CEO User's Guide

Introduction

Students taking the OEFS course should be aware that some of the information in this guide might change during the course of the semester. Such changes will be announced in class or lab and will most likely be posted on the Observing the Earth from Space Web page and OEFS email list. The Observing the Earth from Space web page is the source for the most up to date class information; check it regularly. Please report to the course instructors any inaccuracies or problems you discover with this guide or the lab.

I. Observing the Earth From Space

I.1Help

In addition to these printed manuals, you should take advantage of several sources of on-line help.

For UNIX questions:

First and foremost, read the UNIX man pages! The basic on line help for a Unix system consists of a series of files known as "man pages". To get help for a specific command, type "man" followed by the name of the command. To get a list of possibilities when you do not know exactly what command you need to use, type "man -k" followed by a key word. For example, "man -k file" would give a long list of all the commands that had to do with files, along with a short description of each one. From this list, you can pick the command you need, then use "man command" to get more detailed information. See "man man" or An Introduction to UNIX for more information on man.

For Silicon Graphics questions:

SGI has compiled a nice set of on-line documentation that is accessible by clicking on the "Help" menu on the terra systems in the lab. The "On-Line Books" describe SGI-specific products. Look here for information on how to use the desktop, icons, CD-ROM drives, and so on. These books contain massive amounts of information so use the search tool to find the right answers to your questions. This feature is not available on the Orbit workstations.

For most topics, especially concerning ERMapper or Observing the Earth From Space, use the web!!!

Several web pages provide very useful information for this course. Start Netscape by typing "netscape &" in a Unix shell and click on the "Home" button to take you to the CEO's home page. From the CEO page you will be able to access the following pages, among others:

• Observing the Earth From Space page (Syllabus, corrections, announcements etc.)
• ERMapper Frequently Asked Questions page (Tips from the ERMapper folks)
• Dataset Archive & Quick Look images
• On Line version of the Guide and An Introduction to Unix.

Use the CEO and OEFS email lists to contact other lab users.

Email is a powerful tool for getting help. There are mailing lists for students of the Observing Earth From Space course " oefs-list@pantheon" and for all CEO users "ceo-users-list@pantheon" for this purpose. If the answer to your question is not in any of the above sources, you can send a question to all the people on the list (current users of the course or of the lab) via email. In most cases, someone else will have run into your problem before and will offer a solution, suggestion, or at least moral support.

You may also consult the instructors and TA's for the Observing the Earth From Space course individually. Specific office hours will vary, but Table 1 lists how to contact some of the key people.

I.2 Using the CEO Facilities

I.2.1 Hours

The hours when one can access the orbit machines in each of CEO's affiliated departments vary and will, in general, be more flexible than the hours the CEO lab is open. Also the orbit workstations can be used to telnet into other systems and sites where you have accounts.

Of course, anyone with an account may also log in to the CEO terra workstations at any time from other workstations or X-terminals on the campus network (see An Introduction to UNIX for remote X-displays). Public access X-terminals are available in the Kline Biology Library, however these terminals are often very busy and may not be suitable for long sessions.

I.2.2 Using the Orbit Workstations to access the CEO lab

I.2.2.1 Login

1. Login to netOS - At the netOS login screen you must enter your username and password, and select which system you wish to connect to. This login step only creates a temporary workspace for internal workstation applications. A Unix session login screen is displayed. This should be cancelled by clicking on the X in the upper right corner of the window.
2. Switch to Motif - Click on the netOS button in the lower left corner of the screen. Select Applications. Select Window Managers. Select Switch to MWM. The Motif window manager will load in about five seconds (you should see the cursor change to an hourglass briefly).
3. Login to a host system - Right-click anywhere on the screen to bring up a menu. Select @workApplications. From the sub-menu select UNIX Session. Select one of the Terra systems from the host list and click on the Telnet button. Log into the system with your username and password. You can now run your applications from this UNIX session.

I.2.2.2 Logout

1. Logout of the host system - Close all of your open applications. From the UNIX shell enter the logout command. This closes your session on the host system at the lab.
2. Logout of the netOS session - Right-click anywhere on the screen to bring up the menu. Select @workApplications. From the sub-menu select Setup. Left-click on the Logout button then select OK in the Please Confirm message box. This will end the close the netOS session and release the temporary disk space reserved in the first step of the login process. The netOS login screen will be displayed for the next user.

I.2.3 Tape Drive

I.2.3.1 Sample Backup and Restore:

To write data to tape:

1. Log in to the terra3 system.
2. Insert your tape in the tape drive.
3. "cd" to the directory that contains the directory you want to backup. For example, if you have a directory called "final_project" in your directory called dataset, type "cd dataset".
4. Use the "mt" command to make sure that the tape is loaded. Type "mt status". The line next to "Media:" should read "READY, writeable, at BOT". If it says your tape is write protected, eject the tape using "mt unload", flick the write protect switch, and then reinsert the tape. Repeat the "mt" and "mt status" commands.
5. To write the directory "final_project" to tape, use the command "tar cvf $TAPE final_project". This will list the files as they are written to tape.
6. After writing, rewind the tape with "mt rewind".
7. You can check the contents of the tape with "tar tv".
8. Eject the tape using "mt unload".

To restore data from the tape:

1. Log in to the terra3 system.
2. Insert your tape in the tape drive.
3. "cd" to the directory in which you want to place the files from the tape.
4. Use the command "tar xvf $TAPE" to read the files back off the tape.
5. After reading, rewind the tape with "mt rewind".
6. Eject the tape using "mt unload".

I.2.3.2 Reading & Writing Tapes from Other Operating Systems

* SunOS to SGI on the Eliant 820:
Write on the Sun using tar with a blocking factor of 20. Read on the SGI using the no swap, variable record length device. For example "tar cvbf 20 /dev/nrst0 <directoryname>" on the Sun could be read with "tar xvbf 20 $TAPE <directoryname>" on our SGI. To go the other way, use the same commands but switch the "c" and "x" options to tar. (See the tar man page.)

* SGI to OSF DAT:
Use the variable length device when writing on the SGI. For example, "tar cvf /dev/mt/tps3d2v <directoryname>" on SGI could be read by "tar xvf /dev/rmt0h" on an OSF machine with a tlz04 or tlz06 DAT drive. Check the density of the drive on the OSF machine (see the "tz" man page), it should be 61000 bpi. These commands almost work in reverse to go from OSF to SGI, but for some reason there have been problems reading the end of the tar file when going from DEC to SGI.

I.2.4 CD-ROM Drives

Unfortunately, CD's suffer some of the same problems as floppies when you are trying to access them from the desktop icons. Here are some common problems and fixes:

* Problem: When I insert the CD, no disk appears in the CDROM icon and I can't see my files in the directory view window for the CDROM.

Solution: Close the CDROM directory view window before inserting the disk. After you insert the disk, wait a few seconds before double clicking on the CDROM icon to open the directory view window. If this still doesn't work, check that the CDROM is mounted by typing "df"; look for a line near the bottom of the output that contains "/CDROM". If you can't find this entry, see "another solution" below. If you do see a line in the output from "df" that contains "/CDROM" you can access the disk one of two ways, even if the icon isn't working. One option is to use the Unix shell. The files on CD can be accessed under the directory "/CDROM" using all the normal Unix commands (cd, pwd, ls, etc.). The other option is to open the System Manager by clicking on "System Manager" under the "System" menu on the Toolchest. Under the "shared resources" area on the system manager there should be another CDROM icon. If you double click on this CDROM icon you should get a directory view window that works properly.

Another Solution: Occasionally there may be problems getting the CD to mount properly. The best solution here is to go slowly during the process of inserting and mounting the disk. Open the tray, insert the CD, close the tray. Wait a few seconds (maybe up to 10 or 20) and type "df" to see if the CD has mounted. If there is no "/CDROM" entry, open the drive (push the open button on the drive), wait a second or two then close it again. Try this a few times. If you still don't have any luck, send email to Larry Bonneau at laurent.bonneau@yale.edu informing him of the problem and try using the CDROM on another SGI O2.

* Problem: How do I eject my CD?

Solution: Close the CDROM directory view window (if one is open), and make sure no programs are accessing the disk (this includes your working directory in any open Unix shell). Type "eject /CDROM" in the Unix shell. If you were having problems reading the CD in the first place (as in question 1, above) try pushing the open button on the drive.

I.2.5 CD Recorder

I.2.6 Printing

While there are no current page-limits implemented for printing at the CEO lab, users are reminded that color printing is costly. You are requested to limit color printing to final output where possible. Most applications provide a Print Preview option that should be used to preview output prior to actually printing an image or map. This will help conserve supplies and reduce the operating costs of the lab.

To remove a job from the print queue using the Unix shell type "lpstat" to find the number of your job, then type "cancel job_name", where job_name will take the form "ceohp5m-#" and the # is the number you got from lpstat. An easier way to monitor printing is to open the "Printer Manager" from the "System" menu on the Toolchest and double click on the "ceohp5m" icon. This panel will show the line of jobs waiting to be printed and will let you delete your jobs if you make a mistake.

I.2.7 Electronic File Transfer - FTP

If it is not possible, or practical, to use a web page and browser to share data, the CEO has made arrangements to use the anonymous ftp server of the meteorology group in the Geology Department. This service will be useful for those CEO users who need to share data with colleagues at other institutions who have access to the Internet but not to the World Wide Web.

I.2.7.1 Someone else wants to send you data:

1. Decide on the file format you want. (See FAQ #2 in section I.8)
2. Tell your colleague to ftp to "stormy.geology.yale.edu" using the username "anonymous" and their email address as a password.
3. Tell them to change directory (cd) to "pub/incoming" and change transfer mode to binary (type "bin").
4. Now they can transfer the data using "put".
5. You can then ftp to "stormy.geology.yale.edu" using the username "anonymous" and your email address as a password.
6. Change directory (cd) to "pub/incoming" and use the "get" command to retrieve the data.

