Difference between revisions of "Remote Sensing"

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:'''ASTER''': Advanced Space Borne thermal Emission and Reflected Radiometer
:'''ASTER''': Advanced Space Borne thermal Emission and Reflected Radiometer
:'''ATSR''': Along Track Scanning Radiometer
:'''ATSR''': Along Track Scanning Radiometer
:'''AVIRUS''': Airborne Visual/Infrared Imaging Spectrometer
:'''AVIRIS''': Airborne Visual/Infrared Imaging Spectrometer
:'''CCD''': Charge Coupled Devices
:'''CCD''': Charge Coupled Devices
:'''CERES''': Clouds and Earth’s Radiant Energy System
:'''CERES''': Clouds and Earth’s Radiant Energy System

Revision as of 01:21, 5 June 2011

Remote Sensing 2010

"Participants will use remote sensing imagery, science and mathematical process skills to complete tasks related to an understanding of the causes and consequences of human interaction with forest biomes." - Remote Sensing rules 2010

Like 2009, 5 double sided sheets of paper are permitted, as well as a non-graphing calculator.

This year, the tests tend to comprise of a mix of image interpretation as well as questions regarding concepts of remote sensing and forest biome biology knowledge. Some ecology/biology background is useful. Knowledge of individual space programs and NASA satellites, in addition to the types of sensors used, is useful.

Image Interpretation

The Basics

Image interpretation and analysis is a huge part of the Remote Sensing event. It involves locating, identifying or measuring certain objects in images acquired using Remote Sensing. This isn't as straightforward as it may seem. There are plenty of features that can throw you off in each image. However, some features are the same in each image as well. There will always be a "target" to look for, which will always contrast with other parts of the image- making it "distinguishable". All images in the Remote Sensing event will be in analog format- photograph form- rather than digital.

According to the Canada Centre for Remote Sensing, whose tutorial you can find in the external links section, there are several things to look for to assist in image interpretation. These are tone, shape, size, pattern, texture, shadow, and association.

  • Tone is the brightness or color of an object. It's the main way to distinguish targets from backgrounds.
  • Shape is the shape of an object. Note that a straight-edged shape is usually man-made, such as agricultural or urban structures. Irregular-edged shapes are usually formed naturally.
  • Size, relative or absolute, can be determined by finding common objects in images, such as trees or roads.
  • Pattern refers to the arrangement of objects in an image, such as the spacing between buildings in an urban setting.
  • Texture is the arrangement of tone variation throughout the image.
  • Shadow can help determine size and distinguish objects.
  • Association refers to things that are associated with one another in photographs, which can assist interpretation, i.e. boats on a lake, etc.

The Composites

Projecting color-filtered black and white images through certain filters can yield color composites. The images you'll be dealing with in this event will most likely be composites.

To begin understanding composites, we must first understand how they are made. First, work with three black and white transparencies of the same image. Each represents a different spectral band - blue, green, and red. Shine white light through each one onto a screen. Then, project each band through a filter of the same color- blue band through a blue filter, green through a green, red through a red. Because the blue images are clear on the blue spectral band image, they'll appear blue on the composite. If you line up the three images, you'll have the natural color (or very close) of the image. You've just made a color composite. This process is called "color additive viewing."

Not all composites have to have natural colors. What would happen if you projected the red band through a green filter? Or the green band through a blue filter? If you have an infrared band as one of the transparencies and shine it through the red filter, you can make something called a "False Color (IR) Composite." You may have seen false color composites in competition. Often, they are used to show healthy vegetation compared to vegetation poor in health. They may appear the same naturally, but false color displays healthy vegetation in a much brighter tone. For example, false color composites may show a football field made up of healthy grass as a strong red color, but a football field composed of Astroturf or other artificial substances will show up as a duller red.

It's important to understand what bands correspond to what wavelengths for satellites. This link: http://geo.arc.nasa.gov/sge/health/sensor/cfsensor.html, is the best source for all of the bands of the major satellites. Another link, http://www.physicalgeography.net/fundamentals/2e.html, is good for a very brief overview of the topic of remote sensing.

Common composites:

  • True-color composite- useful for interpreting man-made objects. Simply assign the red, green, and blue bands to the respective color for the image.
  • Blue-nearIR-midIR, where blue channel uses visible blue, green uses near-infrared (so vegetation stays green), and mid-infrared is shown as red. Such images allow seeing the water depth, vegetation coverage, soil moisture content, and presence of fires, all in a single image.
    • NearIR is usually assigned to red on the image; thus, vegetation often appears bright red in false color images, rather than green, because healthy vegetation reflects a lot of nearIR radiation.

The Electromagnetic Spectrum

Energy can be emitted, transmitted, absorbed or reflected in waves when it hits a surface. This is important to remote sensing because that's how sensors detect certain data about the objects a satellite is studying. Active sensors emit radiation toward an object and measure its reflectance. Passive sensors simply use the energy already being radiated from objects without emitting any of their own.

