Landsat 8 is an Earth observation satellite built, launched and operated by a collaboration of NASA and USGS. Data survey is performed by two main sensors which are adjusted into prescribed bands. The satellite operates in visible light, near InfraRed; ShortWave InfraRed to Thermal (LongWave) infrared. The bands are pre-set to 11 bands in total differed by the wavelength of their vision.
One of the sensors – Operational Land Imager (OLI) uses 9 bands in the spectrum of visible light and Near Infrared. The second sensor – Thermal InfraRed Sensor (TIRS) operates in the range of the Infrared frequencies – Longwave InfraRed Light. The satellite provides images with a moderate resolution which varies in different bands from 15 m per pixel in the most accurate to 100 m in the LongWave Infrared where accuracy is not vital.
While monitoring the land cover it is possible to select one or several bands in order to create a clearer picture due to the specific needs for different kinds of researches it is possible to use False Color Images for enhancing the visual appearance of the data. The opportunity given is to substitute the true color of the image with the color required.
Band 2, 3 and 4 (Blue, Green and Red filters respectively) all together these filters are creating a true color band combination or normal RGB picture of the visible light. The basic aim of these filters is to create a visual map of the area.
The picture seen below is a normal satellite mapping photo.
Bands 4, 3, 2 – agricultural farms around Munster, Germany. Dark Green in the picture indicates woods, greens are the healthy plantations, pale yellow or pale green are recently harvested fields, brown and yellow are unhealthy plants or just planted fields. The band is also used for urban researches
Band 8 is panchromatic or, basically, black and white is collecting as many specters as possible in one channel which helps it to make sharper images than any other band. Due to the sensor sees more light its resolution is 15 m per pixel compared to 30 m in others. When more precise pictures are required for the research it is possible to sharpen the photo by adding the band into the combination. With pansharpening of a panchromatic image of higher resolution with multispectral images of lower resolution allows to improve the resolution of the latter and increase their informativeness.
Band 8 image of the agricultural fields in Saudi Arabia.
Band 5 (Near InfraRed) – is very important in terms of ecological monitoring as the Near InfraRed is the spectrum reflected from water contained in plants. Combination of bands 3, 4, 5 is used to monitor plants and forests more precisely than simply monitoring the greenness picture. This band combination is also useful for drainage and soils monitoring during the crops studies.
Bands 5, 4, 3 where 5 is false colored to the scarlet, band 4 is green and band 3 is blue. Vegetations near Munster, Germany are more visible in the spectrum rather than in standard RGB. The red color indicates plantations (coniferous forests are reflected in darker red or even brown compared to deciduous), green-blue color – urban areas, while water bodies are close to black color.
Band 1 (Deep Blue/Violet) –the sensor band in this operation mode is able to collect deep blue color and as a result, helps to monitor coasts as well as to distinguish the dust particles or smoke in the air.
Tianjin, China port image taken in band 1. Greynes of the picture indicates sea depth. The deeper color is the deeper sea level. In the middle of the scene, there are several shipyards.
Bands 6 and 7 use different parts of shortwave infrared and are helpful in terms of monitoring rocks and soils. As the spectrum is almost fully absorbed by water it easily reflects the water sources when analyzing the image. These bands are also employed for ecological and geological researches due to their properties. The geological band combinations allow specifying the areas of interest for future geological study.
Picture of band 7, 6, 4 Munster, Germany. Green color indicates the vegetation. The dark-blue or black highlights water sources. Towns and urban areas are spread within white to cyan to purple ranges. In case the image indicates red this would mean the sensor has taken the picture of the volcano, forest fire or solar panel field which as was mentioned above are reflecting or radiating the whole spectrum of IR.
Band 9 is the “blindest” of the Landsat bands yet is no less interesting. This band covers a very narrow range of frequencies and taken the most of them are absorbed by the atmosphere anything in this band seen brightly is considered to be above the atmosphere. As the Earth is not visible in the spectrum, this band aims to identify clouds and helps to filter them off when required.
Band 9 Image of the desert to the west of Fayum, Egypt.
Band 10 and 11 are Thermal InfraRed bands and they can see heat. Unlike meteorological stations which can monitor the air temperature, these bands combination reflects the temperature mapping of the ground surface which is usually hotter than the air. This thermal bands combination are the most inaccurate ones due to heat dispersion (the pixel resolution is only 100 meters), but they proceed more information in false coloring. This feature is used for soil moisture and evapotranspiration assessment and therefore for water application rate defining.
Band 10 and 11 combination with the ability to see the heat dispersion around Fukushima, Japan. In general, the higher the temperature of the underlying surface, the higher intensity of radiation in the bands 10 and 11. The lighter the color, the hotter the temperature in the area is. The line between the white and grey in the center of the image is the shoreline. Clouds usually have a lower temperature and can be also detected. The colder the temperature of cloud surface, the lower the intensity of radiation, and the darker they are.
Petro Kogut has a PhD in Physics and Mathematics (1998). He successfully defended two dissertations: “Stability and Optimal Stabilization of Neutral Integro-Differential Equations” (1989) and “Stability and Optimal Stabilization of Neutral Integro-Differential Equations, Homogenization of Optimal Control Problems for Systems with Distributed Parameters” (1998).
He is the author of multiple scientific publications, including “Variational Model with Nonstandard Growth Conditions for Restoration of Satellite Optical Images via Their Co-Registration with Synthetic Aperture Radar”.
Dr. Kogut has received two grants: International Fund of Fundamental Investigations - “Vidrodzhennia” (1996) and Ukrainian Fund of Fundamental Investigations (1997).
In 1996, he became the Soros Associated Professor. A year later, he received The First Prize of National Academy of Science of Ukraine for his research in homogenization theory of optimal control problems.
Dr. Kogut has received an honorary decoration, “Excellence in Education of Ukraine” (2014) and the medal of A. M. Makarov, “For significant merits” (2019).
Since 2014, Petro has been the head of the department of differential equations in the Oles Honchar Dnipro National University.
Some of Dr. Kogut’s hobbies include fishing and woodworking.
Dr. Kogut provides scientific advice to EOS Data Analytics.