A brief history of remote sensing as a governmental activity, a commercial industry, and an academic field provides the student with a perspective on the development of the technology and emergence of remote sensing applications. Accounts of remote sensing history generally begin in the 1800s, following the development of photography. Many of the early advancements of remote sensing can be tied to military applications, which continue to drive most of remote sensing technology development even today. However, after WWII, the use of remote sensing in science and infrastructure extended its reach into many areas of academia and civilian life. The recent revolution of geospatial technology and applications, linked to the explosion of computing and internet access, brings remote sensing technology and applications into the everyday lives of most people on the planet. One could argue that there are very few aspects of life that are not touched in some way by this powerful and enabling way of viewing, understanding, and managing our world.
This lesson will also introduce the basic scientific principles of light and its interaction with matter that makes remote sensing possible. This foundation will be drawn upon throughout the course to explain how remotely sensed imagery is acquired, processed, and analyzed. Many new terms will be introduced and carefully defined, in this lesson and throughout the rest of the course. While some of these terms may seem familiar, many are often misused in casual communication. It is a responsibility of the remote sensing or geospatial professional to inform and guide those with whom he/she comes in contact, through correct and precise use of these terms.
Throughout this course, you will be guided to many external resources that you can use to complement the course material and continue to refer to after completing the course.
At the end of this lesson, you will be able to:
If you have any questions now or at any point during this week, please feel free to post them to the Lesson 1 Questions and Comments Discussion Forum in Canvas.
In Chapter 1 of the textbook, Introduction to Remote Sensing, Jim Campbell provides a narrative of the evolution of remote sensing and photogrammetry over the past two centuries. Some of this history is brought to life in a series of short videos, produced by ASPRS [1].
Watch this video about Aerial Survey Pioneers (1:47).
Watch this video about geospatial intelligence in WWII (1:58).
Watch this video about the role of women in the history of photogrammetry (1:38).
Watch this video about the evolution of analog to digital mapping (2:10).
Watch this video about photogrammetry in space exploration (2:00).
As you continue on in this course and in your further studies, bear in mind that the early innovators of remote sensing and photogrammetry did not have access to the sophisticated electronics and computing devices that we take for granted today! In fact, until very recently, it was often difficult to convince decision-makers and managers that imagery could produce information as accurate (or even more accurate) than data collected on the ground.
Today, almost everyone with a computer, a television, or a cell phone is familiar with the common products of remote sensing and digital mapping. The challenge today is not seeking acceptance for these technologies as much as it is making end users and decision makers aware of certain limitations and uncertainties inherent in these products. Whereas production of an image base map used to require an expert and very specialized equipment, today it can be done with inexpensive software on a home computer. It is quite easy to make a very accurate, useful product; it is just as easy to make a very inaccurate one. Professional expertise and experience are still needed to ensure that image base maps and elevation models meet target specifications and that they can be used appropriately in a broad range of applications.
Chapter 2 of Campbell (2007) delves into the scientific principles of electromagnetic radiation that are fundamental to remote sensing. If you have studied an engineering or physical science discipline, much of this may be familiar to you. You will see a few equations in this chapter, and while you won't need to memorize or make computations with these equations, it is important to gain a conceptual understanding of the physical relationships represented.
Electromagnetic energy is described in terms of
These important terms are further explained in the course textbook. The visible and infrared portions of the electromagnetic spectrum are the most important for the type of remote sensing discussed in this course. Figure 1.01 below illustrates the relationship between named colors and wavelength/frequency bands; it will be a useful reference.
