5.5. Technical Terms

The EMS consist of a range of energy which contains several parts, such as the gamma ray, x-ray, ultraviolet, visible, infrared, microwave (radar), radio waves. Different parts of the EMS have different wavelengths and frequencies which travel with the same speed as the velocity of light (2.98x10^8 m/s).

The Electro-Magnetic Spectrum (EMS) is a term that refers to the full range of energy that travels in waves. Each type of energy in this spectrum has a different wavelength, which means they represent different types of electromagnetic waves. For instance, gamma rays have very short wavelengths, while radio waves have much longer wavelengths. They all travel at the same speed, which is the speed of light. This concept is crucial in remote sensing because different types of electromagnetic waves interact with the Earth's surface and atmosphere differently, leading to the acquisition of varied data.

Imagine the EMS as a rainbow extending far beyond visible colors. On one end, we have the very short wavelengths of gamma rays, which can penetrate through materials. On the other end, we have the long wavelengths of radio waves, useful for communication. Just as different colors of paint mix together to create new shades, different parts of the EMS interact with the environment to give us a rich variety of information.

Electromagnetic energy when strikes the objects on the Earth surface can be reflected/emitted, absorbed, scattered or transmitted. The part of the incident energy that is returned back from the objects and measured by the sensor is called reflected energy.

When electromagnetic energy hits an object on Earth's surface, several things can happen. The energy can bounce off (reflected), be absorbed by the object, pass through (transmitted), or be spread out in many directions (scattered). Reflected energy is specifically the part of this energy that bounces back from an object and is captured by sensors in remote sensing. The characteristics of the reflected energy can provide essential information about the surface features, material types, and even conditions of the environment.

Think of sunlight shining on a lake. Some light reflects off the surface, allowing you to see the shimmering. In contrast, if you have a sponge submerged in water, it absorbs water instead of reflecting light. Similarly, in remote sensing, the sensors detect the reflected energy to learn about the Earth’s features, just as your eyes gather information from the sunlight reflecting off various surfaces.

It is the process by which electromagnetic energy is absorbed and converted into some other forms of energy.

Absorption refers to the phenomenon where electromagnetic energy penetrates a material and gets transformed into different energy forms, such as heat. This process is crucial because it influences the amount of energy that is available for sensors to detect. For example, darker surfaces tend to absorb more energy than lighter ones, which means they may reflect less and could appear different in remote sensing images.

Consider how a black shirt feels hotter in the sun than a white shirt. The black shirt absorbs more sunlight and converts it to heat, making it warm, while the white shirt reflects much of the sunlight, staying cooler. This principle is similar in remote sensing; different surfaces (like forests, cities, or water bodies) absorb varying amounts of electromagnetic energy, influencing how they are perceived in the images captured.

It is the amount of radiations of different wavelengths that a medium (e.g., atmosphere) will transmit or allow to pass through.

Transmission is the process through which electromagnetic energy passes through a medium without being absorbed. In the context of remote sensing, the atmosphere can affect how much energy reaches the ground and how much is reflected back to space. Certain wavelengths can pass easily through the atmosphere while others may get absorbed or scattered, impacting data quality and information retrieval.

Think of looking through a tinted window. Light can pass through, but it is altered due to the tint. Similarly, some wavelengths of electromagnetic energy can pass through atmospheric layers unobstructed, while others might be blocked or altered. This transmission plays a key role in determining what sensors can successfully capture and interpret as they collect data from the Earth’s surface.

A remote sensing platform is usually a satellite or an airplane, carrying different sensors.

In remote sensing, a platform refers to the vehicle, such as a satellite or aircraft, that carries sensors to capture data. These platforms provide different perspectives and heights, enabling the collection of various types of data over large areas of the Earth. The choice of platform can influence the resolution and type of data obtained.

Imagine you are at a concert. If you’re sitting in the front row (like a low-flying airplane), you can see the performers closely but may miss the overall crowd. If you’re in a helicopter hovering above (like a satellite), you can see the crowd and the stage layout. Depending on your perspective (or platform), you gather varying information about the same scene, vital for different analysis purposes.

A sensor is an electronic device that detects EMR, and converts them into signals that can be recorded as digital numbers, in the form of bits and bytes, and displayed as an image.

Sensors in remote sensing are the devices responsible for detecting electromagnetic radiation and converting it into digital signals. These signals are then processed to create images which represent what the sensor 'saw' on the Earth’s surface. Sensors come in various types, capable of capturing different wavelengths and resolutions, directly impacting the data quality.