I.2.7.2 You want to make data available to someone else:

1. Follow the steps above in "Someone else wants to send you data:" to put your data in the pub/incoming directory of the ftp server.
2. Tell your colleague to ftp to "stormy.geology.yale.edu" using the username "anonymous" and their email address as a password.
3. Tell them to change directory (cd) to "pub/incoming" and change transfer mode to binary (type "bin).
4. Now they can retieve the data using "get".

I.2.8 Portable Spectrometer

The spectrometer uses a fiber optic cable with a foreoptic attachment to limit the sensor field-of-view. A detector array in the spectrometer captures photons that are converted and stored as electrons. The stored electrons are converted from a voltage to digital data and transferred to a PC as "raw" digital numbers.

The FieldSpec measures three specific radiation quantities: reflectance, radiance, and irradiance. It uses a specially configured Toshiba laptop computer to perform the numerous calibrations and reference corrections that are required when measuring radiation quantities. These calibrations and corrections remove the "dark current" portion of the signal associated with thermal electrons and produce signal ratios to adjust for varying ambient lighting.

Detailed operating instructions can be found in the Observing Earth From Space class exercise "Experiments with a Personal Spectrometer". A copy of this document is located in the On Line Documentation section of the CEO website.

I.3 CEO Data Archive

1. Find the scene you want on the CEO Data Archive. Determine on which CD-ROM the image is stored by looking under the column labeled "CDROM#". Bring this CD-ROM to any one of the terra systems.
2. Place the CD in the CD-ROM drive on the terra system.
3. Change directory to "/CDROM" and read the file "0readme" which contains information about the scenes on the disk and some additional instructions on viewing the data.
4. Click the "load algorithm" button on the ERMapper main menu bar. Select "/CDROM" from the "Directories" menu of the Load Algorithm window. Select the "algorithm" directory and click "OK". Finally, select the ready-made algorithm for the scene you want to view and click "OK". The algorithm will start displaying.
5. Zoom around the image, adjust enhancements and so on until you identify the area you wish to cut out of the image. Open the "Geoposition" window under the "View" menu of the main toolbar, and select "Extents" on the Geoposition window. Record the values for "Cell X" and "Cell Y" for the top left and bottom right corners of the current zoom. Round these values down to the nearest integer.
6. Use the "Cut Raster Data" module found in the menu [Utilities | File Maintenance | Datasets ]. Alternatively you may use the program "cut_erm_data" from the Unix shell to extract the piece of the image that you want.
7. Return the CD-ROM to the shelf in the CEO lab where you found it.

Here's an example of how to cut out just the area around New Haven from the Connecticut fall Landsat MSS image:

1. Use the Data Archive to discover that the Connecticut Fall Landsat MSS image is stored on CD-ROM 1007. Log into a terra system and mount this CD-ROM.
2. Read the file "/CDROM/0readme" and find out that the raw data for this scene is stored in a subdirectory by its path and row and is named by its date.
3. Start ERMapper and load the algorithm "mss_fall_421-rgb.alg" from the directory /CDROM/algorithms. This will load the following dataset: "/CDROM/p013_r31/91-10-20.ers".
4. Zoom to an area around New Haven, and read the Cell X and Y values (really pixel numbers) from the Extents part of the Geoposition window. You might get numbers like 1753.05X, 2238.51Y for the top left and 2238.51X, 2616.09 for the bottom right corners of the zoom. Rounding these numbers off (we can't extract partial pixels), we get 1753 to 2238 for our X range and 2238 to 2616 for the Y range of the window we wish to "cut out" of this dataset.
5. Move to a Unix shell and "cd" to your "dataset" directory. (Type "cd" to get home then "cd dataset"). Make a new directory for the cut out piece, for example "mkdir haven-data". Change into this new directory ("cd haven-data"). In ERMapper load the "Cut Raster Data" module. Select the input filename "/CDROM/p013_r31/91-10-20.ers" and enter a new output filename such as "new_haven_mss". Enter the starting and ending X and Y cell coordinates from the Extents window and run the module.

   Alternatively, from the Unix shell you can run the program cut_erm_data. For this example, you would use:
   "cut_erm_data -c1753-2238 -l2238-2616 /CDROM/p013_r31/91-10-20.ers new_haven_mss.ers"
   which would create a new dataset called new_haven_mss in your haven-data directory. For a list of options to cut_erm_data, type "cut_erm_data -h".

6. Finally eject the CD-ROM by typing "eject /CDROM" and return it to the storage rack.

I.4 Digital Orthophoto Quadrangle (DOQ)

I.4.1 Background

A map, on the other hand, provides a spatially accurate representation of the earth's surface. Within the limits of the scale and map projection selected, shapes, sizes, distances, and angles can be measured accurately. Because a map is a generalized representation of the earth, it typically does not show much detail of the surface. Areas may reflect a broad classification such as forested or urban, and a few major features may be located such as towns and political boundaries.

A Digital Orthophoto Quadrangle (DOQ) is a dataset that combines the detail of a photo with the spatial accuracy of a map. Scanning an aerial photograph into a computer in a raster file format creates a DOQ. The computer then digitally compares the raster image with a corresponding digital terrain model to mathematically adjust the image, pixel by pixel. This eliminates the displacement caused by the airplane's tilt and the perspective view of the photograph resulting from the camera not being directly over every object in the photograph. Features are represented in their true ground position, making direct measurement of distance, areas, angles, and positions possible.

The U.S. Geological Survey (USGS) is the lead Federal agency for the collection and distribution of digital cartographic data and so provides DOQs for much of the United States. This spatially corrected aerial photographic data is available in a 3.75-minute quadrangle format, with four DOQ's per standard, large-scale USGS topographic map. Each image also contains a buffer of 50 to 300 meters beyond the quadrangle boundary to permit the smooth joining of multiple images into a single mosaic. Collections of DOQs are distributed, by U.S. county, on CDROM at a cost of approximately $50 per county.

I.4.2 DOQ Attributes

Each DOQ consists of an image file and a header file. File names follow the DOS name convention with an eight-character name, a "dot", and a three-character file extension. The structure of the name is "onnnnnnn.qqx" where "o" is the leading lower-case letter o, and "nnnnnnn" is approximately the latitude and longitude of the quadrangle. The "qq" represents the quarter of the larger 7.5-minute quadrangle, SE, SW, NE, or NW. The "x" identifies the image file with the lower-case letter c and the header file with the lower-case letter h. File o410725.sec is the image file for the southeast quarter of the Ellington 7.5-minute quadrangle. File o4107204.neh is the header file for the northeast quarter of the Broad Brook 7.5-minute quadrangle.

A full DOQ image file requires approximately 50 MB of storage. Each image file on the CD has been compressed and is stored in the JPG format, which reduces the CD image storage to approximately 4.5 MB but causes the loss of some image detail as a result of compression. Images in the TIFF format, with no compression loss, can be purchased individually from the USGS if necessary. Data is stored on the header files in one of two formats. Header files created after 1996 store the information in ASCII records using a KEYWORD=VALUE format that can be easily viewed using a variety of utilities and programs. Header files created prior to 1996 store records in a binary format in variable length records. These can be displayed using the technique described below for fixing the registration.

Before data can be extracted from the CD, it is necessary to identify the proper DOQ file name. The file doqlist.txt contains a list of 7.5-minute quadrangle names and the associated quarter-quadrangle DOQ file names in the "onnnnnnn.qqx" format described above. This file is located in the directory CDROM/document/doqtext. The image and header files are located in the directory CDROM/data. Additionally, each of the DOQ CDs produced by the USGS contains a complete set of documentation. Please refer to one of the 27 text files to learn more about these images.

I.4.3 DOQs and ERMapper

From ERMapper select the menu [Utilities | Import Image Format | USGS (DOQ)]. This will open up an input/output file window. Select the header file for the DOQ you wish to import. Using the Broad Brook Northeast quarter DOQ example above, the input file would be: /CDROM/data/o4107204.neh. Enter a unique file name for the output file such as: /usr/people/bonneau/dataset/doq/bbrookne.ers. Click on the OK button and the import will begin. Once the import has completed, it will be necessary to correct two problems with the import utility; both the cell size and the registration point are wrong.

To fix the cell size: Use a text editor such as jot, nedit, or vi to edit the header file (.ers) of the ermapper dataset that was created by the import process. Find the "CellInfo" block which will look something like this:

                CellInfo Begin
                        Xdimension      = 0.8705851241556
                        Ydimension      = 0.9036013304257
                CellInfo End

Simply change the Xdimension and Ydimension to reflect the real cell size of these files (1 meter)

                CellInfo Begin
                        Xdimension      = 1.0
                        Ydimension      = 1.0
                CellInfo End

To fix the registration: Open an xterm from within ERMapper (Utilities | User Menu | Open a Terminal Window). Type the following "importdoq -t <quad_to_import>" where "<quad_to_import>" is the full path name to the file on CD (e.g. /CDROM/data/o4107204.neh). The data from the header for this quad will scroll up on your screen. Find the values in Section 3 - Item 10 labeled "[10]Pri X,Y of Pixel 1,1". These are the correct NAD83, UTM easting and northing values that you will need to enter in the ERMapper header file, write them down.

Go back to editing your header file and find the "RegistrationCoord" block that looks like this:

                RegistrationCoord Begin
                        Eastings        = 639620.0008953
                        Northings       = 4658093.22494
                RegistrationCoord End

Replace the easting/northing values that are in there with the ones you just wrote down.

Exit the xterm by typing "exit".

Your DOQ should be ready to use! (Reload the dataset if you are already displaying it.)

