Satellite remote sensing can be traced to the early days of the space age (both Russian and American programs) and actually began as a dual approach to imaging surfaces using several types of sensors from spacecraft. In 1946, V-2 rockets acquired from Germany after World War II were launched to high altitudes from White Sands, New Mexico. These rockets, while never attaining orbit, contained automated still or movie cameras that took pictures as the vehicle ascended. Then, with the emergence of the space program in the 1960s, Earth-orbiting cosmonauts and astronauts acted much like tourists by taking photos out the window of their spacecraft.

The term “remote sensing,” first used in the United States in the 1950s by Ms. Evelyn Pruitt of the U.S. Office of Naval Research, is now commonly used to describe the science-and art-of identifying, observing, and measuring an object without coming into direct contact with it. This process involves the detection and measurement of radiation of different wavelengths reflected or emitted from distant objects or materials, by which they may be identified and categorized by class/type, substance, and spatial distribution.

In short words, Remote Sensing is the science and art of acquiring information (spectral, spatial, temporal) about material objects, area, or phenomenon, without coming into physical contact with the objects, or area, or phenomenon under investigation. Without direct contact, some means of transferring information through space must be utilised. In remote sensing, information transfer is accomplished by use of electromagnetic radiation (EMR). EMR is a form of energy that reveals its presence by the observable effects it produces when it strikes the matter. EMR is considered to span the spectrum of wavelengths from 10-10 mm to cosmic rays up to 1010 mm, the broadcast wavelengths, which extend from 0.30-15 mm.

2. Types of Remote Sensing

In respect to the type of Energy Resources: Passive Remote Sensing:

Makes use of sensors that detect the reflected or emitted electro-magnetic radiation from natural sources.

Electromagnetic Spectrum

The fundamental unit of electromagnetic phenomena is the photon, the smallest possible amount of electromagnetic energy of a particular wavelength. Photons, which are without mass, move at the speed of light-300,000 km/sec (186,000 miles/sec) in the form of waves analogous to the way waves propagate through the oceans. The energy of a photon determines the frequency (and wavelength) of light that is associated with it. The greater the energy of the photon, the greater the frequency of light and vice versa.
The entire array of electromagnetic waves comprises the electromagnetic (EM) spectrum. The waves are called electromagnetic because they consist of combined electric and magnetic waves that result when a charged particle (electron) accelerates. The EM spectrum has been arbitrarily divided into regions or intervals to which descriptive names have been applied. At the very energetic (high frequency; short wavelength) end are gamma rays and x-rays. Radiation in the ultraviolet region extends from about 1 nanometer to about 0.36 micrometers. It is convenient to measure the mid-regions of the spectrum in these two units: micrometers (µm), a unit of length equivalent to one-millionth of a meter, or nanometers (nm), a unit of length equivalent to one-billionith of a meter. The visible region occupies the range between 0.4 and 0.7 µm, or its equivalents of 400 to 700 nm. The infrared (IR) region, spans between 0.7 and 100 µm. At shorter wavelengths (near .7 µm) infrared radiation can be detected by special film, while at longer wavelengths it is felt as heat.
Longer wavelength intervals are measured in units ranging from millimeters (mm) through meters (m). The microwave region spreads across 1 mm to 1 m; this includes all of the intervals used by man-made radar systems, which generate their own active radiation directed towards (and reflected from) targets of interest. The lowest frequency (longest wavelength) region-beyond 1 m-is associated with radio waves.

Absorption Bands and Atmospheric Windows

Some types of electromagnetic radiation easily pass through the atmosphere, while other types do not. The ability of the atmosphere to allow radiation to pass through it is referred to as its transmissivity, and varies with the wavelength/type of the radiation. The gases that comprise our atmosphere absorb radiation in certain wavelengths while allowing radiation with differing wavelengths to pass through.

The areas of the EM spectrum that are absorbed by atmospheric gases such as water vapor, carbon dioxide, and ozone are known as absorption bands. In the figure, absorption bands are represented by a low transmission value that is associated with a specific range of wavelengths.
In contrast to the absorption bands, there are areas of the electromagnetic spectrum where the atmosphere is transparent (little or no absorption of radiation) to specific wavelengths. These wavelength bands are known as atmospheric “windows” since they allow the radiation to easily pass through the atmosphere to Earth’s surface.
Most remote sensing instruments on aircraft or space-based 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. When a remote sensing instrument has a line-of-sight with an object that is reflecting sunlight or emitting heat, the instrument collects and records the radiant energy. While most remote sensing systems are designed to collect reflected radiation, some sensors, especially those on meteorological satellites, directly measure absorption phenomena, such as those associated with carbon dioxide (CO2) and other gases. The atmosphere is nearly opaque to EM radiation in part of the mid-IR and all of the far-IR regions. In the microwave region, by contrast, most of this radiation moves through unimpeded, so radar waves reach the surface (although weather radars are able to detect clouds and precipitation because they are tuned to observe backscattered radiation from liquid and ice particles).

