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Remote sensing terminology
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Remote sensing terminology

Remote sensing

Remote sensing is the science of acquiring information about the Earth's surface without actually being in contact with it. This is done by sensing and recording reflected or emitted electromagnetic energy and processing, analyzing, and applying that information.

Electromagnetic radiation

Remote sensing requires an energy source to illuminate the target. This energy is in the form of electromagnetic radiation. Electromagnetic radiation consists of electromagnetic waves characterized by their wavelength and frequency. The sensors used in ENDELEO detect objects on the earth surface illuminated by the sun.

Electromagnetic spectrum
The electromagnetic spectrum ranges from the shorter wavelengths (including gamma and x-rays) to the longer wavelengths (including microwaves and broadcast radio waves). Only a small part of the spectrum, the visible light, can be sensed by the human eye. To record the energy in other parts of the spectrum, specific sensors are required.


Electromagnetic spectrum

The images used in ENDELEO monitoring tool are all acquired in the visible and infrared part of the spectrum.

Spectral response
When electromagnetic radiation interacts with the Earth’s surface part of it is absorbed or transmitted, while another part is reflected back into the atmosphere. The proportion of the incoming radiation that is absorbed, transmitted, or reflected depends on the wavelength of the incoming radiation and on the characteristics of the target. Remote sensing can be used to measure the energy that is reflected (or emitted) by targets on the Earth's surface over a variety of different wavelengths. This way the spectral response of a target can be measured. By comparing the response patterns of different features we may be able to distinguish between them, where we would not have been able to do so if we were comparing them using only one narrow spectral band . For example, water and vegetation may reflect somewhat similarly in the visible wavelengths but are almost always separable in the infrared. Water absorbs most of the incoming infrared wavelengths, while green vegetation reflects most of the infrared wavelengths. As a consequence in an infrared image water appears dark (low reflectance values) and vegetation appears bright (high reflectance values).

Spectral bands
Remote sensing sensors consist of different spectral bands that measure a certain range of wavelengths within the electromagnetic spectrum. For example the SPOT-VEGETATION sensor consists of four bands: a blue band, a red band, a Near Infrared (NIR) band, and a Shortwave Infrared (SWIR) band.

Spectral resolution
Spectral resolution describes the ability of a sensor to define fine wavelength intervals. The finer the spectral resolution, the narrower the wavelength range for a particular spectral band.
Some features, for example water and vegetation, are easily distinguishable as their spectral response is very different. In other cases, for example, when we want to separate different types of vegetation, the spectral response might be much more similar. Therefore, bands with fine wavelength ranges will be required to separate these different vegetation types.

Spatial resolution
The detail discernible in an image is dependent on the spatial resolution of the sensor and refers to the size of the smallest possible feature that can be detected. For example, a sensor with a spatial resolution of 30 meters will be able to detect objects measuring 30 meters or larger. Images where only large features are visible are said to have coarse or low resolution. For example, images acquired by SPOT-VEGETATION have a spatial resolution of 1000 meters. In fine or high resolution images, small objects can be detected. For example, Landsat images have a spatial resolution of 30 meters. Sensors with low spatial resolution often have a higher temporal resolution than high spatial resolution sensors and are therefore better suited for continued vegetation monitoring over large areas.

Pixels
Remote sensing images are composed of a matrix of picture elements, or pixels, which are the smallest units of an image. In most cases, the size of the pixels is set to the spatial resolution of the sensor.

Temporal resolution
The temporal resolution of a sensor refers to the ability to collect images of the same area of the Earth’s surface at different periods. The higher the temporal resolution, the more frequent images of the same area can be captured.
Spectral characteristics of features may change over time and these changes can be detected by collecting and comparing multi-temporal imagery. For example, during the growing season, most species of vegetation are in a continual state of change and our ability to monitor those subtle changes using remote sensing is dependent on when and how frequently we collect imagery. By imaging on a continuing basis at different times we are able to monitor the changes that take place on the Earth's surface, whether they are naturally occurring (such as changes in natural vegetation cover or flooding) or induced by humans (such as urban development or deforestation).

