Principals of Remote Sensing

Name: Principals of Remote Sensing
Uploaded: 2017-07-04T15:58:20+00:00
Duration: PTM24S19
Description: Principals of Remote Sensing

Principals of Remote Sensing
A User’s Perspective Konari, Iran Image taken 2/2/2000 The Mand River and the small town of Konari nestle in the Zagros Mountains in western Iran. Jarlath O’Neil-Dunne Source: NASA

Outline What is remote sensing Applications Electromagnetic spectrum Data collection Image display Source: NASA Principals of Remote Sensing

Remote Sensing is a technology for sampling electromagnetic radiation to acquire and interpret non-immediate 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). - Dr. Nicholas Short Source: NASA Principals of Remote Sensing

Multispectral Satellite
Remotely Sensed Data Aerial Camera Multispectral Satellite Radar Satellite Hyperspectral Sensor Principals of Remote Sensing

Applications Transportation Updating road maps Asphalt conditions Wetland delineation Source: Halcon Principals of Remote Sensing

Applications Agriculture Crop health analysis Precision agriculture Compliance mapping Yield estimation Clubroot disease Source: NGIC Principals of Remote Sensing

Applications Natural Resource Management Habitat analysis Environmental assessment Pest/disease outbreaks Impervious surface mapping Lake monitoring Source: TRIC Principals of Remote Sensing

Applications National Security Targeting Damage assessment Weapons monitoring Homeland security Navigation Border disputes Source: SPOT Principals of Remote Sensing

Applications Global Transparency Weapons proliferation Environmental degradation Humanitarian crisis Independent verification © Space Imaging Principals of Remote Sensing

Electromagnetic Radiation
Irradiance Radiation Source Energy from the sun travels to Earth through space as electric and magnetic waves, or electromagnetic radiation. The range of electromagnetic radiation of various wavelengths and frequencies, extending from cosmic waves to radio waves, is known as the electromagnetic spectrum. - Notice the different wavelengths of light; the wavelength is the distance from crest to crest or trough to trough. Radiation wavelength can vary from several kilometers (such as radio waves) to billionths or trillionths of a meter, such as x-rays or cosmic rays. - Analysts are interested in how this energy interacts with the earth’s surface materials. The science of remote sensing allows analysts to detect and record this interaction. - We can then use these images to extract information and make conclusions about the subject area. The collection and recording of EMR is the basis of remote sensing. Everything from your pocket camera to airborne or satellites imaging systems are forms of remotely sensed data, the difference being that most airborne and satellite imaging systems are designed to record information in parts of the spectrum beyond the limitations of typical optical cameras and the human eye. Principals of Remote Sensing

Electromagnetic Spectrum
Thermal IR TV & Radio Cosmic Rays Visible UV Near IR X Rays Microwave 10 -7 10 -6 10 -5 10 -4 10 -3 10 -2 10 -1 1 101 10 2 10 3 10 4 10 5 10 6 10 7 10 8 This slide depicts the electromagnetic spectrum from cosmic rays to the TV and Radio waves that you receive at home. You will notice that the visible spectrum falls right about in the middle of the EM spectrum. It is a small piece of the entire spectrum... roughly around 2%. Our discussion will focus in on those energy waves of the EM that fall between .4 and .7 micro meters in length. As you can see, the cosmic rays are extremely short waves while TV and Radio waves &e longer in length. Blue um Green um Red um wavelength, m Principals of Remote Sensing

Spectral Imagery Regions
Wavelength (Micrometers) .01 .04 .07 1.0 3.0 5.0 14.00 um Ultra- Violet Visible Visile Near IR Shortwave IR Midwave IR Longwave IR Panchromatic Black & White Film Comprises 2% of EM Spectrum Color Film The visible spectrum comprises only 2% of total electromagnetic spectrum. Now remember, that the “Visible” portion of the spectrum is defined as that part that we, as human beings can see. There is in addition to the visible portion, all of the rest of the spectrum that either can presently be exploited, or may be exploited in the future through emergent technology. Some organisms can actually “see” in the portions of the EM spectrum nearest to the visible: e.g. some birds and insects can see into the U.V.; night hunting animals such as owls, badgers, and cats can see into the near I.R. One very interesting fact is that our sun’s peak radiative output just happens to be in the “Visible” range of the EM spectrum, so you may draw you own conclusions regarding human evolution. Color IR Film Spectral Imagery Principals of Remote Sensing