The newly created ERMapper DOQ file will be approximately 50 MB in size. You may want cut out a smaller subset of the image to retain in your personal storage area and delete the full DOQ file. Another option is to resample the image to reduce both the resolution and size. If the image is converted from one-meter to three-meter resolution the file size will be reduced to 1/9th of the original size, or approximately 5.5 MB.

I.5 ERMapper Hardcopy

1. Save the algorithm that creates the desired image. To do this, select "Save As..." from the "File" menu, or click the toolbar icon of a floppy disk with an arrow pointing to it.
2. Start the hardcopy program by selecting "File|Print..." or clicking the printer on the toolbar.
3. Using the file chooser on the same line as "Algorithm", (Open file folder icon) select the algorithm you want to print.
4. Using the file chooser on the same line as "Output Name", select the appropriate output selection (see choices below) from the "CEO" directory /usr/people/erm/ermapper/hardcopy/CEO.
5. Use Printer Setup to select between "portrait" and "landscape" mode.
6. Verify the output if it's a Postscript file using the Postscript previewer "xpsview" (see below).

I.5.1 Print Preview (8.5x11_print_preview)

I.5.2 Grayscale

Note that making good-looking grayscale prints is difficult. You will probably have to adjust your algorithm's transform to produce optimum results. One strategy that helps is to reduce the maximum output brightness and increase the minimum output brightness. MIS can print very high quality grayscale images, but for the most part, grayscale should be printed on the CEO lab printer.

I.5.3 Color

Generally you should use the "Color to file" option. This will allow you to check these files with the postscript preview program before you send them to be printed. It is also how to make files that you can send to MIS.

I.5.4 Using the Postscript Previewer

I.5.5 SGI Image Files

Choose "SGI_image" from the "Output Name" menu to use this option. This program will create an SGI image 500x500 pixels which should fit on one 8.5"x11" page. This program creates a file in your home directory called "print.rgb" which is the new SGI image of your algorithm.

I.5.6 GIF Image Files

I.6 Importing and Exporting Data

I.6.1 ERMapper to GRASS:

1. In ERMapper select: UTILITIES/EXPORT RASTER/ASCII. This will simply create a text file with raw reflectance values. Make sure to save this file to your home directory.
2. You must append a header to the beginning of this ASCII file using a text editor (jot or nedit) so that GRASS can properly import the image. This header information can be obtained in ERMapper using the dataset glyph in the Overlays window. An example of a header is shown below (also see pg. 341 of the GRASS reference manual)
   

   north:		6870.00000    	** this map has a 30m by 30m resolution **
   south:		0.00000	    	** i.e., 6870 meters / 229 rows = 30m **
   east:		12510.00000
   west:		0.00000
   rows:		229
   cols:		417
   
   (this is followed by the data produced using ERMapper's ASCII export utility)
   0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 5 5 5 4 4 4 4 3 3 3 3
   0 0 0 0 0 0 0 0 0 3 3 3 3 4 4 4 6 6 6 5 5 5 5 4 4 4
   etc., etc.
   

3. Create a new LOCATION and "permanent" MAPSET in GRASS.
4. When running GRASS under this new location and mapset, type "r.in.ascii". This will run an ASCII to binary conversion program. All GRASS support files will be created automatically.

I.6.2 GRASS to ERMapper:

1. To create an ASCII file that ERMapper can import, while running GRASS type
   
   "r.stats -1m <map_to_export> output=<ascii_text_file>"
   
   where <map_to_export> is the GRASS raster layer being exported, and <ascii_text_file> is the name of the ascii text file to be imported into ERMapper.
2. In ERMapper select: UTILITIES/IMPORT RASTER (A-F)/ASCII BSQ. ERMapper will then present you with a list of import options (most of these can usually be left blank). After hitting "GO", you will then be prompted for the number of rows and columns in the input dataset

I.6.3 ERMapper to Workstation Arc/Info GRID:

1. In ERMapper, select: UTILITIES/EXPORT RASTER/ASCII. This will simply create a text file with raw reflectance values. Make sure to save this file to your home directory.
2. Use the "mv" command to move the ASCII file to the desired ARC/INFO data directory (called an ARC/INFO Workspace).
3. Start up ARC/INFO.
4. Append a header section to this ASCII file as shown below using jot (see ASCIIGRID command in ARC/INFO on-line help):
   

   ncols 417
   nrows 229
   xllcorner 0
   y11corner 0
   cellsize 30
   nodata_value 0  (this is optional)
   {ascii data follow}

5. Use the "ASCIIGRID" command in ARC to convert this ASCII file to an ARC/INFO GRID file.

I.6.4 Workstation Arc/Info GRID to ERMapper:

1. Use "GRIDASCII" command in ARC to produce an ASCII file.
2. Remove the header section of this file using jot.
3. In ERMapper select: UTILITIES/IMPORT RASTER (A-F)/ASCII BSQ. ERMapper will then present you with a list of import options (most of these can usually be left blank). After hitting "GO", you will then be prompted for the number of rows and columns in the input dataset.

I.6.5 Workstation Arc/Info Coverage to ERMapper:

1. In ARC/INFO use the "ARCDXF" command to create an AutoCAD .dxf file.
2. While running ERMapper with an active algorithm displayed, select "Annotation Overlay" under the "Add" button in the Overlays window.
3. Load the AutoCAD file into this new overlay using the Dataset glyph.
4. This vector overlay can be included in a hardcopy print of the algorithm.

I.6.6 ERMapper to IDRISI:

1. Follow all steps listed under section I.6.1, "ERMapper to GRASS:".
2. Create an IDRISI-compatible file in GRASS by typing:
   

   	r.stats -1m <map_to_export> output=<text_file>

   
   
3. Since UNIX and DOS use different conventions for carriage returns, you need to use the UNIX command
   

   	to_dos <UNIX file> <MSDOS file>.

   
   
4. Rename the text file as a suitable IDRISI image file with a .img extension (e.g., water.img).
5. Create an IDRISI (.doc) header file for the .img file using the DOCUMENT module. Specify integer data type, and ASCII type. Also enter a specific min and max value for your raster layer manually (don't let IDRISI calculate this when is asks you).

I.6.7 IDRISI to ERMapper:

1. Using IDRISI's CONVERT module, convert the IDRISI image(s) to an ASCII integer format.
2. Move the resultant IDRISI".img" file to your home directory on the UNIX system.
3. Since UNIX and DOS use different conventions for carriage returns, you need to use the UNIX command:
   

   	to_unix <UNIX file name> <MSDOS file name>.

   
   
4. In ERMapper select: UTILITIES/IMPORT RASTER (A-F)/ASCII BSQ. ERMapper will then present you with a list of import options (most of these can usually be left blank). After hitting "GO", you will then be prompted for the number of rows and columns in the input dataset.

I.6.8 Scanned images to ERMapper:

1. Use the CEO scanner or one of the public scanners in YCC, CCL or Dunham to scan your map/aerial photograph. Save as an uncompressed, 8-bit color or grayscale tiff file.
2. Use "fetch" to ftp this file to your home directory in the CEO lab. Make sure to use the "raw data" transfer type (transfer mode) not "macbinary", "text", or "auto". Make sure the filename ends in".tiff".
3. Import the image into ERMapper using the "Utilities/Import Graphics Formats/TIFF" option.

I.7 Annotations

Some of the features included in the annotation and Map composition overlays are very powerful and useful. For example, scale bars, north arrows, lat/long grids, circles, labeling, point features, etc... To take full advantage of these features you should learn the ERMapper vector file format, which is described in the Open Standards and Dataset and Library Standards manuals. However, to make simple annotations to your image, for example to add a title, labels, arrows and so on, there is an easier and better looking option. This is Showcase, SGI's document preparation software.

Showcase has extensive on-line help, which you should use to figure out the details of how to create the effects you want. However, here is an outline of the steps you would have to take to create a print of your image with showcase annotations:

1. Save your algorithm.
2. Use ERMapper's SGI image hardcopy program (described above in the Hardcopy section) to create a copy of your algorithm in SGI format.
3. Start Showcase (type "showcase" at the shell prompt).
4. From the File menu, choose "Insert/Image", then select the image you just created in step 2. (Be sure to use "insert", not "open" for this step!).
5. Now you can use all the showcase tools (text, lines, shapes etc..) to mark up your image. Two of the most common ones are the Block text and Label text tools, symbolized by a "T" with lines behind it and an "L" with an insertion point next to it, respectively. The Block text creates an object that fills the page. Text wraps around to the next line in Block text. The Label text creates an object that is only as large as the text you type into it. You must use a return to get a new line in a Label text object. See the on line help for details.
6. To make your final print, use the "Save to File" option in the print dialog box. This creates a postscript file that you can then send to the printer in the lab or to MIS.

I.8 Frequently Asked Questions

In many cases it's pretty arbitrary which projection you should use. One overriding factor in this decision is whether you already have some other information that is in a given projection. If so, certainly use that projection. See the discussion in the Geometric Corrections section of the Guide for more information on map projections in general, or the discussion in the ERMapper Users Forum for more information on how ERM deals with map projections.

2. Someone is giving me an image, what format should I ask for?

There are two formatting considerations when obtaining your own imagery. First, you need to decide on the physical media that will transport the image from the source system to the CEO. We can handle, in order of preference; ftp transfer over the Internet (by far the easiest), CD-ROM, and 8mm Exabyte tapes (by far the biggest pain). If you do get a tape, get as much information as possible about exactly how the tape was made. If you are getting the tape from a colleague, ask for the command used to write the data to the tape, and show him/her section 0 for hints on the format of the tape. If at all possible, try to use ftp rather than tapes. See the section above on anonymous ftp for details. If you get something commercially, ask for the formatting document describing the tape contents. This is very important for successfully reading tapes!