Their are several types of energy that can be emitted, depending on their wavelength: Em spect.jpeg

It's important to know which types of energy are useful for what.

Gamma rays and x-rays cannot be used for remote sensing because they are absorbed by the Earth's atmosphere: in general, the shorter the wavelength (and the greater the frequency), the more absorption occurs.

Ultraviolet radiation is not useful either because it is blocked by the ozone layer.

Visible light allows satellites to detect colors a human eye would see. Infrared is divided into categories: near infrared, reflected infrared and thermal infrared. Near infrared is useful for vegetation, and thermal infrared is also known as heat and is emitted passively, not actively.

Microwaves are used in radar (see more in Sensors section)


During the competition, you may be asked to analyze a picture's NDVI values. NDVI stands for "Normalized Difference Vegetation Index" and is used to describe various land types, usually to determine whether or not the image contains vegetation. The equation provided by USGS for NDVI is as follows:

NDVI = (Channel 2 - Channel 1) / (Channel 2 + Channel 1)

Channel 1 is in the red light part of the electromagnetic spectrum. In this region, the chlorophyll absorbs much of the incoming sunlight. Channel 2 is in the Near Infrared part of the spectrum, where the plant's mesophyll leaf structure can cause reflectance. You may also see the equation given like so:

[math]\displaystyle{ \mbox{NDVI}=\frac{(\mbox{NIR}-\mbox{VIS})}{(\mbox{NIR}+\mbox{VIS})} }[/math]

(Where NIR is Near Infrared and VIS is Visual (Red) Light)

So, healthy vegetation has a low red light reflectance (Channel 1) and a high infrared reflectance (Channel 2). This would produce a high NDVI value. As the amount of vegetation decreases, so too does the NDVI values. The range of NDVI values is -1 to +1.

Generally, areas rich in vegetation will have higher positive values. Soil tends to cause NDVI values somewhat lower than vegetation, small positive amounts. Bodies of water, such as lakes or oceans, will have even lower positive (or, in some cases, high negative) values.

There are some factors that may affect NDVI values. Atmospheric conditions can have an affect on NDVI, as well as the water content of soil. Clouds sometimes produce NDVI values of their own, but if they aren't thick enough to do so, they may throw off measurements considerably.


EVI, or the Enhanced Vegetation Index, was created to improve off of NDVI and eliminate some of its errors. It has an improved sensitivity to regions high in biomass and its elimination of canopy background. The equation for EVI is as follows: [math]\displaystyle{ EVI= G \times \frac{(NIR-RED)}{(NIR+C1 \times RED-C2 \times Blue+L)} }[/math]

Where NIR is again Near Infrared, and Red and Blue are of course those colors' bands. All three of these are at least partially atmospherically-corrected surface reflectances. The equation filters out canopy noise through L.

EVI has been adopted as a standard product for two of NASA's MODIS satellites, Terra and Aqua. As it factors out background noise, it's often considered to be more popular than NDVI.



This is a list of some useful remote sensing vocabulary: All of this can be found in the ccrs tutorial

Absorption: when substances absorb radiation
Active sensing: giving off radiation, then sensing the backscatter
Electromagnetic radiation: most common energy source for remote sensing consisting of an electric and magnetic field perpendicular to each other and the direction of travel while traveling at the speed of light c (3.0 m/sec)
Frequency: the number of waves passing a given point in a given amount of time; measured in hertz
Image: any pictoral representing any wavelength used in sensing
Orbit: path followed by a satellite
Passive sensing: sensing naturally available radiation
Radiometric resolution: ability of sensor to discriminate very small differences in energy
Reflection: when radiation is redirected upon striking a target; this is the target interaction useful for remote sensing
Remote sensing: the science of acquiring data without being in contact with it
Scale: ratio of size on image to real-life size
Scattering (or atmospheric scattering): when particles in the atmosphere redirect radiation
Spatial resolution: smallest detail a sensor can detect
Spectral resolution: ability of sensor to distinguish between fine wavelength intervals
Swath: area imaged by a satellite with a fixed width
Temporal resolution: describes the time between which the same area is viewed twice
Transmission: when radiation passes through a target
Wavelength: the distance between two crests of a periodic

Examples of Instruments

Know what types of instruments will be used for certain applications.

RADAR: short for Radio Detection and Ranging. It transmits radio waves, which are scattered and reflected when they come into contact with something. They can pass through water droplets and are generally used with active remote sensing systems. Radar is good for locating objects and measuring elevation.
LIDAR: short for Light Detection and Ranging. It is similar to RADAR but uses laser pulses instead of radio waves.
TM: stands for Thematic Mapper. It was introduced in the Landsat program and involves seven image data bands that scan along a ground track.
MSS: stands for Multispectral Scanner. It was introduced in the Landsat program also, and each band responds to a different type of radiation, thus the name “multispectral”.

Examples of Satellites

Most of the satellites tested for are NASA-related.