Reflectance Blue White Light Green Red High Reflectance Here is a general model showing how, ultimately, all energy originates from the sun. It interacts with the atmosphere as it travels to the earth. After absorption, scattering and transmittance some is reflected back through the atmosphere (more scattering and interference) where it ultimately interacts with the sensor. Heat (or Thermal energy) can be generated at a source (such as a vehicle, electrical transformer or factory), or can be short-wavelength visible and NIR energy which is absorbed, and then emitted as thermal energy. As an example in a hot parking the sun’s energy is absorbed and heats up the parking lot which is latter emitted as thermal energy (long wavelength.) This is why it still gives off heat long after the sun goes down. Low 0.4mm 0.5mm 0.6mm 0.7mm Blue Green Red Principals of Remote Sensing

Spectral Response Curves
Principals of Remote Sensing

Spectral Response Green Reflectance NIR Reflectance © Space Imaging © Space Imaging Principals of Remote Sensing

Passive Detection Camera or sensor irradiance scattering reflectance emittance The science of remote sensing allows us, the analysts, to detect and record the changes caused by the interactions of EMR on various surface materials . This energy/matter interaction results in a unique pattern of electromagnetic radiation. Satellites record these patterns using sensors that are sensitive to particular parts of the electromagnetic spectrum. We can then use these images, to extract information about the subject areas. One sensor - two or more separate images recorded from different parts of the electromagnetic spectrum. Each part of the spectrum recorded is called a band. These bands are sensor-specific. Three key points to remember are: - Data is collected simultaneously in defined bands of the spectrum. - Each object or material has a unique spectral signature. - Through processing, selected bands are processed to discriminate the materials of interest. absorptance transmittance Principals of Remote Sensing

Sensor array Lens 225 214 199 198 202 176 Each “cell” recorded as a “digital number” (DN) or “brightness value” Measures amount of EM radiation The brighter the signal, the higher the value. - Each sensor concentrates on a limited range of electromagnetic energy. - A single satellite sensor typically contains numerous, individual detector elements. Each of these elements record a single measurement of electromagnetic radiation for each geographic area that it scans. - This DN is the average measurement of electromagnetic radiation from all material within that cell. - Each cell of an image or “pixel” (short for picture element) represents the “footprint” or ground area covered by the field of view of a single detector element, and is the smallest part of an image. One can think of a digital image as a matrix of these tiny, pixels arranged in regular rows and columns - Let’s consider this in greater detail (lead-in to next slide) Principals of Remote Sensing

Pixels i columns 114 109 101 97 j rows - The pixels in digital imagery have a uniform, set range of possible numerical (DN) values, based on that particular system’s image processor memory, measured in bits. The number of values which can be expressed by a set of bits is 2 to the power of the number of bits used. The most common RSI satellites currently in use (i.e., Landsat, SPOT, IRS, etc.) are based on 8 bit data systems. Hence, their possible range of values (DNs) is 0 to 255 (numerically expressed as 2 to the 8th power = 256). In comparison, upcoming high resolution satellites (such as Ikonos and Quick Bird ) will be based on 11 bit systems that will have brightness value range of 2,048 (2 to the 11th power) - Digital images may be described in strictly numeric terms on a three-coordinate system with x and y locating each pixel and z describing the brightness value. The 4x4 matrix shown above represents a record of brightness values recorded for that portion of the image. - The brightness values are depicted in shades of gray. The higher the DN the brighter (or whiter) the pixel appears. Conversely, the lower DNs appear darker (or blacker). Obviously, the more levels of gray, or range of brightness values (DNs), the better the image can be depicted. i x j = 4 x 4 = 16 pixels Each cell is called a “picture element”, or pixel Each pixel represents a single brightness value for a specific geographical area Principals of Remote Sensing

