5.1. Introduction

The word “remote” means “from a distance,” and “sensing” in this case means “to record.” So remote sensing can be defined as the collection of information about an object, area, or phenomenon and subsequent analysis of data acquired by a device from a remote distance (Lillesand and Kiefer, 1996). More precisely, it can be defined as the “art of science & technology of obtaining reliable information about the physical objects and environment through the process of recording, measuring & interpreting images/data obtained from remotely distant sensor systems.”

Remote sensing is a technique that allows us to collect data about an object or area from afar, instead of being physically present. This involves using sensors that gather various forms of data, such as images, which are then analyzed to derive useful information. In simple terms, it's like taking a picture of a landscape using a camera from an airplane; you can see and understand what's there without being on the ground.

Imagine a bird flying high above a park. With its keen eyesight, it can see everything happening below without needing to land. This is similar to how remote sensing works, where sensors collect data from far away, much like the bird observing from the sky.

For example, with eyes it is possible to gather information about the surroundings from a distance. Reading newspaper, watching TV, listening a lecture during classes, are all examples of remote sensing. The human eyes capture the light reflected by these objects and the brain interprets the colour, shape, pattern, etc., and translates this data into useful information. The human eye, however, is limited to a small part of the total electromagnetic spectrum (EMS), i.e., visible light.

Human beings gather information about the world primarily through their eyes, which can detect visible light. Activities like reading or watching TV rely on the eye's ability to interpret reflected light, just like remote sensors interpret different types of electromagnetic radiation. However, the spectrum that our eyes can perceive is much narrower than what remote sensing technology can capture, which includes infrared and microwave wavelengths.

Consider a painter who can only see the colors on their palette and create art from those – while they can create detailed pictures, they are limited by the palette. Similarly, our eyes have a 'palette' limited to visible light, while remote sensing technology can perceive a much broader spectrum, allowing us to capture more information.

Remote sensing involves an interaction between the incident radiation (EMS) and the targets/objects. It also involves the sensing of reflected/emitted energy from the objects with the help of sensors. The sensors on-board satellites detect the radiations from the objects/targets. In remote sensing, various kinds of devices and sensors are used to record the electromagnetic radiation outside the visible range, especially the near infrared, middle infrared, thermal infrared, and microwaves.

In remote sensing, the process begins when electromagnetic radiation hits an object in the environment, such as trees or water. This radiation gets reflected or emitted by those objects. Satellite sensors then capture this reflected energy, letting scientists analyze what is there based on the unique signatures that different materials provide across various wavelengths—not just visible light, but also infrared and microwave wavelengths as well.

Think about a detective using various lenses to examine clues at a crime scene. Some lenses allow the detective to see details that the naked eye can't. Similarly, satellites use sensors that can detect various wavelengths of light, enabling them to gather information about different objects that we can't see with our eyes alone.

The amount of radiation emitted and reflected from the Earth’s features/objects depends on the physical and chemical properties of the objects or material. It also depends on the surface roughness, angle of incidence, intensity, and wavelength of radiant energy. The variation in energy helps in the identification of various objects on the Earth’s surface. So, the sensors play an important role in data capture and dissemination of objects/targets.

Different objects reflect and emit radiation in unique ways depending on their physical characteristics like color, texture, and material properties. For example, a smooth surface, like a lake, will reflect light differently than a rough surface, like a forest floor. Understanding these characteristics allows sensors to distinguish between different types of land cover or objects, which is crucial for accurate data analysis.

Imagine different types of fabric reflecting sunlight: a smooth silk sheet and a rough wool blanket reflect light in distinct ways. Similarly, in remote sensing, different surfaces reflect and emit light differently, making it easier to identify them based on these unique patterns.

Remote sensing systems provide spatio-temporal information on Earth surface processes from local to global scale. The images from these systems help in a wide range of disciplines, such as surveying and mapping, geography, geology, computer science, zoology, agriculture, forestry, botany, meteorology, soil science, urban planning, military, oceanography, and civil engineering. Information such as land use, vegetation types, urbanisation, soils, water, geology, forest, surface elevation, and snow can be derived from remote sensing images.

Remote sensing is valuable across numerous fields. It allows researchers and professionals to gather important data on various aspects of the Earth’s surface without needing to be physically present at each location. For instance, remote sensing can help monitor deforestation, map urban sprawl, and assess agricultural conditions through the analysis of satellite imagery.

Think of a doctor using an x-ray to see inside a patient’s body without performing surgery. Remote sensing acts similarly by allowing scientists and researchers to see what’s happening on Earth from a distance, enabling them to understand and monitor changes in the environment effectively.

Today, remote sensing images have become an integral part of many national level government schemes or projects. With the availability of very high-resolution images from CARTOSAT-3, SPOT-7, Sentinel, IKONOS, WorldView, QuickBird, GeoEye, AMSR, TRMM, SSM/I, RADAR, SAR, etc., remote sensing applications are growing rapidly. Integration of remote sensing data with other thematic layers in a Geographic Information System (GIS) provides additional benefits and flexibility to be used in a variety of applications requiring spatial modelling.

Remote sensing data is now essential for governments worldwide in planning and executing projects, from urban planning to disaster management. Advanced satellite technology provides detailed images that help decision-makers visualize and analyze changes to land use, environmental impacts, and resource allocation. When combined with GIS data, remote sensing images enhance spatial analysis capabilities.

Similar to how a city planner uses detailed maps and data to create urban layouts, governments use remote sensing images to make informed decisions about land use, resource management, and environmental protection, thus enhancing overall community planning.

This chapter specifically provides the details of data from optical sensors, their characteristics, and methods of classification. However, a small description is also given about microwave data and images.

The primary focus of this chapter is to delve into optical remote sensing, which includes understanding the data captured by sensors, how to classify this data, and its characteristics. This foundational knowledge is crucial for effectively utilizing remote sensing technologies in various applications, while also briefly touching on microwave data as a complementary aspect.

Consider a student preparing for an exam by focusing on the math section while briefly reviewing science material. This chapter focuses on getting in-depth knowledge of optical data while recognizing that other subjects (like microwave data) also play a valuable role in remote sensing.

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