Second, you need to decide on the format of the image file. ERM has a long list of formats that it can import (see the options under "Utilities/Import Raster" for a complete list). Some common options that work well are: a flat binary file (you need to know the number of rows/cols and how many bytes per pixel), an uncompressed TIFF image (TIFF is tricky because there are many variations on this format, but if you get the uncompressed form there should be no problem), the Mac PICT format, and an ASCII grid. ERDAS IMG files and Arc/Info export format files can also be used.

3. I get errors when I try to display a file that was previously O.K.

There could be many reasons for this, but the most common problem is running out of disk space. If you exceed your disk quota, you will not be able to write to the disk. This problem occurs if, for example, you are near your quota limit and edit a file. If you try to save it you may exceed your limit. These changes will not be saved. From the Unix shell you can check your disk space with the "quota" command. The output will tell you how much space you are using (usage) as well as how much you can use (quota and limit). If you exceed the quota you will have seven days to reduce your data storage to less than the quota amount. If this is not done, the system will automatically lock you out of your account and you will need to have the System Administrator (Larry Bonneau) modify the system before you can log in again. If you exceed the limit amount, any writing to disk will stop. There are many symptoms of running out of disk space, too many to list here. The best solution is to keep track of how much space you are using and remove old files before you run out of space.

4. When I start ERMapper all the button colors are funny.

This happens if your display is trying to access too many colors at once. Try quitting all of your open applications (including ERMapper) and logging out, then log back in again. Start ERMapper first, before starting any other applications. (You will get the better performance from ERMapper with no other applications running anyway).

5. When I log in I don't get any icons (for CD-ROM/directories/dumpster) on the right side of my screen.

There are two possible problems. If you get a message about the objectserver (maybe immediately when you log in, maybe in as long as a few minutes afterwards) send an email to Larry Bonneau. You can continue using the machine if necessary, but you may want to move to a different one if you like to use the icons. If you don't get a message about the object server within a few minutes try the following. Log out and log in to another machine. In your home directory, delete the subdirectory: .desktop-terraX", where "X" is the number of the terra that wouldn't display your icons. Now try logging back into the first machine again. If you did this correctly, you should get a message about not having logged into this machine before, just click OK.

6. How do I compute the area (number of cells or percentage) of each class in my classified dataset within a given region (subset of the entire dataset)?

An example of this would be to take a classified image of Connecticut that distinguished development, forest, and grass and compute the percentage of each of those three classes within 5 miles of New Haven, Hartford and Danbury. Incredible as it may seem, ERMapper does not have a simple function that will do this, so you need to fool ERMapper a bit to get the numbers you want. The strategy is to display each region in a separate overlay in an algorithm, run the algorithm at full resolution, save it, and use the histograms ERMapper saves for each overlay to count the number of pixels. For just a handful of regions it's pretty easy to build such an algorithm by hand. However with many regions, this approach gets cumbersome quickly, so we have written two programs to help with this. The steps are as follows:

1. Start with a classified dataset. Define regions for this dataset using "Edit|Edit/Create Regions" (new regions) or "Process|Polygon<-->Region Conversion|Vector dataset polygons to Regions" (make regions from an existing vector (annotation) file.
2. Make sure you are in the same directory as the classified dataset and run the program "create_alg". The syntax of this program is "create_alg dataset.ers", where you replace "dataset.ers" with the name of the header file of your classified dataset. The program create_alg will generate an algorithm in the same directory as the classified dataset called "dataset_compute_areas.alg", where "dataset" is the name of your classified dataset.
3. Start ERMapper, load, run and save the algorithm generated by create_alg. It is O.K. to use the same name - you can always run create_alg again if you want to.
4. Go back to the Unix shell and run the program "dump_alg". The syntax for this program is "dump_alg filename.alg" where "filename.alg" is the algorithm that you saved in step 3. Dump_alg will create two new files called "dataset_areas.csv" and "dataset_cells.csv", again "dataset" is the name of your classified dataset. These two files are comma delimited text files that contain a matrix of numbers. The rows are regions (region names are in quotes in the first column) and the columns are either number of cells (cells.csv) or areas (areas.csv) of each class within that region (class names are in quotes in the first row). One can read these files right from the screen using a text editor or easily import them into a spreadsheet for more sophisticated processing.

An example of this process would go like this. Let's say I classify a Landsat image of Connecticut into three classes: urban, forest and grass. This classified dataset is called "conn_class" (so the header is "conn_class.ers"). I am interested in comparing the areas around New Haven, Hartford and Danbury, so I define three polygon regions for this dataset using "Edit/Create Regions" and the annotation tools. (Regions must be polygons; they cannot be circles or squares etc.) Now I run create_alg using syntax like this "create_alg conn_class.ers". This generates an algorithm in the same directory called "conn_class_compute_areas.alg". I start ERMapper, load, and run "conn_class_compute_areas.alg" then save it, clicking "OK" when ERMapper warns me that it already exists and I am going to overwrite it. Now I run dump_alg on the newly saved algorithm; "dump_alg conn_class_compute_areas.alg". This generates two new files called "conn_class_cells.csv" and "conn_class_areas.csv". The first line in each of these files will be "Urban","Forest","Grass", and the first column will be "New Haven" "Hartford" "Danbury". The numbers in the rows of "conn_class_cells.csv" will be a number of pixels. The numbers in the rows of "conn_class_areas.csv" will be the same, but multiplied by the area of a pixel in that dataset, as defined in the dataset header.

II. Major Characteristics of Remotely Sensed Data

II.1 Resolution Considerations

1. Spatial: The spatial resolution of a sensor is perhaps the most intuitive or obvious characteristic of an image. Spatial resolution may be defined as the area represented by one pixel in an image, or as the size of the smallest object on the ground which can be distinguished by the sensor. However, this measurement is usually smaller than the smallest object that can clearly be distinguished by someone looking at an image.
   A sensor's instantaneous field of view, IFOV, (defined as the ground area sampled (viewed) at one time) may or may not be the same as its spatial resolution, depending on the method used for sampling.
   
2. Spectral: A sensor's spectral resolution is defined by the region (bandwidth) of the electromagnetic spectrum over which it is sensitive.
3. Radiometric: Radiometric resolution refers to the number of possible values in each band of data. A high radiometric resolution allows finer distinction between values.
4. Temporal: The time between successive passes over the same region defines temporal resolution.

II.2 Passive Sensors

II.2.1 Spectral Signatures

In principle, minerals may be uniquely identified based on their spectral signature. However, in practice, the spectral and spatial resolution of most passive sensors is too poor for the degree of detail necessary to do this. Figure 2 illustrates the spectral signatures of several minerals. Notice that broad variations (over several microns) occurs between some minerals, but others have the same general shape and may only be distinguished by subtle variations in the intensities of their signatures, or by the presence/absence of distinctive, narrow absorption bands in the spectra. The spectral signatures of geologic objects is further complicated by variations in weathering and surface cover, as well as the blending of individual component signatures into one combination signature for each pixel. Although these limitations might prevent identification of a specific mineral or rock from an image alone, areas with similar signatures may be identified and objects may be placed in broad groups based on their spectral signatures. The actual identification of these groups may then be confirmed with ground truth. This approach works best in regions with little obscuring ground cover such as deserts and mountains above the tree line.

Sensors equipped with thermal bands and with sufficient temporal resolution may be used to estimate the thermal inertia of an area, potentially enabling an analyst to more accurately identify an object or composition of a region.

Applications involving plant life are generally better able to take advantage of the spectral information contained in an image acquired by current day sensors for several reasons. One major reason for this is that vegetation has a very distinctive spectral signature in the visible and near infrared (NIR) which is detectable by sensors with low spatial, spectral, or radiometric resolution. Figure 3, figure 4, and figure 5 illustrate typical reflectance spectra from a few types of vegetation. The key features in these plots are vegetation's high NIR reflectance in general, and the relative reflectance of grass, deciduous and coniferous trees. Chlorophyll and water are the substances that dominate the spectral signature of plant life. Different concentrations of these compounds produce marked differences in the spectra of different plants, allowing one to make estimates of plant health, biomass, and species identification. Figure 6, figure 7, and figure 8 demonstrate how the spectral signature of vegetation changes with variations in water content, biomass, and plant health.

The spectral signature of water bodies themselves are largely dependent on water depth and suspended matter [1]. Radiation received at a sensor will have penetrated the water to some depth, reflected off suspended matter or off the bottom, then traveled back through the water to the surface. Water is a strong absorber of EM radiation, especially at longer wavelengths; blue light will travel through water for a few tens of meters or more while infrared light is absorbed almost immediately at the surface. The spectral signature of a water body therefore is composed of the spectral signature of the reflecting surface (the bottom or suspended particle) minus whatever is absorbed or scattered as the light rays travel through the overlying water. This effect is shown in figure 9, which plots the spectral reflectance of water with a sandy bottom at various depths. Notice that the overall shape of the plot remains similar at different depths, but that the intensity drops off as the water gets deeper.

Water will also affect the signature of bare soil or sand. Figure 10 is a plot of wet and dry sand reflectance. Notice that the water decreases the reflectance of the sand more severely at longer wavelengths. This effect is also illustrated in figure 11 which plots three spectra acquired on a wet lawn. The plot demonstrating high reflectance in the NIR is from grass on this lawn. The grass in the middle plot is partially covered with water so that only the tips of the blades of grass stick out of the water. A puddle of water covers the grass in the third plot by about 2-3 inches. Note that the water decreases the reflectance at all wavelengths, but that this effect is stronger at longer wavelengths.

II.2.1.1 Key References:

II.2.2 Patterns and Textures

Spatial analysis can be a powerful tool for identifying and characterizing large-scale features such as folds, faults, and drainage patterns. Remotely sensed data provide the ability to efficiently map extremely large areas.

