What is Remote Sensing?

“Remote Sensing is the art, science, and technology of obtaining reliable information about physical objects and the environment, through the process of recording, measuring and interpreting imagery and digital representations of energy patterns derived from noncontact sensor systems” (American Society for Photogrammetry and Remote Sensing).

Electromagnetic Energy and the Electromagnetic Spectrum:

When you use electricity, you are using electromagnetic energy. Electromagnetic energy is the source of almost all energy for remote sensing. This energy travels in waves that are measured in wavelengths and at speeds measured by frequency. Wavelength is the mean distance between maximums (or minimums) of a roughly periodic pattern and is normally measured in micrometers (um) or nanometers (nm). Frequency is the number of wavelengths that pass a point per unit time. A wave that sends one crest by every second (completing one cycle) is said to have a frequency of one cycle per second or one hertz, abbreviated 1 Hz. Electromagnetic energy spans a broad spectrum, and thus it is called the electromagnetic spectrum and ranges from very long radio waves to very short gamma rays. A radio detects the longer waves in the spectrum, and an x-ray machine uses the shorter waves.

Source of image and further reading: http://missionscience.nasa.gov/ems/01_intro.html

The human eye can detect only a small portion of the electromagnetic spectrum called visible light (the visible spectrum), and these are the colors you may see in a rainbow. Violet has the shortest wavelength, at around 380 nanometers, and red has the longest wavelength, at around 700 nanometers.

Note that there are different measurement units for the electromagnetic spectrum. The table to the below lists some of the most commonly used metrics in remote sensing.

Measurements used in Remote Sensing

Unit

Length

Kilometer

1,000 m

Meter

1.0 m

Centimeter (cm)

0.01 or 10-2 m

Millimeter (mm)

0.001 or 10-3 m

Micrometer (um or µm)

0.000001 or 10-6 m

Nanometer (nm)

10-9 m

Angstrom unit (Å)

10-10m

We can use scientific instruments such as sensors on Earth observing satellites (EOS) to remotely capture the full range of the electromagnetic spectrum and study the Earth, which is one form of remote sensing. Earth Observing Satellite (EOS) sensors can capture multispectral or panchromatic images. A multispectral image is one that captures image data at specific frequencies across the electromagnetic spectrum. These images divide the electromagnetic spectrum into many spectral bands. Panchromatic images record the total intensity of radiation falling per pixel in an image. Some of the more commonly used EOS in the natural sciences are Landsat and MODIS. For example, the Landsat 8 satellite was launched into orbit around Earth in the year 2013. Onboard the Landsat 8 satellite are an Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS). The OLI sensor captures images of the Earth at 30 meter (30 m2) spatial resolution (see What does a remotely sensed image from an Earth observing satellite look like? section below for more information about spatial resolution) and consists of nine spectral bands, which are useful for various applications. The TIRS sensor captures the same images as the OLI sensor, however the spatial resolution is 100 m2 and it consists of two thermal bands. 

Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) Bands (source: http://landsat.usgs.gov/best_spectral_bands_to_use.php)

Band

Wavelength(µm)

Useful for mapping

Band 1 – coastal aerosol

0.43-0.45

Coastal and aerosol studies

Band 2 – blue

0.45-0.51

Bathymetric mapping, distinguishing soil from vegetation and deciduous from coniferous vegetation

Band 3 - green

0.53-0.59

Emphasizes peak vegetation, which is useful for assessing plant vigor

Band 4 - red

0.64-0.67

Discriminates vegetation slopes

Band 5 - Near Infrared (NIR)

0.85-0.88

Emphasizes biomass content and shorelines

Band 6 - Short-wave Infrared (SWIR) 1

1.57-1.65

Discriminates moisture content of soil and vegetation; penetrates thin clouds

Band 7 - Short-wave Infrared (SWIR) 2

2.11-2.29

Improved moisture content of soil and vegetation and  thin cloud penetration

Band 8 - Panchromatic

.50-.68

15 meter resolution, sharper image definition

Band 9 – Cirrus

1.36 -1.38

Improved detection of cirrus cloud contamination

Band 10 – TIRS 1

10.60 – 11.19

100 meter resolution, thermal mapping and estimated soil moisture

Band 11 – TIRS 2

11.5-12.51

100 meter resolution, Improved thermal mapping and estimated soil moisture

Earth observing satellites may be in low Earth orbit (LEO) or geostationary orbit (GEO). Most of the satellites including the Landsat and MODIS satellites used in the natural resource sciences are in LEO.

Passive Remote Sensing makes use of sensors that detect the reflected or emitted electro-magnetic energy from natural sources. If an object does not emit its own light (which accounts for most objects in the world), it must reflect light in order to be seen. For example, the painted walls in a room do not emit their own light; they reflect the light from the ceiling “lights” overhead. Emissivity of a material (ε or e) is the relative ability of its surface to emit absorbed energy by radiation. A true black body that absorbed and emitted all frequencies of energy would have an ε = 1. This type of surface does not exist on Earth, therefore objects we detect using remote sensing will have ε < 1. Emissivity is a dimensionless quantity, so it does not have units. In general, the duller and blacker a material is, the closer its emissivity is to 1. The more reflective a material is, the lower its emissivity. Active Remote Sensing makes use of sensors that emit electromagenetic energy and then detect the reflected light.