Diagram of atmospheric windows-wavelengths at which electromagnetic radiation will penetrate the Earth’s atmosphere. Chemical notation (CO2, O3) indicates the gas responsible for blocking sunlight at a particular wavelength.

Active remote Sensing:

Makes use of sensors that detect reflected responses from objects that are irradiated from artificially-generated energy sources, such as radar.

In respect to Wavelength Regions:

Remote Sensing is classified into three types in respect to the wavelength regions

1. Visible and Reflective Infrared Remote Sensing.
2. Thermal Infrared Remote Sensing.
3. Microwave Remote Sensing.
4. Bands Used in Remote Sensing

Emission of EMR (Electro-Magnetic Radiation) from gases is due to atoms and molecules in the gas. Atoms consist of a positively charged nucleus surrounded by orbiting electrons, which have discrete energy states. Transition of electrons from one energy state to the other leads to emission of radiation at discrete wavelengths. The resulting spectrum is called line spectrum. Molecules possess rotational and vibrational energy states. Transition between which leads to emission of radiation in a band spectrum. The wavelengths, which are emitted by atoms/molecules, are also the ones, which are absorbed by them. Emission from solids and liquids occurs when they are heated and results in a continuous spectrum. This is called thermal emission and it is an important source of EMR from the viewpoint of remote sensing.

The Electro-Magnetic Radiation (EMR), which is reflected or emitted from an object, is the usual source of Remote Sensing data. However, any medium, such as gravity or magnetic fields, can be used in remote sensing.
Remote Sensing Technology makes use of the wide range Electro-Magnetic Spectrum (EMS) from a very short wave “Gamma Ray” to a very long ‘Radio Wave’.

Wavelength regions of electro-magnetic radiation have different names ranging from Gamma ray, X-ray, Ultraviolet (UV), Visible light, Infrared (IR) to Radio Wave, in order from the shorter wavelengths.

The optical wavelength region, an important region for remote sensing applications, is further subdivided as follows:

Microwave region (1mm to 1m) is another portion of EM spectrum that is frequently used to gather valuable remote sensing information.

Satellite Imagery

All matter is composed of atoms and molecules with particular compositions. Therefore, matter will emit or absorb electro-magnetic radiation on a particular wavelength with respect to the inner state. All matter reflects, absorbs, penetrates and emits Electro-magnetic radiation in a unique way. Electro-magnetic radiation through the atmosphere to and from matters on the earth’s surface are reflected, scattered, diffracted, refracted, absorbed, transmitted and dispersed. For example, the reason why a leaf looks green is that the chlorophyll absorbs blue and red spectra and reflects the green. The unique characteristics of matter are called spectral characteristics.

Spectral Reflectance & Color Readability

Two points about the above given relationship (expressed in the form of equation) should be noted.

1. The proportions of energy reflected, absorbed, and transmitted will vary for different earth features, depending upon their material type and conditions. These differences permit us to distinguish different features on an image.
2. The wavelength dependency means that, even within a given feature type, the proportion of reflected, absorbed, and transmitted energy will vary at different wavelengths.

Thus, two features may be distinguishable in one spectral range and be very different on another wavelength band. Within the visible portion of the spectrum, these spectral variations result in the visual effect called COLOUR. For example we call blue objects ‘blue’ when they reflect highly in the ‘green’ spectral region, and so on. Thus the eye uses spectral variations in the magnitude of reflected energy to discriminate between various objects.

A graph of the spectral reflectance of an object as a function of wavelength is called a spectral reflectance curve. The configuration of spectral reflectance curves provides insight characteristics of an object and has a strong influence on the choice of wavelength region(s) in which remote sensing data are acquired for a particular application.

Examples of TM Imagery

Below are three examples of TM imagery in color. We refrain here from displaying any of the individual black and white TM bands because Section 1 presents and examines excellent examples of these from a subscene of Morro Bay, California.

The first TM image is a late Fall, false color (TM bands 2, 3, 5 in blue, green, and red) rendition of mountain ranges in southeastern California and western Nevada. The large valley towards the left is Death Valley, with the Panamint Range to its left. The large range near the upper right is the Spring Mountains, whose reddish tones indicate vegetation (mixed evergreens and deciduous trees). The bottom of the image includes the north edge of the Mojave Desert.