Consider a digital camera as a sensor. Just as it captures light and transforms it into an image on a screen, remote sensing sensors capture electromagnetic energy being reflected from the Earth and convert it to a format that computers can process. The clearer and more advanced the camera (or sensor), the better the image and detail captured.

The sensors are designed to operate in several wavelength ranges to gather the EMR reflected from or emitted by the ground features/objects. The wavelength range is called a channel or a spectral band or simply a band. The sensors can collect the data in a number of spectral bands, and may be grouped as; panchromatic (single band), multispectral (more than one band), or hyperspectral (usually over 100 bands) sensors.

A spectral band is a specific range of wavelengths that sensors are designed to detect and record. Sensors can collect information in various ways: panchromatic sensors gather data in a single wavelength, multispectral sensors take data across several wavelengths, and hyperspectral sensors operate across many more bands, often over 100. Each band provides different information about the surface, allowing for detailed analysis and identification of features.

Think of different flavors of ice cream as spectral bands. Each flavor provides a unique taste experience, just like each band captures distinct information about the Earth's surface. Just as combining flavors can create a unique dessert, combining data from different spectral bands can enhance our understanding of the environment.

The picture resulting from the sensing process is called an image. A remote sensing image can be in paper format or digital format. A digital satellite image is also called a raster image which can be displayed on a computer monitor.

An image in remote sensing is the visual representation of the data captured by the sensors. It can be produced in various formats, predominantly as digital images, which allow for better processing and analysis. Digital images are made up of pixels, where each pixel contains specific data points related to the surface features, allowing viewers to interpret the information effectively.

Imagine taking a photo with your smartphone. The resulting image displays the scene based on the light captured. In remote sensing, a 'photo' is created from the electromagnetic signals detected, allowing scientists to see large-scale features, like forests or urban areas, from space, just like your picture shows details of a moment in time.

It is a medium to calibrate the variations in the brightness of an image that ranges from black to white with intermediate gray values.

Gray scale refers to the range of shades from black (minimum brightness) to white (maximum brightness) available in an image. When analyzing images in remote sensing, gray scale helps in evaluating the differentiation of various features based on their brightness levels, allowing for the identification and categorization of different ground features.

Think of a black and white photo. The variations in shading provide depth and detail that help you recognize objects. Similarly, in remote sensing, varying shades of gray in an image can represent different materials or conditions on the Earth’s surface, making it easier to analyze and interpret.

The word pixel is made from 'picture element'. It is the smallest element in an image. As the image is composed of row and columns, one small grid is called a pixel.

A pixel, short for 'picture element', is the smallest unit of a digital image. Each pixel represents a specific value of brightness and color. In remote sensing images, pixels are organized in a grid pattern to form a complete image. The resolution of an image depends on how many pixels are used; higher pixel counts lead to sharper images.

Imagine a mosaic made up of many tiny tiles. Each tile represents a piece of the larger picture. Pixels function similarly in a digital image—together they create the entire visual. The more tiles (or pixels) you use, the more detailed and clearer the final image appears.

Digital Number in remote sensing system is a variable assigned to a pixel, usually in the form of a byte. In an 8 bit image (28), it ranges from 0–255 into 256 grey levels. The DN value is very important as digital analysis of remote sensing images is based on the variation in these values.

Digital Number (DN) is a value assigned to each pixel in an image, typically expressed in byte form. For an 8-bit image, these numbers range from 0 to 255. The DN value indicates the brightness or color of the pixel, and variations in these values are crucial for digital analysis since they help in interpreting different features and conditions present on Earth’s surface.

Think of DN values like grading scales in school. Just as grades range from low to high to reflect performance, DN values tell us how bright a pixel is. Spatial analysis relies on these variations to help us categorize land uses—like forests, urban areas, or water bodies—much like a teacher uses grades to assess students' understanding.

The satellite moves north to east in the orbit and collects EMR reflected/emitted from the ground objects. The width of the area covered on the ground when satellite sensor scans the Earth surface while moving in an orbit is called a swath. This swath width is different for different satellites and sensors, for example, it is 185 km wide by earlier LANDSATs.

A swath is the strip of the Earth's surface that a satellite sensor covers while it passes overhead. As the satellite orbits, its sensor collects electromagnetic radiation from a specific width of the Earth beneath it. Different satellites have varying swath widths; for example, earlier LANDSAT satellites had a swath width of 185 kilometers. The swath determines the area imaged in one pass and affects how frequently images of the same area can be obtained.