Source:

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Sensors

SPOT-VEGETATION

The first SPOT-VEGETATION instrument was launched aboard the SPOT 4 satellite on the 24th of March 1998; the SPOT-VEGETATION2 instrument is operational from the SPOT5 satellite since 1st February 2003. The sensor covers the whole earth daily at 1 km resolution in 4 spectral bands: blue, red, near infrared and shortwave infrared. It is mainly designed for decision support in the fields of agriculture and early warning, forest monitoring and management of natural resources.
Ten-day SPOT-VEGETATION synthesis products over three months old are distributed free of charge by VITO (http://free.vgt.vito.be). VITO also calculates different value-added products (vegetation indices, biophysical parameters, etc.) and distributes these data to the users (e.g. GMFS, Marsop, DevCoCast and ENDELEO).

MODIS

The MODIS (Moderate-resolution Imaging Spectroradiometer)-TERRA sensor is operational since 1999. The instrument has 36 spectral bands and covers the world’s land surface in 1 to 2 days. The TERRA-MODIS sensor is designed to help better understand global land dynamics. Depending on the band, the resolution varies from 250m to 1km. Bands necessary to calculate the ENDELEO vegetation indicators are at 250m resolution.
NASA freely provides daily, weekly and bi-weekly MODIS data, from raw images to Level3 (value-added) products. In the frame of the JRC-MARSOP project VITO also generates 10-daily MODIS products at 250m resolution.

Landsat

Landsat is the first satellite designed specifically to monitor the Earth's surface. Landsat-1 was launched by NASA in 1972. Since that time, this highly successful programme has collected an abundance of data from around the world. Landsat-1 was followed by Landsats-2, -3, -4, -5, and -7. Landsat-6 unfortunately failed to reach orbit.

The main sensors on the Landsat satellites are the Multispectral Scanner (MSS), the Thematic Mapper (TM), and the Enhanced Thematic Mapper (ETM+). MSS is available on Landsat-1, -2, -3, -4, and -5. It contains four spectral bands (green, red, and two Near Infrared (NIR) bands) with a spatial resolution of 79 meters. The TM sensor is available on Landsat-4 and -5 and contains seven spectral bands. The ETM+ sensor is available on Landsat-7 and contains eight spectral bands. Overview of the different TM and ETM+ bands and their spatial resolution is provided below. Landsat image cover an area of 185 by 185 kilometers.

Spectral bands on the TM and ETM+ sensor


Band

Wavelength range

Spatial resolution (meters)

TM1, ETM+1

Blue

28.5

TM2, ETM+2

Green

28.5

TM3, ETM+3

Red

28.5

TM4, ETM+4

Near IR

28.5

TM5, ETM+5

Short Wave Infrared

28.5

TM6, ETM+6

Thermal IR

120 (TM) and 60 (ETM+)

TM7, ETM+7

Short Wave Infrared

28.5

ETM+8

Panchromatic

14.25

Since May 2003, a defect of the scan line corrector of the ETM+ sensor results in gaps occurring in the Landsat images. The gaps in the data form alternating wedges that increase in width from the center to the edge of a scene. Nevertheless, the images containing data gaps remain useful for many applications such as land cover change detection.   

Archived Landsat data can be accessed free of charge through following link: http://glovis.usgs.gov/

ASTER

The ASTER sensor was developed by NASA and Japan’s Ministry of International Trade and Industry. It contains three subsystems operating in different spectral regions: the Visible and Near Infrared (VNIR) subsystem, the Short Wave Infrared (SWIR) subsystem, and the Thermal Infrared (TIR) subsystem. The VNIR subsystem incorporates three spectral bands with a spatial resolution of 15 meters: a green, red, and near infrared band. The SWIR subsystem incorporated six spectral bands with a spatial resolution of 30 meters. The TIR subsystem operates in five spectral bands in the thermal IR region with a spatial resolution of 90 meters.

ASTER images cover an area of 60 by 60 kilometres.

SPOT HRVIR
The SPOT High Resolution Visible Infrared (HRVIR) sensor is available on the SPOT-4 satellite. It contains 4 spectral bands with a spatial resolution of 20 meters: a green, red, near infrared and, shortwave infrared band. Additionally it contains a panchromatic band with a spatial resolution of 10 meters. 

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Links

Kenya Atlas

Monitoring websites

GIS Data

Remote sensing tutorials

 

 
 
 
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