Bands 50 Grass R E F L C T A N (%) 40 Concrete 30 20 10 BLUE GREEN GREEN RED 0.8 1.3 Wavelength (micrometers) 0.4 0.5 0.6 0.7 Visible Near IR Principals of Remote Sensing

Atmospheric Windows 1 2 3 4 5 7 6 Landsat TM Wavelength, mm Ultra Violet Visible Near -Mid Infrared Thermal IR Principals of Remote Sensing

Band Placement Here is an example of spectral curves for various materials, and how they are sensed using LANDSAT and IKONOS. It is interesting to note the additional amount of spectral information which LANDSAT contributes, due to the two mid-IR bands (as well as the thermal, which is way off to the right). Water, reflects relatively high in blue, and then tapers off, to the point where it doesn’t reflect at all in the IR regions (except turbid water, where the additional suspended sediment causes it to reflect highly across the visible part of the spectrum, and partly into the NIR). Vegetation has a small peak where it reflects visible green light, and small dips where it absorbs blue and red light (for use in photosynthesis). It then jumps up in the NIR and mid IR where the internal structure of leafy vegetation reflects highly. Dry soil reflects highly in the visible wavelengths (due to its bright color) and uniformly across the IR spectrum, due to the minerals. Muck soil contains a lot of organic matter, so it is dark in the visible part of the spectrum, and the moisture in it causes it to reflect poorly in the IR spectrum. Principals of Remote Sensing

Band Display BLUE GREEN RED Band 1 Band 2 Band 3 NEAR IR SHORT WAVE IR MID- WAVE IR NEAR IR Band 4 Band 5 Band 7 Principals of Remote Sensing

Color Theory All colors created from additive primary colors: Red Green Blue Complementary colors: Magenta Yellow Cyan Red Green Blue M C Y W Black Principals of Remote Sensing

Multispectral Display
Band Composite Output = Color Guns = Band Combination = 7 4 2 (LANDSAT) BLUE GREEN RED NEAR IR SHORT WAVE IR MID- LONGWAVE IR 1 Landsat TM Band 2 3 4 5 7 6 Principals of Remote Sensing

Individual Landsat Bands
Applied to Color Guns Resulting Image Band 3 Visible Red Band 2 Visible Green Band 1 Visible Blue

Individual Landsat Bands
Applied to Color Guns Resulting Image Band 4 Near Infrared Band 3 Visible Red Band 2 Visible Green

Sensor Properties Spatial resolution Spectral resolution/# bands Radiometric resolution Temporal resolution Source: NASA Principals of Remote Sensing

Spectral Resolution/# Bands
2 3 1 Visible Band 4 5 7 6 Near IR SWIR LWIR Multi- spectral NIR SWIR SWIR LW IR 100s of Bands Hyper-spectral 1000s of Bands Ultra- spectral Principals of Remote Sensing

Spectral Resolution/# Bands

Radiometric Resolution

Temporal Resolution “Scanner” “Starer” Sensor Aperture 1 ms 1 minute 1 ms 1 ms 1 ms Principals of Remote Sensing

Temporal Resolution Orbit 2, Day 1 Orbit 1, Day 1 Orbit 2, Day 8 Orbit 1, Day 8 Landsat 185 Km 2752 Km at the Equator Principals of Remote Sensing

Trade-Offs Aerial Photo IKONOS Landsat © Space Imaging Spatial Resolution ½ m 4m 30m # Bands 1 4 7 Radiometric Resolution 8 bit 11 bit Temporal Resolution On demand 3-4 days 16 days Principals of Remote Sensing

The End Source: Space Imaging Principals of Remote Sensing

Principals of Remote Sensing

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