Spatial analysis is generally less useful than spectral analysis for identifying different groups of plant life. In general variations in plant texture can be subtler than large geologic features and can occur quite frequently, increasing the effect of (typically) high frequency noise in the data. However, patterns of plant life, perhaps determined through spectral techniques, can often give clues to the underlying geology of an area. For example, a transition from one species of plant to another might indicate a transition from one soil type to another.

II.3 Active Sensors

II.3.1 RADAR

RADAR is an acronym for radio detection and ranging. The earliest radar systems operated in the radio band of the electromagnetic spectrum from approximately 1 to 10m. Modern radar systems transmit in the shorter wavelength microwave band from approximately 0.8cm to 1m. A radar system produces frequent, short bursts of microwave energy and measures the strength of the reflected echo, sometimes referred to as backscatter. Longer-wavelength radar systems can penetrate clouds and some surfaces such as sand and snow. This makes it an ideal tool for imaging tropical regions that have almost constant cloud cover. It has also been used to locate ancient stream beds in desert areas.

Two common forms of radar are not used to image the earth's surface. One is the Doppler radar system, otherwise known as the radar gun. It uses Doppler frequency shifts to measure relative differences in speed of the reflector and target. Plan Position Indicator (PPI) radar systems feature a rotating antenna with a circular sweeping display. These are commonly used for weather forecasting and air traffic control.

Side Looking Airborne Radar (SLAR) systems are used to image the earth's surface. These systems have an antenna fixed to the bottom of an airplane or spacecraft that is typically pointed to the side of the flight path. The side looking scheme was devised so that airplanes could fly parallel to the border of a hostile nation and "look" into the enemy territory.

Radar systems transmit energy in the microwave portion of the electromagnetic spectrum using wavelengths from approximately 0.75 cm to 100 cm. This range is divided into 8 bands, each identified by an alphabetic code (Table 3). These random letter designations were assigned during World War II as a security measure.

POLARIZATION - Radar systems transmit energy in either a horizontal (H) or vertical (V) polarized plane. Systems generally receive reflected energy in the same plane as was transmitted. These are referred to as HH or VV systems. Horizontal systems are usually better at discriminating rectangular features such as buildings and fields, while vertical systems are usually better at discriminating between vertical features such as trees. More sophisticated systems have two receiving antennas and capture reflected energy with the opposite polarity. These are referred to as HV or VH systems. Some advanced radar systems can transmit and receive both polarities and produce four images of an area; HH, VV, HV, and VH. These multi-polarity sensors offer greater information, similar to the capabilities of multi-spectral images used by passive sensors.

INTERPRETATION - Satellite images produced by passive sensors record the variations in reflectivity and absorption of objects across the electro-magnetic spectrum. Interpretation of radar images is significantly different than interpretations of passive sensors. Radar images record variations in structure, texture, and electrical properties of the targeted surfaces.

Surface slope has a significant impact on the macro-scale interpretation of radar images. Slopes that face an antenna (foreslopes) are brighter than slopes facing away from the antenna. As the foreslopes approach perpendicular to the radar beam, their reflectance becomes brighter. This is known as foreslope brightening. Foreslopes take less time to image than backslopes. This phenomenon, called foreshortening, results in foreslopes being recorded shorter than they really are. Objects with very steep foreslopes will appear to lean toward the radar source. This is a result of the radar beam intercepting the top of the object before the base and is known as layover.

Surface roughness produces micro-scale relief on radar images. Radar-smooth surfaces will cause the transmitted energy to reflect away from the antenna. These surfaces appear dark on an image. Typical radar-smooth surfaces are calm water and paved roads. Radar-rough surfaces produce a diffuse reflection, resulting in a brighter image. Examples of radar-rough surfaces are cobbles, old-growth forest canopies, and surface waves on water. Apparent roughness on radar images is also dependent on radar wavelength and relative angle between the radar beam and the target surface.

The dielectric constant is a measure of an objects' ability to conduct or reflect microwave energy. For most objects, this phenomenon has no significant impact on a radar image. As surface moisture increases, the dielectric constant and reflectivity increase. This would make a recently irrigated field appear brighter than a similar field without irrigation. Metallic objects such as bridges and railroad tracks act as amplifiers and appear very bright on radar images.

One of the first places to look is in your text book by Lillesand and Kiefer. Chapter 8 - Microwave Sensing provides a thorough background on radar systems and their special image processing techniques. Journal articles are also a source of relevant information. For example, the December 1995 issue of Photogrammetric Engineering & Remote Sensing (PE&RS) has three articles related to the use of Synthetic Aperture Radar (SAR) systems and sea ice. Other sources of information about radar are the remote sensing software packages used at the CEO, and websites for the various radar systems.

The ERMapper Applications Manual has a chapter dedicated to SAR imagery in mineral and oil exploration. Two case studies are described, outlining the reasons why radar imagery was appropriate for these projects. The Applications Manual is available in the CEO lab. It can also be found on-line by selecting the Help button from the main ERMapper menu. ERMapper has another on-line manual for radar. This is the ER Radar Manual. It provides a detailed description of specific processing and analysis techniques and algorithms used within ERMapper.

Two other remote sensing software packages available at the CEO are ERDAS Imagine and ENVI. Each application has a set of tools and on-line documentation for processing radar images. In addition, ENVI has several tutorial exercises to learn how to process and interpret radar data. The ENVI tutorial manual can be found in the CEO lab.

The World Wide Web is a vast source of information, some of it even on radar! You should begin by going to the CEO Links section of the CEO Home Page. There is a section for Radar which includes links to several of the more important radar providers.

The CEO also has sample images and browse software for RADARSAT, SIR-C, and JERS-1 data. These image samples should help you to understand the capabilities and challenges involved with using radar data. These samples are stored in the cabinet that contains the CEO Data Archive. See any of the CEO staff if you wish to work with these data sets.

II.3.2 LIDAR

LIDAR is an acronym for light detection and ranging. This active remote sensing system transmits pulses of laser light from an airborne platform and records the time delay of the reflection to measure the distance between the aircraft and the surface. When global positioning systems are integrated with the lidar, surface maps can be generated with sub-meter accuracies.

Lidar systems are able to record multiple returns at each pulse. This means that multiple surfaces can be measured at the same time. It is possible to map a forest canopy and the forest floor, or the surface and depth of a water body.

II.4 Sensor Descriptions

Figure 12 is a graphic comparison of the spatial and spectral resolutions of each of the various sensors discussed below. Each band on each sensor is represented by a rectangle on this plot. The x-axis on this plot is calibrated to wavelength (log scale to show detail at short wavelengths), so each band's width in the x-dimension spans the wavelengths to which it is sensitive. The y-axis on this plot is arranged by sensor, and each box's y-dimension is proportional to its spatial resolution This convention is the same for plots 12a and 12b, the difference being that the spatial scale on the y-axis changes.

II.4.1 Landsat Sensors

II.4.1.1 Historical, Orbital and Resolution Overview:

II.4.1.1.1 RBV

Unfortunately, these sensors were plagued with technical problems and they were replaced on Landsat 4 with the Thematic Mapper sensors.

II.4.1.1.2 MSS

band 1: (green, 0.50-0.60µm) This region corresponds to the green reflectance of healthy vegetation and is useful for mapping detail, such as depth or sediment in water bodies. Cultural features such as roads and buildings also show up well in this band.
band 2: (red, 0.60-0.70µm) Chlorophyll absorbs these wavelengths in healthy vegetation. This band is useful for soil and geologic boundary discrimination.
band 3: (near IR, 0.70-0.80µm) The near IR is responsive to vegetation biomass and health.
band 4: (near IR, 0.80-1.10µm) Band 4 is very similar to band 3. It is used for vegetation discrimination, penetrating haze, and water/land boundaries.

II.4.1.1.3 TM

band 1: (blue, 0.45-0.52µm) Water increasingly absorbs EM radiation at longer wavelengths, so band 1 provides the best data for mapping depth/detail of water covered areas. It is also used for soil/vegetation discrimination, forest mapping and distinguishing cultural features.
band 2: (green, 0.52-0.60µm) Like MSS band 1, this corresponds to the green reflectance of chlorophyll in healthy vegetation.
band 3: (red, 0.63-0.69µm) This band is useful for distinguishing plant species, soil and geologic boundaries.
band 4: (near IR, 0.76-0.90µm) Band 4 corresponds to the region of the EM spectrum which is especially sensitive to varying vegetation biomass. It also emphasizes soil/crop and land/water boundaries.
band 5: (mid IR, 1.55-1.74µm) This region is sensitive to plant water content which is a useful measure in studies of vegetation health. This band is also used for distinguishing clouds, snow and ice.
band 6: (thermal IR 10.40-12.50µm) This region of the spectrum is dominated completely by radiation emitted by the earth and is useful for crop stress detection, heat intensity, insecticide applications, thermal pollution and geothermal mapping.
band 7: (mid IR, 2.08-2.35µm) This region is used for mapping geologic formations and soil boundaries. It is also responsive to plant and soil moisture content.

ETM+

II.4.1.2 Data Sources and Pricing:

One can search each of these archives online through the Global Land Information System (GLIS) which is accessible via telnet to "xglis.cr.usgs.gov" or via the WWW at http://edcwww.cr.usgs.gov/glis/glis.html.