Imagine a lawnmower moving across your yard. The width of the blade determines how much grass is cut in one pass. Similarly, the swath of a satellite sensor determines how much ground it observes in one orbit. A wider swath can cover more area quickly, just as a wide mower makes cutting the grass faster while ensuring fewer passes are needed.

Since the reflected radiations from the ground are continuously recorded by the sensor, each orbit is given a unique number for identification of the ground scene/area, called Path number. The length of the scene defines the Row number. Thus, each scene can be located with its unique Path-Row number. It varies from sensor to sensor and satellite to satellite.

Path-Row numbering is a system used to organize and identify images captured by satellite sensors. Each satellite records data in specific pathways (Path) across the Earth's surface. The Row number defines the length of the scene taken along the path, creating a unique identifier for each image scene. This system allows for easy reference and retrieval of specific data as each combination of Path and Row points to a precise area on the Earth.

Think of Path-Row numbers like coordinates on a map. Just as latitudinal and longitudinal numbers help you locate a specific point on a map, Path-Row numbers help you find data from specific areas captured by satellites. This organization is essential for managing and accessing vast amounts of remote sensing data efficiently.

A graphical representation of DN values in a set of data is called a histogram. In a histogram, individual DN values are displayed along the x-axis, and the frequency of their occurrence is displayed along the y-axis.

A histogram is a visual representation that illustrates how frequently different DN values occur within a remote sensing image. The x-axis displays the range of DN values, while the y-axis shows the frequency of those values. Histograms are useful for analyzing image brightness and contrast, providing insights into how data is distributed across an image.

Imagine a class of students taking a test. A histogram could show how many students received each test score. In remote sensing, histograms show how many pixels have each DN value in an image. Just as a teacher uses this data to understand student performances, scientists use histograms to evaluate an image's quality and characteristics.

It can be defined as the amount of energy output by a source of light relative to the source to be compared. Brightness is a relative term, and it depends on our visual perception. For example, in some cases we can easily say that the image is bright, or image is dull.

Brightness in the context of remote sensing refers to how much light energy a pixel in an image reflects and represents. It's a relative measure based on the amount of energy output by the sources of light. Brightness can vary depending on the surface materials, lighting conditions, and atmospheric effects, thus affecting how images are perceived and analyzed.

Think of two pictures side by side, one bright and clear and the other dark and unclear. The brightness of each image affects how you perceive details. In remote sensing, brighter areas in an image may indicate features like urban areas or water bodies, while darker areas could represent forests or shadows, providing critical insights into land use.

Contrast can be simply explained as the difference between the maximum and minimum DN values in an image. It is an important factor in any subjective evaluation of image quality. Contrast is the difference in visual properties that makes an object distinguishable from other objects and with the background.

Contrast in remote sensing images refers to the difference between the lightest and darkest elements in the image, measured by DN values. High contrast means more distinction between objects, making features easier to identify, while low contrast results in images that may appear hazy or blend together. Contrast plays a vital role in accurately interpreting images and extracting meaningful information from them.

Consider watching a movie on a bright screen versus a dim one. In bright settings, the colors pop, and you can distinguish characters easily. However, in dim lighting, everything may look blurry. Similarly, in remote sensing, high contrast allows scientists to see urban areas and forests distinctly, while low contrast can obscure information critical to understanding landscapes and changes.

It is the computational process of assigning the pixels or objects into a set of categories, or classes, having common spectral characteristics or DN values.

Classification in remote sensing is the method used to categorize pixels in an image based on their spectral characteristics, primarily driven by DN values. This process involves grouping similar pixels together to identify different land cover types, such as water, forest, and urban areas. Accurate classification is essential for producing thematic maps that inform environmental studies, urban planning, and resource management.

• Chunk Title: Thematic Map
• Chunk Text: A map that displays the spatial distribution of an attribute related to a single topic, theme, or subject, is called a thematic map. Usually, a thematic map displays a single attribute, such as soil type, vegetation, rivers, geology, land use, or habitation.
• Detailed Explanation: A thematic map is a specialized map that focuses on visualizing specific themes or attributes rather than general geographic information. These maps display the spatial distribution of various characteristics, such as land use, climate zones, or demographic data. Thematic maps play a significant role in helping researchers, planners, and policymakers visualize and analyze data related to specific topics.

Think of a weather map showing temperatures across a region, where each color represents different temperature ranges. This concrete visual makes climate differences clear and actionable. Similarly, a thematic map organizes environmental data, such as vegetation types across a region, helping scientists and planners understand spatial trends and make informed decisions.

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