Color | Angstrom (A) | Nanometer (nm) | Micrometer (µm) | Frequency(hz x 1014) |
---|---|---|---|---|
Ultraviolet, sw | 2,537 | 254 | 0.254 | 11.82 |
Ultraviolet, lw | 3,660 | 366 | 0.366 | 8.19 |
Violet (limit) | 4,000 | 400 | 0.40 | 7.50 |
Blue | 4,500 | 450 | 0.45 | 6.66 |
Green | 5,000 | 500 | 0.50 | 6.00 |
Green | 5,500 | 550 | 0.55 | 5.45 |
Yellow | 5,800 | 580 | 0.58 | 5.17 |
Orange | 6,000 | 600 | 0.60 | 5.00 |
Red | 6,500 | 650 | 0.65 | 4.62 |
Red (limit) | 7,000 | 700 | 0.70 | 4.29 |
Infrared, near | 10,000 | 1,000 | 1.00 | 3.00 |
Infrared, far | 300,000 | 30,000 | 30.00 | 0.10 |
Understanding the interactions of electromagnetic energy with the atmosphere and the Earth's surface is critical to the interpretation and analysis of remotely sensed imagery. Radiation is scattered, refracted, and absorbed by the atmosphere, and these effects must be accounted for and corrected in order to determine what is happening on the ground. The Earth's surface can reflect, absorb, transmit, and emit electromagnetic energy and, in fact, is doing all of these at the same time, in varying fractions across the entire spectrum, as a function of wavelength. The spectral signature that recorded for each pixel in a remotely sensed image is unique, based on the characteristics of the target surface and the effects of the intervening atmosphere. In remote sensing analysis, similarities and differences among the spectral signatures of individual pixels are used to establish a set of more general classes that describe the landscape or help identify objects of particular interest in a scene.
A remote sensing system comprises two basic components: a sensor and a platform. The sensor is the instrument used to record data; a platform is the vehicle used to deploy the sensor. Lesson 2 will discuss imaging sensors and platforms in much greater detail. Every sensor is designed with a unique field of view which defines the size of the area instantaneously imaged on the ground. The sensor field of view combined with the height of the sensor platform above the ground determines the sensor footprint. A sensor with a very wide field of view on a high-altitude platform may have an instantaneous footprint of hundreds of square kilometers; a sensor with a narrow field of view at a lower altitude may have an instantaneous footprint of ten of square kilometers.
Resolution, as a general term, refers to the degree of fineness with which an image can be produced and the degree of detail that can be discerned. In remote sensing, there are four relevant types of resolution:
Spatial resolution is a measure of the finest detail distinguishable in an image. Spatial resolution depends on the sensor design and is often inversely related to the size of the image footprint. Sensors with very large footprints tend to have low spatial resolution; and sensors with very high spatial resolution tend to have small footprints. Spatial resolution will determine whether individual houses can be distinguished in a scene and to what degree detailed features of the house or damage to the house can be seen. For imaging satellites of potential interest to the housing inspection program, spatial resolution varies from tens of kilometers per pixel to sub-meter. Spatial resolution is closely tied to Ground Sample Distance (GSD) which is the nominal dimension of a single side of a square pixel in ground units.
Temporal resolution refers to the frequency at which data are captured for a specific place on the earth. The more frequently data they are captured by a particular sensor, the better, or finer, is the temporal resolution of that sensor. Temporal resolution is often quoted as a “revisit time” or “repeat cycle.” Temporal resolution is relevant when using imagery or elevations datasets captured successively over time to detect changes to the landscape. For sun-synchronous satellites of interest to the housing inspection program, revisit times vary from about 2 weeks to 1 day.
Spectral resolution describes the way an optical sensor responds to various wavelengths of light. High spectral resolution means that the sensor distinguishes between very narrow bands of wavelengths; a “hyperspectral” sensor can discern and distinguish between many shades of a color, recording many gradations of color across the infrared, visible, and ultraviolet wavelengths. Low spectral resolution means the sensor records the energy in a wide band of wavelengths as a single measurement; the most common “multispectral” sensors divide the electromagnetic spectrum from infrared to visible wavelengths into four generalized bands: infrared, red, green, and blue. The way a particular object or surface reflects incoming light can be characterized as a spectral signature and can be used to classify objects or surfaces within a remotely sensed scene. For example, an asphalt parking lot, a corn field, and a stand of pine trees will have all have different spectral signatures. Automated techniques can be used to separate various types of objects within a scene; these techniques will be discussed in Section III below.
Radiometric resolution refers to the ability of a sensor to detect differences in energy magnitude. Sensors with low radiometric resolution are able to detect only relatively large differences in the amount of energy received; sensors with high radiometric resolution are able to detect relatively small differences. The range of possible values of brightness that can be assigned to a pixel in an image file or band is determined by the file format and is also related to radiometric resolution. In an 8-bit image, values can range from 0 - 255; in a 12-bit image, values can range from 0 - 4096; in a 16-bit image, values can range from 0 - 65536; and so on.