The second scene is an 80 km (50 mile) enlargement of part of a TM image covering the Sonoran Desert of northwest Mexico (a bit of the Gulf of California appears in the lower left), shown here in true color. Star and crescentic sand dunes dominate this subscene in this vast sand sea deposited over igneous lavas. The dark patches in the upper right are volcanic lavas but the dark mass to its southwest is the Sierra del Rosario, composed of granitic rocks.

The third TM image is also a subscene, about 70 km (43 miles) on a side, in west-central Mexico. Mexico City, with the largest urban population in the western hemisphere (about 30 million), appears in this false color version as the medium blue area in the upper left part of the image. Note that its area is much less than that of Los Angeles (one of the opening scenes in Section 4), indicating a high population density, i.e., crowding. The city lies at an average elevation of 2800 meters (9184 feet) astride the Neo-Volcanic Plateau, a zone that runs across Mexico and is seismically active. Just off the image to the right is a cluster of active volcanoes including the famed Popocatepetl which is over 5100 meters (almost 17000 feet).

For comparison, we reproduced a map of roughly the same area as was imaged in 1973 by the first Landsat MSS. Draw your own conclusion about the relative details seen in the TM versus the MSS. Note that the size of Mexico City was not much smaller then even though its population was just over 7.5 million. The two images, when compared, illustrate the concept of change detection.

Landsat 7 has come on line in April of 1999 after its last working predecessor, Landsat 5 has continued to operate faithfully for 15 years (since 1984). Landsat 7 has only a single instrument, called the Enhanced Thematic Mapper (ETM+). This consists of the same 6 bands in the Visible and Near Infrared as the TMs, again at 30 m resolution. The thermal band has an increase in spatial resolution by a factor of 2 – to 60 meters. The new component is a panchromatic (0.52 to 0.90 µm) black and white sensor (somewhat analogous to the RBV) which images at a 15 meter spatial resolution. The Landsat 7 program is operated jointed by NASA Goddard Space Flight Center and the U.S. Geological Survey. Here are some representative scenes.

The first scene acquired by Landsat 7 covers a part of southeastern South Dakota that includes the city of Sioux Falls. The U.S. Geological Survey’s EROS Data Center (EDC), where Landsat imagery can be inspected and ordered, lies just off the image to the right.

Part of the Landsat 7 panchromatic image of this same scene, showing a portion of Sioux Falls, with individual buildings now resolvable, is presented next.

Another urban area is seen in this quasi-natural color subscene of the “Peninsula” area south of San Francisco. At the top is the San Mateo Bridge and Foster City (just beneath its west terminus), a residential area built on extensive fill into the San Francisco Bay, thus on newly created land. The Dumbarton Bridge near the bottom right leads into Palo Alto, home of Stanford University. Note the salt pans to the left of the bridge. The lake – actually the Upper Crystal Springs Reservoir – near the bottom left lies right on top of the infamous San Andreas Fault Zone. The nearby road (a 100 feet or so higher) is Interstate 280.

Finally, an early subscene shows the Cape Canaveral area of the east-central coast, where NASA’s Kennedy Space Center (KSC) is located.

Color Discrimination based on Wavelengths of Spectral Reflectances.

IRS-IA/IB LISS I and LISSII*

*Spatial Resolution of Linear imaging self scanning (LISS): LISS-I (72.5 m) and LISS-II (36.25m)

4. Remote Sensing Methods

There are two types of remote sensing instruments-passive and active. Passive instruments detect natural energy that is reflected or emitted from the observed scene. Passive instruments sense only radiation emitted by the object being viewed or reflected by the object from a source other than the instrument. Reflected sunlight is the most common external source of radiation sensed by passive instruments. Scientists use a variety of passive remote sensors.

Radiometer

An instrument that quantitatively measures the intensity of electromagnetic radiation in some band of wavelengths in the spectrum. Usually a radiometer is further identified by the portion of the spectrum it covers; for example, visible, infrared, or microwave.

Imaging Radiometer

A radiometer that includes a scanning capability to provide a two-dimensional array of pixels from which an image may be produced is called an imaging radiometer. Scanning can be performed mechanically or electronically by using an array of detectors.

Spectrometer

A device designed to detect, measure, and analyze the spectral content of the incident electromagnetic radiation is called a spectrometer. Conventional, imaging spectrometers use gratings or prisms to disperse the radiation for spectral discrimination.

Spectroradiometer

A radiometer that can measure the intensity of radiation in multiple wavelength bands (i.e., multispectral). Often the bands are of a high spectral resolution-designed for the remote sensing of specific parameters such as sea surface temperature, cloud characteristics, ocean color, vegetation, trace chemical species in the atmosphere, etc.