The current pricing scheme for Landsat data is as follows:
EROS: MSS data is available for $200 per scene on 9-track or 8mm tapes. EROS performs basic radiometric corrections to the data such as decompressing the band 4 data, and will sell images either geometrically uncorrected, or geometrically corrected to one of a few possible user-specified map projections. The general public can purchase images at least 10 years old directly from EROS. More recent images must be purchased from EOSAT. The U.S. Government and its Affiliated Users (non-commercial research affiliates) can purchase recent images from EROS. Landsat 7 ETM+ images will be available from EROS beginning one year from the date of a successful satellite launch. Users ordering data from the EROS TM archive can choose between system corrected or precision corrected data. System corrected data have had radiometric and basic geometric corrections applied and cost $425. Precision corrected data have additional geometric corrections applied to increase the geodetic accuracy of the navigation information included with the image and cost $600. The number for Customer Services at EDC is (605) 594-6151.

EROS now distributes Landsat TM scenes. For images more than ten years old are the costs are $425 for system corrected scenes and $600 for precision corrected scenes. The U.S. Government and its Affiliated Users can purchase more recent images for this same price if it is already in the EROS archive. If the scene is not in the EROS archive the cost of the image varys, with a maximum cost of approximately $2,500.

EOSAT: TM data from EOSAT costs $4400 per scene for basic radiometric and geometric processing. Users may order a number of other digital products from EOSAT. Additional pricing over and above the basic $44000 charge for the scene reflects the amount of processing which has been applied to the raw data. The number for customer services at EOSAT is 1-800-344-9933.

LGSOWG: MSS data from one of the other Landsat Ground Stations costs $1200, and TM data costs $4000. The complete list of stations and contacts is available through the GLIS system.

II.4.1.3 Key References:

II.4.2 SPOT

II.4.2.1 Historical, Orbital and Resolution Overview:

The SPOT ground track has a swath width of 60 km nadir and 80 km off-nadir, and images are stored in 60 km along-track segments. Unlike the MSS and TM instruments, SPOT acquires its data using a 'push broom' method as opposed to a side-scanning mirror. The platform carries two identical high-resolution-visible (HRV) scanners, which may be used in either of two modes

SPOT 4 was successfully launched on March 24, 1998. The satellite features the new high-resolution visible/infrared (HRVIR) sensor package and a "vegetation" sensor package. The HRVIR differs from the HRV in that it includes a new band in the short-wave infrared range that is very sensitive to soil and leaf moisture. The "vegetation" sensor package captures data using the same four bands of the electromagnetic spectrum but has a pixel resolution of 1km and a swath width of 2,250 km. This will provide near-global coverage daily.

Panchromatic mode

band 1: (0.51-0.73µm) The radiometric information content of this band is very similar to that of a black and white photograph. SPOT panchromatic images are not very useful by themselves for classification of landscapes. However, their very high spatial resolution makes them useful for visual interpretation, digitally sharpening lower-resolution multi-spectral data, and generation of stereo pairs.

Multi-Spectral mode

band 1: (green, 0.50-0.59µm) Like MSS band 1, this corresponds to the chlorophyll reflectance of healthy vegetation.
band 2: (red, 0.61-0.68µm) Like MSS band 2, this band is useful for distinguishing plant species, soil and geologic boundaries.
band 3: (near IR, 0.79-0.89µm) Like MSS band 4, this band is sensitive to varying vegetation biomass and emphasizes soil/crop and land/water boundaries.
band 4: (short-wave IR, 1.5-1.75µm) (HRVIR only) This band has a high degree of sensitivity to soil and leaf moisture.

II.4.2.2 Data Sources and Pricing:

II.4.2.3 Key References:

On the web at: http://www.spot.com/

II.4.3 AVHRR

II.4.3.1 Historical, Orbital and Resolution Overview:

The AVHRR transmits data in three modes. Data is continuously broadcast at full spatial resolution, and may be received/stored by any station within line of sight that is capable of capturing the signal. Data acquired directly from the satellite in this manner is known as High Resolution Picture Transmission (HRPT). Full resolution is also stored using onboard tape recorders for selected regions then dumped to a ground station once per orbit. Datasets recorded then dumped in this manner are known as Local Area Coverage (LAC) data and have the same resolution characteristics as HRPT. In addition to these high resolution AVHRR data, lower spatial resolution data sets, known as Global Area Coverage (GAC), are maintained for all regions. These lower resolution datasets are produced by sampling every third scan line and averaging four out of every five pixels along each scan line, resulting in approximately 4 km resolution.

The AVHRR's four or five spectral bands are used primarily for mapping large areas, especially when good temporal resolution is required. Applications include snow cover and vegetation mapping; flood, wild fire, dust and sandstorm monitoring; regional soil moisture analysis; and various large-scale geologic applications.

band 1: (visible, 0.58-0.68µm) The blue-green region of the spectrum corresponds to the chlorophyll absorption of healthy vegetation.
band 2: (near IR, 0.725-1.10µm) This region is sensitive to varying vegetation biomass and emphasizes soil/crop and land/water boundaries.
band 3: (IR, 3.55-3.93µm) A thermal band which detects both reflected sunlight and earth-emitted radiation and is useful for snow/ice discrimination and forest fire detection.
band 4: (thermal IR, 10.30-11.30µm) A band useful for crop stress detection and locating/monitoring geothermal activity. This channel is also commonly used for water surface temperature measurements.
band 5: (thermal IR, 11.50-12.50µm) Similar to band 4, this channel is often used in combination with band 4 to better account for the effects of atmospheric absorption, scattering, and emission.

II.4.3.2 Data Sources and Pricing:

Many other sites offer AVHRR data in various formats, covering various locales with varying pricing schemes. Generally these are sites with HRPT stations that post whatever they get for some short time period. Good places to start looking are the University of Miami, Louisiana State University, University of Hawaii, University of Colorado and Dundee University in England.

II.4.3.3 Key References:

II.4.4 GOES

II.4.4.1 Historical, Orbital and Resolution Overview:

GOES 7

In the imaging mode, three bands of information are routinely acquired:
Visible band: The GOES visible band is sensitive to most of the visible region of the spectrum, from about 0.4 to 0.7 µm. Radiation in this region of the spectrum is entirely composed of reflected or backscattered sunlight and is therefore indicative of the earth's albedo, an important quantity for computing the radiation budget of the earth's atmosphere. This band is only activated during the day. Visible images are acquired at 1 km resolution, but are often distributed at 8 or 16 km resolution for practical purposes.

IR band: The GOES IR band that is usually acquired in imaging mode is centered around 11.2 µm, located in the infrared window. Emitted radiation from the earth's surface and atmosphere dominates this region of the spectrum. Images of this band are usually printed as negatives so clouds (low IR emitters, due to their low temperature) appear white. This band is commonly used to estimate a temperature profile for the earth's atmosphere, among other applications. IR images are commonly acquired at 7 km resolution and resampled to 8 or 16 km resolution for distribution.

Water vapor band: The GOES water vapor band most commonly used for imaging is sensitive to radiation around 6.7 µm, though bands centered at 12.7 and 7.3 µm (also sensitive to H₂O emission), and 13.3 (CO₂) or 3.9 (window) µm are sometimes substituted. Images of this band are also commonly printed as negatives for the same reason as the IR band. Water vapor images give a picture of upper-tropospheric moisture distribution and are commonly acquired at approximately 16 km resolution.

The sounding mode of the VAS is primarily used for research purposes because it cannot operate independently of the imager.

GOES 8 and 9

The imager on the GOES I-M series has five spectral bands:
band 1: (visible, 0.52-0.72 µm) The visible band produces images of clouds and the Earth's surface in clear sky with 1 km resolution.
band 2: (mid IR, 3.78-4.03 µm) Similar to the AVHRR band 3, this region of the spectrum is dominated by reflected sunlight during the day, and emitted thermal radiation at night. It is useful for daytime snow/ice/water discrimination, night time measurements of sea surface temperature, and hot spot (volcano, forest fire) monitoring. Band 2's resolution of 4 km is a major improvement over GOES-7's 13.8 km resolution in a similar band.
band 3: (water vapor, 6.47-7.02 µm) GOES-8 images of upper-tropospheric water vapor have a slightly narrower spectral range, and increased radiometric (10-bit vs. 8-bit) and spatial (8 km vs. 13.8 km) resolution relative to the corresponding GOES-7 data.
band 4: (Thermal IR, 10.2-11.2 µm) Similar to the AVHRR band 4, this band provides cloud top and sea surface temperatures day and night at 4 km resolution.
band 5: (Thermal IR, 11.5-12.5 µm) Similar to the AVHRR band 5, this band is slightly more sensitive to water vapor absorption than band 4, and should provide useful sea surface and low-level moisture data, especially when used in conjunction with band 4.

The GOES I-M sounder acquires data in 18 infrared bands and one visible band. See Menzel and Purdom for detailed descriptions of both the imager and sounder instruments.

II.4.4.2 Data Format and Availability

Weather information can be found at http://www.atmos.uiuc.edu/wxinfo.html
The GOES Project home page http://rsd.gsfc.nasa.gov/goes/ has many pointers to other servers

II.4.4.3 Key References

II.4.5 IKONOS

II.4.5.1 Historical, Orbital and Resolution Overview:

The IKONOS satellite was launched on 24 September 1999 at the Vandenberg Air Force Base, California. It is the world's first commercial, high-resolution satellite. It has 1-meter ground resolution for panchromatic band (nominal at <26deg off nadir) and 4-meter for multispectral bands (nominal at <26deg off nadir). The IKONOS has a swath width 13km at nadir and an along track distance of 13km for an individual scene. The sun-synchronous orbit has an altitude of 423 miles/681 kilometers. Revisit frequency is 2.9 days for 1-meter resolution; and 1.5 days for 1.5-meter resolution.

Panchromatic mode: In panchromatic mode, the ground resolution is 1-meter, and data are stored in only one band which spans the visible to near infrared region of the electro-magnetic spectrum.
band 1: (0.45-0.90µm) The radiometric information content of this band is very similar to that of a black and white photograph. The very high spatial resolution makes these images useful for visual interpretation and for digitally sharpening lower-resolution multi-spectral data.

