Lab 9: Remote Sensing

Transcription

1 Lab 9: Remote Sensing Looking At Earth Using Our Eyes in the Skies Name: TA NAME with guidance from Dr. Caiti Steele Date: Goals The purpose of this lab is to provide a basic introduction to remote sensing (RS), while examining the differences between traditional cameras, digital cameras, and multispectral sensors. Introduction Remote sensing is the science (and to some extent, art) of acquiring information about the Earth's surface without actually being in contact with it. This is done by sensing and recording reflected or emitted [electromagnetic] energy and processing, analyzing, and applying that information. (Canadian Centre for Remote Sensing, 2004). In the past RS data came from images obtained from aerial photographs. Most recently the term has come to refer to data collected from satellite-mounted multi-spectral sensors, such as those on the Landsat series of satellites. This data is normally available in digital format from one of the satellite agencies. Some type of RS or GIS software must be employed to view this digital data as map images. Once the image (usually received as a set of layers) has been processed, there are a number of ways in which it can be used and manipulated. Remote sensing data has proved invaluable in a wide range of research fields, from making maps of the earth surface, to monitoring renewable and non-renewable natural resources, to urban planning. Its success is due to its unique capability to provide near real-time information about the earth s surface on a variety of scales. The entire array of electromagnetic radiation at its various wavelengths is referred to as the electromagnetic spectrum. On one end of this spectra are TV and radio waves having longer wavelengths. On the other end of the scale are x-rays and gamma-rays having very short wavelengths. Our eyes are sensitive to a very narrow band of wavelengths known as the visible spectrum. We perceive these wavelengths of radiation as color. Various sections of the spectrum are used in remote sensing. These are primarily bands of wavelengths where the radiation passes easily through the atmosphere to the surface of the earth, and are called atmospheric windows. A more extensive introduction to Remote Sensing is provided in Appendix E. You must read through this information prior to attempting the following lab exercise. Remote Sensing (RS) and Geographic Information Systems (GIS) are rapidly growing areas of study that affect our lives in an ever increasing manner, thus meriting our interest and exploration. Environmental Science Laboratory 110L New Mexico State University, Department of Agronomy and Horticulture 1

2 Pre-lab Questions and Resources Remote sensing techniques are used in the preservation, management, and exploration of natural resources. Find examples for each of these applications of remote sensing. What are photogrammetry, remote sensing, and geographic information systems (GIS)? How might these technologies relate to your course of study? Key Words Remote Sensing, electromagnetic-spectra, Geographic Information Systems Objectives 1. Know the definitions of all bolded terms in the introduction. 2. Manipulate and interpret digital images produced from remote sensing data. 3. Determine what factors may have given rise to the temporal changes observed via remotely sensed data in this lab. 4. Investigate how remote sensing data has an impact on your life and field of study. Procedure 1. Go to: and click on Landsat Image at the top left of the window. Now select band 3 on the red bar, band 2 on the green bar and band 1 on the blue bar and click compose. The resulting image looks just like a normal color photograph. Remember that a computer monitor display only has three electron guns red, green and blue, so what you have produced here is a true or real color composite, so-called because we selected the red spectral band for the red monitor display gun, the green spectral band for the green display gun and the blue band for the blue display gun. At the bottom of the web page there is a box that describes the uses of the TM bands. Read through these descriptions and then try out the different band combinations suggested at the bottom of the box. 2. Carefully examine the spectral response curves in figure 8 of Appendix E. Which band(s) would be best for distinguishing between: a. Buildings and Desert Scrub b. Bare Soil and Pavement c. Shadow, Dark Asphalt and Water d. Grass and Agriculture Environmental Science Laboratory 110L New Mexico State University, Department of Agronomy and Horticulture 2

3 3. Which land cover classes are the most confused in figure 9 of Appendix E (unsupervised classification of Las Cruces image)? Explain why these classes are confused. 4. For this exercise, we will use the image data sets (given below) for Atlanta a Landsat TM image from 1991 (figure 1a) and a Landsat ETM image from 2000 (figure 1b). If you look closely at the imagery, you may be able to see development has occurred in some of the urban areas surrounding Atlanta. The imagery was classified using unsupervised classification (figures 1c and 1d). Only bands 2 (green), 3 (red) and 4 (NIR) were used for the classification. The software was programmed to identify 12 spectral classes in the image. The 12 spectral classes were then labeled as one of three informational classes: Vegetated, Paved / built-up or Water (table 1). In 1991, what proportion of the land cover was i) Water, ii) Paved / built-up, iii) Vegetated a. In 2000, what proportion of the land cover was i) Water, ii) Paved / built-up, iii) Vegetated b. Calculate the percentage change in cover in the Paved / built-up class between 1991 and 2000 c. Suggest possible sources of error that may affect the estimate of land cover change that we derive from the images. 5. For this lab, we have focused on Land Cover Mapping as a remote sensing application. Now search the internet to create a list of other environmental applications for remote sensing. The websites below should help you to get started. National Operational Hydrologic Remote Sensing Center: SeaWiFS Project: MODIS Active fire mapping program: Calculations and Data Presentation Provide answers for sections 1 through 5 showing any calculations used to answer the questions. Questions 1. If you were a land manager for the state of New Mexico in charge of urban development, how might you utilize remote sensing data in this task? 2. Write a few paragraphs on how remote sensing affects your daily life. Give at least three examples. Environmental Science Laboratory 110L New Mexico State University, Department of Agronomy and Horticulture 3

4 Figure 1a: Atlanta 1991 (Bands 2,3,4). Figure 1b: Atlanta 2000 (Bands 2,3,4). Figure 1c: Thematic map of Atlanta (1991). Figure 1d: Thematic map of Atlanta (2000). Legend Paved / built-up Vegetated Water Environmental Science Laboratory 110L New Mexico State University, Department of Agronomy and Horticulture 4

5 Table 1: Spectral classes and land cover classes of the Atlanta imagery. Atlanta 1991 Atlanta 2000 Spectral class number Land cover class Area (Sq. miles) Spectral class number Land cover class Area (Sq. miles) 1 Water Water Water Vegetated Paved / built-up Vegetated Paved / built-up Water Vegetated Paved / built-up Vegetated Vegetated Vegetated Paved / built-up Vegetated Vegetated Paved / built-up Paved / built-up Paved / built-up Paved / built-up Paved / built-up Paved / built-up Paved / built-up Paved / built-up Important information Please submit the lab report by 5 p.m. on Friday Nov. 5 th snemmers@nmsu.edu to Sylvia Nemmers Environmental Science Laboratory 110L New Mexico State University, Department of Agronomy and Horticulture 5