A-Train: a satellite constellation scheduled to be with seven satellites working together in Sun synchronous (SS) orbit. Their compiled images can have high-resolution results.
  • Aqua: used for monitoring the water cycle.
  • Aura: measures air quality and climate.
  • CloudSat: uses RADAR to monitor clouds’ altitude and properties.
  • CALIPSO: measures materials within clouds
  • PARASOL: a satellite which studies clouds and aerosols. It has begun to leave the A-Train.
Landsat: A series of 7 satellites using multiple spectral bands. Only two are operational today (Landsat 7 and Landsat 5) These are generally the most commonly tested satellites, as well as those using the ASTER sensor.
GOES (Geostationary Operational Environmental Satellite) System: 2 weather satellites in Geostationary orbit 36000 km
SeaWiFS (Sea-viewing Wide-Field-of View Sensor): Eight spectral bands of very narrow wavelength ranges, monitors ocean primary production and phytoplankton processes, ocean influences on climate processes (heat storage and aerosol formation), and monitors the cycles of carbon, sulfur, and nitrogen.

Forest biome

The second portion of this event requires the use of knowledge of forest biomes and the interaction of humans with them.


There are three major types of forests, which are all characterized by the amounts of trees growing in them.

  • Tropical forests are near the equator. They have the greatest diversity in species, and only two seasons are present (rainy and dry).
  • Temperate forests are located in eastern North America, northeastern Asia, and western and central Europe. There are four defined seasons and a moderate climate. Precipitation (75-150 cm) is distributed evenly throughout the year.
  • Boreal forests (taiga) are in northern Eurasia and North America. There is a short, warm summer and a very long and cold winter.

Human Interaction

As humans have expanded their reign over the planet, the health of the forest biome has taken a hit. Effects of humans such as deforestation threaten the well-being of the planet, especially since forests play an important role in processes such as the water cycle, carbon cycle, and ecological diversity.


ALI: Advanced Land Imager
ASTER: Advanced Space Borne thermal Emission and Reflected Radiometer
ATSR: Along Track Scanning Radiometer
AVIRIS: Airborne Visual/Infrared Imaging Spectrometer
CCD: Charge Coupled Devices
CERES: Clouds and Earth’s Radiant Energy System
CIR: Colour Infrared
CZCS: Coastal Zone Color Scanner
EMR: ElectroMagnetic Radiation
EMS: ElectroMagnetic Spectrum
EOS: Earth Observing System
FC: False Colour
FCC: False Colour Composite
FLIR: Forward Looking InfraRed
GOES: Geostationary Operational Environmental Satellite
GPS: Global Positioning Satellite
HRV: High Resolution Visible
IFOV: Instantaneous Field of View
IRS: Indian Remote Sensing
LIDAR: LIght Detection And Ranging
LISS-III: Linear Imaging Self-Scanning Sensor
LWIR: LongWave InfraRed
LWR: LongWave Radiation
MESSR: Multispectral Electronic Self-Scanning Radiometer
MISR: Multi-angle Imaging Spectro Radiometer
MODIS: MODerate Resolution Imaging Spectroradiometer
MOS: Marine Observation Satellite
MSR: Microwave Scanning Radiometer
NDVI: Normalized Difference Vegetation Index
NIR: Near InfraRed
NOAA AVHRR: National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer
PAN: Panchromatic
PCA: Principal Components Analysis
RADAR: RAdio Detection And Ranging
RAR: Real Aperture Radar
RGB: Red, Green, Blue Colour Space
R/S: Remote Sensing
SAR: Synthetic Aperture Radar
SPOT: Système Pour l'Observation de la Terre
SWIR: ShortWave-InfraRed
TIR: Thermal Infrared
TM: Thematic Mapper OR Thermal Mapper
VTIR: Visible and Thermal Infrared Radiometer
WiFS: Wide Field Sensor

More acronyms can be found in the CCRS (Canada Center for Remote Sensing) Tutorial

<spoiler text="Remote Sensing 2009">

Remote Sensing 2009

"Participants will use remote sensing imagery, science and mathematical process skills to complete tasks related to an understanding of the causes and consequences of global warming." - Remote Sensing rules 2009

You may bring five (5) pages of double-sided paper with notes in any form. Each participant may bring any non-graphing calculator.

This event is essentially a test based on identifying satellite imagery. Be prepared to study about and memorize different NASA space programs aimed at imaging earth from space. Also, learn to identify different human constructions based on satellite photos. Test questions will often be open-ended, with answers to questions based on analysis of such satellite images in visible, infrared, and radio wavelengths. Other such images include but are not limited to charts of variation in average temperature and measure of chlorophyll concentration in the ocean. </spoiler>



Remote Sensing and Image Interpretation

Remote Sensing: Principles and Interpretation


2010 links

  • http://rst.gsfc.nasa.gov/Front/tofc.html
    • The NASA tutorial is more advanced than the Canada one, and it is recommended reading after the Canada one has already been read. Difficult to read both due to time constraints, however, most substance in this tutorial will not be necessary on most tests. Good if time permits.

Older links

Most of these links are either no longer active or not relevant to the 2010 event