Active instruments provide their own energy (electromagnetic radiation) to illuminate the object or scene they observe. They send a pulse of energy from the sensor to the object and then receive the radiation that is reflected or backscattered from that object. Scientists use many different types of active remote sensors.

Radar (Radio Detection and Ranging)

A radar uses a transmitter operating at either radio or microwave frequencies to emit electromagnetic radiation and a directional antenna or receiver to measure the time of arrival of reflected or backscattred pulses of radiation from distant objects. Distance to the object can be determined since electromagnetic radiation propagates at the speed of light.

Scatterometer

A scatterometer is a high frequency microwave radar designed specifically to measure backscattered radiation. Over ocean surfaces, measurements of backscattered radiation in the microwave spectral region can be used to derive maps of surface wind speed and direction.

Lidar (Light Detection and Ranging)

A lidar uses a laser (light amplification by stimulated emission of radiation) to transmit a light pulse and a receiver with sensitive detectors to measure the backscattered or reflected light. Distance to the object is determined by recording the time between the transmitted and backscattered pulses and using the speed of light to calculate the distance traveled. Lidars can determine atmospheric profiles of aerosols, clouds, and other constituents of the atmosphere.

Laser Altimeter

A laser altimeter uses a lidar (see above) to measure the height of the instrument platform above the surface. By independently knowing the height of the platform with respect to the mean Earth’s surface, the topography of the underlying surface can be determined.

Spectral Signatures

A primary use of remote sensing data is in classifying the myriad features in a scene (usually presented as an image) into meaningful categories or classes. The image then becomes a thematic map (the theme is selectable e.g., land use, geology, vegetation types, rainfall). A farmer may use thematic maps to monitor the health of his crops without going out to the field. A geologist may use the images to study the types of minerals or rock structure found in a certain area. A biologist may want to study the variety of plants in a certain location.

For example, at certain wavelengths, sand reflects more energy than green vegetation while at other wavelengths it absorbs more (reflects less) energy. Therefore, in principle, various kinds of surface materials can be distinguished 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 of the amount of radiation reaching the surface). Using reflectance differences, the four most common surface materials (GL = grasslands; PW = pinewoods; RS = red sand; SW = silty water) can be easily distinguished, as shown in the next figure.

When more than two wavelengths are used, the resulting images tend to show more separation among the objects. Imagine looking at different objects through red lenses, or only blue or green lenses. In a similar manner, certain satellite sensors can record reflected energy in the red, green, blue, or infrared bands of the spectrum, a process called multispectral remote sensing. The improved ability of multispectral sensors provides a basic remote sensing data resource for quantitative thematic information, such as the type of land cover. Resource managers use information from multispectral data to monitor fragile lands and other natural resources, including vegetated areas, wetlands, and forests. These data provide unique identification characteristics leading to a quantitative assessment of the Earth’s features.

Introduction to Remote Sensing for Agriculture

Remote sensing is the ability to measure the properties of an object without touching it. Almost all of the applications of remote sensing to date have been based on observing crops in distinct areas of the electromagnetic spectrum. The spectrum is represented in the figure below. Agricultural remote sensing is commonly done in the visible, near-infrared and thermal infrared portions of the spectrum; however, new applications in the microwave area are under development.

Regions within the Visible and Infrared Spectrum

Visible Infrared

• 0.40-0.45 um Violet 0.7 – 3.0 um Near-Infrared
• 0.45-0.50 um Blue 3.0 – 14 um Thermal-Infrared
• 0.50-0.55 um Green 14.0 – 1000 um Far-Infrared
• 0.55-0.60 um Yellow
• 0.60-0.65 um Orange
• 0.65-0.70 um Red

Measurements

The amount of energy radiating from a surface in a particular portion of the spectrum is measured by an instrument called a radiometer. Radiometers can be hand held for research purposes and monitoring small field plots or placed on board aircraft and satellites to survey entire fields, farms, or agricultural regions. The amount of radiation from an object (called radiance) is influenced by both the properties of the object and the radiation hitting the object (irradiance). When the sun is used as the source of irradiance, the irradiance is not constant (varying with the time of day and atmospheric conditions); therefore, the radiance of an object is not necessarily a good indicator of physical properties. Instead, the apparent reflectance of an object is best used to learn about its properties. Reflectance is the ratio of the radiance from an object to the irradiance reaching the object. The reflectance of an object can be impacted by the angle you are looking from and the angle of the sun (for more information on this, see the section on bi-directional reflectance). The radiometer has special filters that can be designed so that only radiation from a specific part of the spectrum is measured.