Multispectral mode: In multispectral mode, the ground resolution of each band is 4-meter, and data are stored in four spectral bands which are same as bands 1,2, 3 and 4 of Landsat 4&5.
band 1: (blue, 0.45-0.52µm) Same band range as Landsat 4&5 TM band 1. Water increasingly absorbs EM radiation at longer wavelengths, so band 1 provides the best data for mapping depth/detail of water covered areas. It is also used for soil/vegetation discrimination, forest mapping and distinguishing cultural features.
band 2: (green, 0.52-0.60µm) Same band range as Landsat 4&5 TM band 2, this corresponds to the green reflectance of chlorophyll in healthy vegetation.
band 3: (red, 0.63-0.69µm) Same band range as Landsat 4&5 TM band 3. This band is useful for distinguishing plant species, soil and geologic boundaries.
band 4: (near IR, 0.76-0.90µm) Same band range as Landsat 4&5 TM band 4. It corresponds to the region of the EM spectrum which is especially sensitive to varying vegetation biomass. It also emphasizes soil/crop and land/water boundaries.

II.4.5.2 Data Format and Availability

In the near future, Space Imaging will offer the ability to fully browse and buy imagery, products and services through their website, http://www.spaceimaging.com/level2/level2buy.htm

II.4.5.3 Key References

On the web at: http://www.spaceimaging.com/aboutus/satellites/IKONOS/ikonos.html

II.4.6 RADARSAT

II.4.6.1 Historical, Orbital and Resolution Overview:

The RADARSAT satellite was launched in November of 1995 and has been operating continuously since that time. The RADARSAT system was developed in Canada and launched by NASA in exchange for data access rights. RADARSAT-1 has an orbital altitude of 798 km and an inclination of 98.6 degrees and circles the earth 14 times a day. It has a sun-synchronous orbit which allows it to rely on solar rather than battery power and provides satellite overpasses at the same local mean time. RADARSAT-2 is planned to launched in the year 2001.

The Synthetic Aperture Radar (SAR) sensor on RADARSAT-1 can be directed from an incidence angle of 10 to 60 degrees, in swaths of 45 to 500 km in width. This produces image resolutions ranging from 8 to 100 meters. RADARSAT-1 has a repeat cycle of 24 days, but covers the Artic daily and can reach any part of Canada in three days. Using the 500 km swath width, equatorial coverage can be repeated every six days.

SAR Characteristics: RADARSAT-1 operates in the C-band at a frequency of 5.3GHz and a wavelength of 5.6 cm. The antenna polarization is HH, meaning that the system transmits and receives energy in the horizontal plane.

II.4.6.2 Data Format and Availability

The commercial distribution of RADARSAT images is handled by RADARSAT International. Images cost several thousand dollars apiece. Specific missions can be planned to acquire images at required locations, times, and resolutions for an additional fee. RADARSAT International can be contacted at: http://www.rsi.ca/pricelist/price.htm

The scientific and educational community can acquire low-cost imagery from the NASA funded Alaska SAR Facility (ASF) housed at the University of Alaska Fairbanks. To learn more about the ASF and data availability, check out their web site at: http://www.asf.alaska.edu/

II.4.6.3 Key References

II.4.7 JERS

II.4.7.1 Historical, Orbital and Resolution Overview:

The Japan Earth Resources Satellite (JERS) was launched on 11 February 1992. It acquired images from 24 August 1992 to 31 December 1996. JERS-1 has an orbital altitude of 570 km and an inclination of 98 degrees. It has a repeat cycle of 44 days and operated in s sun-synchronous mode.

The SAR sensor on JERS-1 has an off-nadir angle of 35 degrees, with a swath width of 75 km. This produces an image resolution of 18 meters. JERS-1 operates in the L-band at a frequency of 1275 MHz.

JERS-1 also has an optical sensor package OPS. OPS has a 75 km swath width and a pixel resolution of 18m x 24m. The sensor captures reflected energy in seven spectral bands from the visible to mid-infrared wavelengths. It can also produce stereoscopic images.

band 1: (green. 0.52-0.60 µm). This corresponds to the green reflectance of chlorophyll in healthy vegetation.
band 2: (red. 0.63-0.69 µm). This band is useful for distinguishing plant species.
band 3: (near IR. 0.76-0.86 µm). This band is especially sensitive to plant biomass.
band 4: (near IR. 0.76-0.86 µm). This band operates at the same wavelength as band 3 but is aimed at 15.3 degrees forward to produce stereoscopic images.
band 5: (mid IR. 1.60-1.71 µm). This band is sensitive to plant water content.
band 6: (mid IR. 2.01-2.12 µm). This band is used to map geologic formations and is responsive to plant and soil moisture.
band 7: (mid IR. 2.13-2.25 µm). This band is used to map geologic formations and is responsive to plant and soil moisture.
band 8: (mid IR. 2.27-2.40 µm). This band is used to map geologic formations and is responsive to plant and soil moisture.

II.4.7.2 Data Format and Availability

Information regarding data availability and cost can be obtained at the Earth Observation Center of the National Space Development Agency of Japan at the following website: http://www.eoc.nasda.go.jp/homepage.html

II.4.7.3 Key References

On the web at: http://www.eoc.nasda.go.jp/guide/satellite/sat_menu_e.html

II.4.8 Terra

II.4.8.1 Historical, Orbital and Resolution Overview:

On 18 December1999 the Terra spacecraft (formerly known as EOS AM-1) was launched. Terra has an orbital height of 705km with a sun-synchronous, near-polar orbit. This spacecraft contains 5 new sensor packages to study the earth's surfaces and atmosphere. For the latest update, and a great deal of more information on the Terra mission, check out the NASA web site at: http://terra.nasa.gov/

II.4.8.1.1 ASTER - Advanced Space borne Thermal Emission and Reflection Radiometer
ASTER is designed to obtain high spatial resolution global, regional, and local images of the Earth. This sensor records 14 spectral bands of data ranging from visible, through short-wave infrared, to thermal. It has a spatial resolution of between 15 and 90 meters and is capable of 3-D stereoscopic viewing. It is anticipated that within 2 years a global 15 meter elevation model will be created using this package.

Bands 1 through 3 have a spatial resolution of 15m and cover the visible and near IR portions of the spectrum. Two receivers operate in the near IR wavelength, one pointing to nadir, one pointing backwards to produce stereoscopic images. Bands 4 through 9 operate in the short-wave IR portion of the spectrum and have a spatial resolution of 30. Bands 10 through 14 operate in the thermal IR portion of the spectrum and have a spatial resolution of 90m. For additional information about this sensor visit the ASTER web site at: http://asterweb.jpl.nasa.gov/

II.4.8.1.2 CERES - Clouds and the Earth's Radiant Energy
CERES will be used to measure solar-reflected and Earth-emitted radiation at the Earth's surface and the top of the atmosphere. This will be used to measure the earth's radiation balance daily. More information can be found at the ECERES web site at: http://asd-www.larc.nasa.gov/ceres/ASDceres.html

II.4.8.1.3 MISR - Multi-angle Imaging Spectro-Radiometer
This package features nine cameras pointing at various angles through the atmosphere. It will capture four bands of data at the red, green, blue, and near-infrared portions of the spectrum. It is designed to determine the amount, type and height of clouds and measure atmospheric aerosol particles. For specific information about MISR, visit the web site at: http://www-misr.jpl.nasa.gov/

II.4.8.1.4 MODIS - MOderate-resolution Imaging Spectro-radiometer
This package captures 36 bands of data, in the visible and IR portions of the spectrum, at a spatial resolution of between 250m and 1km. It is designed to provide coverage of the Earth's land, oceans, and atmosphere. It will provide global coverage every two days. For specific information about this sensor visit the MODIS web site at: http://ltpwww.gsfc.nasa.gov/MODIS/

II.4.8.1.5 MOPITT - Measurements Of Pollution In The Troposphere
This package is designed to observe carbon monoxide and methane in the lower atmosphere, and its interaction with the land and oceans. It has a spatial resolution of 22km and a swath width of 640km. More information can be obtained at the MOPITT web site at: http://www.science.sp-agency.ca/J1-MOPITT(Eng).htm

II.4.9 SeaWiFS

II.4.9.1 Historical, Orbital and Resolution Overview:

SeaWiFS stands for the Sea-viewing Wide Field-of-view Sensor. The objective of this project is to gather data on global ocean bio-optical properties. Various types and quantities of marine phytoplankton can be identified by observing subtle changes in the oceans color. This ocean color data contributes to the study of ocean primary production and biogeochemistry.

The SeaStar spacecraft carrying the SeaWiFS instrument was launched on 1 August 1997. It has a 705km, sun-synchronous orbit. It features a spatial resolution of 1.1km and a nominal swath width of 2,800 km providing daily coverage of the world's oceans. The SeaWiFS instrument records information in 8 bands of approximately 20nm in width ranging from 400nm to 885nm. Find out more about the SeaWiFS project at the following web site: http://seawifs.gsfc.nasa.gov/SEAWIFS.html

SeaWiFS data is continuously being evaluated, recalibrated, and refined. As of November 1999 the third phase of data reprocessing was being finalized. Details of the reprocessing can be found at http://www.yale.edu/ceo/Documentation/sea_reproc.html

III. Selected Composite Datasets

III.1 1 Km Composite AVHRR Datasets:

III.2 1 Km Land Cover Characteristics Data Bases:

III.4 Pathfinder AVHRR Land Data:

III.4 USGS 3 Arc Second Digital Elevation Model:

III.5 Digital Chart of the World DEM:

III.6 Key References:

USGS Global Land Information Center: http://edcwww.cr.usgs.gov/webglis

Mission to Planet Earth Scientific Data: http://www.hq.nasa.gov/office/mtpe/

* Additional source information can be found via the World Wide Web as well as in the "Climate Image Datasets" black binder available in the CEO lab.