What does a remotely sensed image from an Earth observing satellite look like?

Satellite images are just like maps of the Earth’s surface. They are two-dimensional images that may be analog or digital. Images from Earth observing satellites are digital. Just like your computer screen, digital imagery is made up of tiny squares, each of a different gray shade or color. These squares are called pixels or cells, and they represent the relative reflected light energy recorded for that part of the image. Images have various spatial resolutions associated with them. Higher spatial resolution (fine scale) means that the sensor is able to discern smaller objects (image is made up of smaller pixels). Lower spatial resolution (coarse scale) means that the sensor picks up only larger objects (the image is made up of larger pixels). The digital image of Ethiopia in blue below is at 1 kilometer (km) spatial resolution. As you can see, the further we zoom into the image, the more pixels we can distinguish. Each of the pixels in the red box represent 1 km2 of Earth’s surface.

Another type of resolution that should be mentioned here is temporal resolution. Temporal resolutionrefers to how often a satellite captures a scene from the same area on the surface of the earth. For example, the Landsat 8 satellite has a temporal resolutionof 16 days. That means that every 16 days, a new image is available for a given portion of the earth’s surface.

The digital image below of Jimma, Ethiopia was taken from Google Earth. You can view satellite imagery for the entire earth using the Google Earth program and it is freely available for download here: http://www.google.com/Earth/. What do you see in the image below? Can you find the stadium, the airport, the water, and the city?

These images can be interpreted as they appear. This is also called true color view. We can also use computer software to look at an image in false color view. As discussed earlier, the human eye cannot see in the infrared portion of the electromagnetic spectrum. However, we can make infrared bands from satellite imagery appear in a color that our eyes can see on the computer screen. Additionally, we may derive indices from these images that are combinations of the spectral bands in the image. Indices were first developed from simple band ratios, which highlighted the spectral properties of vegetation at different stages of growth. Subsequent indices were produced to compensate for background effects such as bare soil, and to compensate for the effects of atmospheric distortion on an image. For example, think about how your vision is distorted when you are in a smoky area. The same thing happens with a satellite image and there are corrections that we can make to compensate for this. Advanced spectral vegetation indices have been developed; these are dimensionless measurements developed from mathematical ratios of the frequencies of electromagnetic spectrum captured by an EOS. Chlorophyll in plants absorbs energy in the blue and red wavelength bands centered around 0.45 and 0.67 µm therefore healthy vegetation appears green, and unhealthy vegetation appears yellow as this absorption decreases. In the near infrared (IR) portion of the electromagnetic spectrum (i.e. 0.7 to 1.3 µm), a healthy plant leaf reflects up to 50 percent of solar energy, with the total amount reflected greatly dependent on leaf structure. Thus, the near IR band may be used to distinguish specific plant species on the landscape based on leaf structure. For wavelength values greater than 1.3 µm, leaf reflectance is inversely related to total water content of the leaf. One of the most commonly used spectral vegetation indices is the normalized difference vegetation index (NDVI). This index is calculated using the equation: NDVI = near infrared band – red band / near infrared band + red band. Since that time, many more useful indices have been developed to aide in mapping vegetation and other land cover, such as soil adjusted vegetation index (SAVI), enhanced vegetation index (EVI), and normalized difference water index (NDWI) to name a few. These indices can be derived from remotely sensed imagery using software such as ArcGIS (http://www.esri.com/software/arcgis/arcgis-for-desktop) and ENVI (https://www.harrisgeospatial.com/SoftwareTechnology/ENVI.aspx).

The image below shows NDVI for a portion of the Afar Region in Ethiopia. The raw data used to calculate NDVI was acquired from a Landsat 8 image. This image has a 30 meter (30 m2) spatial resolution. Areas in green have a high NDVI, which means they are reflecting more in the infrared band of the electromagnetic spectrum, between the wavelengths 0.85 – 0.88 µm. Therefore, the green areas represent healthy vegetation.

More information about vegetation indices: http://www.harrisgeospatial.com/docs/AlphabeticalListSpectralIndices.html

What are the potential applications of remote sensing?

  1. Land use/land cover mapping
  2. Geologic and soil mapping
  3. Agricultural applications
  4. Forestry applications
  5. Rangeland applications
  6. Water resource applications
  7. Urban and regional planning
  8. Wetlands mapping
  9. Wildlife ecology
  10. Environmental assessments
  11. Landform mapping
  12. Archaeological applications

Where can I download free remotely sensed data?

  • EarthExplorer: This website is hosted by the US Geological Survey (USGS) and is updated frequently. Remotely sensed images may be downloaded across the globe, based on location and time. http://Earthexplorer.usgs.gov/
  • LandsatLook Viewer: This website allows the user to interactively explore the Landsat archive (years 1972 to current) using a web browser. http://landsatlook.usgs.gov/