Chapter 1 of Campbell (2007) defines key aspects of remote sensing data collection and analysis. It also defines a number of key terms that you will hear over and over again throughout this course. Campbell discusses the evolution of government and commercial remote sensing programs, and how remote sensing supports national and international earth resource monitoring. This introduction sets the contextual stage for the highly technical material to come. It is important to understand the motivation behind technology development, and to see how technology contributes to the broader societal, political, and economic framework of geospatial systems, science, and intelligence, be the application to military, business, social, or environmental intelligence.
In another seminal remote sensing textbook, Remote Sensing of the Environment, cited in the course syllabus as an additional reference, John Jensen describes factors that distinguish a superior image analyst. He says, "It is a fact that some image analysts are superior to other image analysts because they: 1) understand the scientific principles better, 2) are more widely traveled and have seen many landscape objects and geographic areas, and/or 3) they can synthesize scientific principles and real-world knowledge to reach logical and correct conclusions. (Jensen, 2007)
Jensen goes on to describe the role of the human being in remote sensing process.
"Human beings select the most appropriate remote sensing system to collect the data, specify the various resolutions of the remote sensor data, calibrate the sensor, select the platform that will carry the sensor, determine when the data will be collected, and specify how the data are processed."
This statement succinctly expresses our goals, as instructors, for developing a remote sensing curriculum within a broader geospatial program. It has been our experience, working with local, state, and federal government agencies, in engineering, environmental, and disaster response and recovery applications, that more expertise in the application of remote sensing is needed. By expertise, we mean a solid, working knowledge of the fundamentals, and use of those fundamentals in combination with good problem-solving and critical thinking skills. In today's world, there are a small number of professionals at a "very expert" level with a particular sensor or application, but there is a shortage in the workforce of people who are knowledgeable at a basic or intermediate level over the broad scope of remote sensing.
As remotely sensed data reaches the general public through tools such as Google Earth, in-car navigation systems, and other web-based and consumer-level technologies, it becomes increasingly important for the basic principles of remote sensing and mapping to become common knowledge. Misinterpretation and ill-informed decision-making can easily occur if the individuals involved do not understand the operating principles of the remote sensing system used to create the data, which is in turn used to derive information. After taking this course, you should have acquired enough knowledge to understand the purpose and scope for each of the activities set forth by Jensen above; you should "know what you don't know," and, when you don't know, you should be armed with the basic concepts and vocabulary that will allow you ask appropriate questions, to seek out the right expert, and to communicate effectively with that person.
The geospatial professional has grown sufficiently to support a large number of professional societies and associations. Some have a public sector focus, some an academic/research focus, and others a commercial focus; they may also be organized around particular applications or disciplines. Most of these organizations encompass remote sensing in one form or another, especially as a source of data or an analysis tool. However, few of these organizations focus on the technology of remote sensing or photogrammetry itself: the design and deployment of sensors, processing of sensor data into usable GIS products, development of tools for large-scale production and analysis of digital imagery and elevation data, etc. The American Society for Photogrammetry and Remote Sensing (ASPRS) and the International Society for Photogrammetry and Remote Sensing (ISPRS) are the two most important sources for information and professional development in these specific areas of interest.
ASPRS was founded in 1934 by a small group of like-minded pioneers in a unique and emerging field. Today, over 7000 individuals worldwide are members. Students in this course may have joined ASPRS to get a discount on the course textbook. There are many other ways that ASPRS membership can support professional development and career advancement.
Watch the following video about ASPRS Membership (1:50)
ISPRS was founded in 1910, and is devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. National organizations, such as ASPRS, are the voting members; individuals can take part in activities, conferences, technical Working Groups, and Commissions through affiliation with one of the Member organizations. The ISPRS Congress, an international conference dedicated to photogrammetry and remote sensing, takes place every four years and is hosted by the home country of the elected President.
Links
[1] http://www.asprs.org
[2] https://www.youtube.com/watch?v=wW-JTtwNC_4
[3] https://www.youtube.com/watch?v=hQu0wxXN6U4
[4] https://www.youtube.com/watch?v=kzgrwmaurKU
[5] https://www.youtube.com/watch?v=4jABMysbNbc
[6] https://www.youtube.com/watch?v=KVVbhqq6SRg
[7] https://www.youtube.com/watch?v=9ZLLQPFSAhs
[8] http://www.isprs.org