Spectral Characteristics

The usefulness of the visible and infrared portions of the spectrum can be seen by comparing the typical spectral responses of a crop canopy and a bare soil.

Notice the following features of the curves above:

• Peak in crop curve around 550 nm — this corresponds to the color green. That is why plants look green to our eye, as this is where they reflect the most radiation in the visible part of the spectrum.
• Dip in the crop spectra around 690 nm — this corresponds to the color red and is primarily due to chlorophyll absorption.
• The crop canopy is lower in the red compared to the soil but much higher in the near-infrared (NIR). The structure of the leaves account for this relative increase in reflectance in the near-infrared.

Because of these properties, measurements of reflectance in the red and NIR can be used to determine differences in crop canopy densities. The response of vegetation in the red and NIR have been used to form “Vegetation Indices,” which typically involve some ratio of near-infrared to red reflectance.

6. Pixels and Bits

Using radio waves, data from Earth-orbiting satellites are transmitted on a regular basis to properly equipped ground stations. As the data are received they are translated into a digital image that can be displayed on a computer screen. Just like the pictures on your television set, satellite imagery is made up of tiny squares, each of a different gray shade or color. These squares are called pixels-short for picture elements-and represent the relative reflected light energy recorded for that part of the image.

This weather satellite image of hurricane Floyd from September 15, 1999, has been magnified to show the individual picture elements (pixels) that form most remote sensing images. (Image derived from NOAA GOES DATA)

Each pixel represents a square area on an image that is a measure of the sensor’s ability to resolve (see) objects of different sizes. For example, the Enhanced Thematic Mapper (ETM+) on the Landsat 7 satellite has a maximum resolution of 15 meters; therefore, each pixel represents an area 15 m x 15 m, or 225 m2. Higher resolution (smaller pixel area) means that the sensor is able to discern smaller objects. By adding up the number of pixels in an image, you can calculate the area of a scene. For example, if you count the number of green pixels in a false color image, you can calculate the total area covered with vegetation.

How does the computer know which parts of the image should be dark and which one should be bright? Computers understand the numeric language of binary numbers, which are sets of numbers consisting of 0s and 1s that act as an “on-off” switch. Converting from our decimal system to binary numbers, 00 = 0, 01 = 1, 10 = 2, 11 = 3. Note that we cannot use decimal numbers since all computers are fussy-they only like “on” and “off.”

For example, consider an image that is made up of 8 columns by 5 rows of pixels. In this figure, four shades are present: black, dark gray, light gray and white. The darkest point is assigned the binary number 00, dark gray as 01, light gray as 10, and the brightest part the binary number 11. We therefore have four pixels (B5, C4, D7 and E2) that the spacecraft says are 00. There are three dark gray pixels (B3, C2, C6 and E6) assigned the binary number 01, three light gray pixels (D3, D6 and E5) that are binary number 10, and 29 white pixels are assigned the binary number 11.

Four shades between white and black would produce images with too much contrast, so instead of using binary numbers between 00 and 11, spacecraft use a string of 8 binary numbers (called “8-bit data”), which can range from 00000000 to 11111111. These numbers correspond from 0 to 255 in the decimal system. With 8-bit data, we can assign the darkest point in an image to the number 00000000, and the brightest point in the image to 11111111. This produces 256 shades of gray between black and white. It is these binary numbers between 0 and 255 that the spacecraft sends back for each pixel in every row and column-and it takes a computer to keep track of every number for every pixel!

7. Color Images

Another essential ingredient in most remote sensing images is color. While variations in black and white imagery can be very informative, the number of different gray tones that the eye can separate is limited to about 20 to 30 steps (out of a maximum of about 200) on a contrast scale. On the other hand, the eye can distinguish 20,000 or more color tints, enabling small but often important variations within the target materials or classes to be discerned.

Since different bands (or wavelengths) have a different contrast, computers can be used to produce a color image from a black and white remote sensing data set. Remember, satellites record the reflected and emitted brightness in the different parts of the spectrum, as is demonstrated in the figure above.

Similar to the screen on a color television set, computer screens can display three different images using blue light, green light and red light. The combination of these three wavelengths of light will generate the color image that our eyes can see. This is accomplished by displaying black and white satellite images corresponding to various bands in either blue, green, or red light to achieve the relative contrast between the bands. Finally, when these three colors are combined, a color image-called a “false color image”-is produced (it’s called “false color” because colors are assigned that we can see and easily interpret with our eyes).

In order to understand what the colors mean in the satellite image, we must know which band (or wavelength) is used for each of the blue, green and red parts of the computer display. Without detailed knowledge of how each band has been changed for contrast and brightness, we cannot be sure why the colors are what they are.