Chapters 5 and 6 focused on acquisition and application of geographic data that are collected in the world using GPS-enabled devices and other means (e.g., traditional field surveys). This chapter moves the focus to acquisition and application of geographic data collected remotely, using a range of remote sensing technologies. Remote sensing is the measurement of an object without direct contact; the Office of Naval Research derived the term remote sensing in the early 1960s.
This chapter considers the characteristics and uses of raster data produced with airborne and satellite remote sensing systems. Remote sensing is a key source of data for land use and land cover mapping, agricultural and environmental resource management, mineral exploration, weather forecasting, and global change research.
Remotely sensed images are now prevalent in many aspects of our daily lives. You are exposed to imagery through media sources, such as CNN or Fox News, and you can view imagery across the world with Google Maps or Bing Maps. You will encounter examples of imagery from these and other sources in this chapter. In addition to introducing these types of data products, you will also learn about some of the techniques that are used to analyze such images.
The overall goal of Chapter 7 is to acquaint you with the properties of data produced by satellite-based sensors. Specifically, in the chapter, you will learn to:
- compare and contrast the characteristics of image data produced by photography and digital remote sensing systems;
- use the Web to find Landsat data for a particular place and time;
- explain why and how remotely sensed image data are processed; and what types of corrections are necessary for getting geographically accurate information;
- understand how remotely sensed imagery is turned into a range of map products through application of photogrammetric techniques.
Table of Contents
- Introducing Remote Sensing
- Electromagnetic Radiation
- Multi-spectral Image Processing
- Survey of Multispectral Imagery Types and Their Applications
- Other Types of Imagery
- Case Study: Using Landsat for Land Cover Classification for NLCD