IV. Tools of Image Analysis

IV.1 Geometric Corrections

The term georeferencing refers to the process of assigning map coordinates to specific pixels in a raster dataset. If the image itself is already in a known map projection, the values of individual pixels need not be altered. Instead, one simply assigns a coordinate to each pixel which retains its original data value.

In certain cases, however, the image will not already be in the desired map projection. The process of projecting raster data onto a plane and forcing it to conform to a given map projection is known as rectification. During the rectification process data values from the original raster grid must be extrapolated onto the new, rectified grid. The method used to assign these values is known as resampling or warping. Depending on the situation, one might use any of several common resampling methods to accomplish a given rectification.

For the most part rectification, by definition, involves georeferencing since all map projections are associated with a coordinate system. However, in some cases, one might only care if the image in question aligns with the grid of another raster dataset, not if the image is in any given projection! In a case such as this, rather than assigning map coordinates to image pixels, one assigns image coordinates to the pixels, then performs a warp (rectification process). This process, aligning the grids of two raster datasets, is known as registration and is necessary for combining datasets of different types, for example image and topography, as well as for change detection. Image to image registration only involves georeferencing if the reference image (not the one being warped) is already georeferenced.

IV.1.1 Rectification:

1. First, ground control points (GCP's) are assigned to certain pixels in the original image. GCP's are pixels which are identifiable as specific points with known coordinates.
2. These GCP's are located on the desired output grid (located in a given map projection).
3. The original image and the output grid are 'overlaid' so the GCP's of both line up. This is an idealized view of what happens. In practice, a computer will compute how the original image needs to be stretched/rotated to make the GCP's line up then it will compute a formula to calculate how much each part of the image needs to be stretched to conform to the given projection.
4. Finally, the value of each of the pixels on the new grid is calculated based on the new pixel's proximity to an old pixel.

Data which has been rectified to a particular projection is known as geocoded.

IV.1.2 Map Projections in a Nutshell:

IV.1.2.1 What and Why?

Unfortunately, it is impossible to create a completely distortion-free planar representation of a round object. Compromises must be made between the accuracy of area, shape, scale and direction as represented on the map. Different applications require different balances of these factors, and in some cases demand that the map satisfy some additional functional characteristic, plotting great circles as straight lines, or maintaining constant scale along a satellite ground track for example. The wide variation in uses of maps has lead to the development of many different map projections, which can be grouped based on their distortion-free characteristics as follows:

• Equal area maps distort scale, shapes and local angle, but an area on one part of the map covers the same actual area on the surface of the Earth as an equal area in another part of the map.
• Conformal maps do not conserve area, scale or direction, but do maintain correct local angular relationships. Lines of latitude and longitude, for example, intersect at right angles everywhere on a conformal map.
• Equidistant projections "show true scale between one or two points and all others on the map, or along every meridian." [4]
• Azimuthal projections show all points on the map with their correct direction from the center.

IV.1.2.2 Spheroids, Ellipsoids, Datums and Projections:

Just as one chooses a given map projection for a given application, one must choose a particular sphere or ellipse to approximate the Earth for each application. Different shaped spheres and ellipses fit the actual shape of the Earth better in some places than in others. Some are very close fits in one place, but not in another, while some are pretty good approximations everywhere, but don't really fit perfectly anywhere. When a particular sphere or ellipse is "pinned down" to a particular point on the surface of the Earth by specifying a tie point, or point of tangency, the pair are known as a datum. A specific datum and map projection pair are required to compute the transformation between latitude/longitude and map coordinates.

IV.1.2.3 Processing Considerations for Ordering Images:

1. The lowest level of processing one could obtain is the direct transmission from a given satellite. In many cases this signal is encrypted to prevent users from stealing data, but in others (GOES or AVHRR HRPT, for example) the data stream is considered public domain. Anyone with the appropriate hardware and software can legally receive the transmission. This method is only practical for users requiring large volumes of data.
2. The next level of processing that is typically available is the raw signal to which certain systematic corrections have been made - corrections for known errors induced by the sensor's optics, for example. This is what most users (those without their own receiving stations) would consider "raw".
3. The simplest geometric correction typically applied to imagery is based on knowledge of where the satellite was and where the sensor was pointing when the data were acquired. This type of correction involves warping the image (see the section IV.1.1 Rectification). Usually the data are warped to a given map projection, resulting in a geometrically correct image that has associated coordinate information that is typically accurate to within several pixels. [5]
4. The next level of processing improves the navigational accuracy of the image by using GCP's to perform the geometric corrections. This correction also requires warping the image, and produces images that are often navigated to within a pixel, but that can be in error up to a few pixels.
5. Further corrections can be made to the image by removing the effects of topography, sun angle, atmospheric effects, and other random noise. Images processed to this level may be navigated with a high degree of precision.

Most typical CEO users are likely to acquire images processed to levels 2, 3 or 4. [6] When purchasing imagery, consider the following trade off; geometric fidelity and navigational accuracy are obtained by warping the image which degrades the radiometric accuracy of the resulting image. Conventional wisdom states that applications requiring very accurate classifications or conversion of digital numbers to physical values (radiance, albedo, temperature etc..) obtain better results by performing these operations on "raw" data (level 2) and then performing geometrical corrections as opposed to deriving quantitative data from already geometrically correct images. On the other hand, accurate geometric correction is both time consuming and difficult. Those experienced with a particular type of imagery are likely to always acquire the closest thing to raw as possible, preferring to perform all the corrections based not only on the published calibration information, but also on vast stores of personal experience. After much hard work, such an approach is likely to yield better results and more accurate answers than an approach based on corrections applied by the vendor. However, much experience with a particular data type, and with satellite image processing techniques in general, is required to make this approach pay off. Applications that can afford reduced radiometric fidelity (and the higher cost of corrected imagery) and/or do not have the requisite ability/time/experience to perform all the corrections, may be better served by purchasing imagery with higher levels of pre-processed geometric correction.

IV.1.2.4 Common Satellite Mapping Projections:

IV.1.2.4.1 The Universal Transverse Mercator (UTM) Projection:

The UTM is a series of Transverse Mercator projections established by the U.S. Army in 1947 to provide a standard for world-wide mapping. Under this system, the world is divided into sixty zones, each six degrees in longitude. Distances in the x-direction (Eastings) and in the y-direction (Northings) are measured from the origin for each zone (where the zone's central meridian intersects the equator). Table 7 lists the UTM zones.

The UTM does not have a preferred datum, and is commonly used with whatever datum best approximates the region being mapped. Snyder [7] points out that the U.S.G.S. uses the Clarke 1866 ellipsoid for all land under U.S. jurisdiction except Hawaii where the International ellipsoid is used. In ERMapper, use the "NAD27" datum for the Clarke 1866 ellipsoid. Most of Connecticut is in UTM Zone 18 with a small portion of eastern Connecticut in UTM Zone 19. Connecticut data will typically use the "NAD27" datum with the Clarke 1866 ellipsoid.

Table 7: Universal Transverse Mercator zones, central meridians, and longitude ranges.
All values are listed in full degrees east (E) or west (W) from the Greenwich prime meridian (0). [From ERDAS, Inc., 1991]

Zone	Central Meridian	Longitude Range	Zone	Central Meridian	Longitude Range
1	177W	180-174W	31	3E	0-6E
2	171W	174-168W	32	9E	6-12E
3	165W	168-162W	33	15E	12-18E
4	159W	162-156W	34	21E	18-24E
5	153W	156-150W	35	27E	24-30E
6	147W	150-144W	36	33E	30-36E
7	141W	144-138W	37	39E	36-42E
8	135W	138-132W	38	45E	42-48E
9	129W	132-126W	39	51E	48-54E
10	123W	126-120W	40	57E	54-60E
11	117W	120-114W	41	63E	60-66E
12	111W	114-108W	42	69E	66-72E
13	105W	108-102W	43	75E	72-78E
14	99W	102-96W	44	81E	78-84E
15	93W	96-90W	45	87E	84-90E
16	87W	90-84W	46	93E	90-96E
17	81W	84-78W	47	99E	96-102E
18	75W	78-72W	48	105E	102-108E
19	69W	72-66W	49	111E	108-114E
20	63W	66-60W	50	117E	114-120E
21	57W	60-54W	51	123E	120-126E
22	51W	54-48W	52	129E	126-132E
23	45W	48-42W	53	135E	132-138E
24	39W	42-36W	54	141E	138-144E
25	33W	36-30W	55	147E	144-150E
26	27W	30-24W	56	153E	150-156E
27	21W	24-18W	57	159E	156-162E
28	15W	18-12W	58	165E	162-168E
29	9W	12-6W	59	171E	168-174E
30	3W	6W-0	60	177E	174-180E

IV.1.2.4.2 The Hotine Oblique Mercator (HOM) Projection:

IV.1.2.4.3 The Space Oblique Mercator (SOM) Projection:

The SOM was designed to overcome these limitations by introducing time as a projection parameter. This approach allows the spacecraft-Earth geometry to change over the course of an orbital period and permits the central line of the projection to be curved rather than straight. The projection is not perfectly conformal, but errors are extremely small within the (~185 km) swath width of the Landsat satellites. ERMapper does not support the SOM because it is so complicated. This is a problem because many of the scenes in the CEO's archive have been processed to the SOM projection and are therefore not navigable using ERMapper without rewarping these scenes to a different projection using GCPs. See Snyder's book or appendix D of the Landsat Data User's Handbook for formulae and a more complete description of this important projection. It may be possible to import these images using ERDAS Imagine, reprojecting the image to a more standard projection, then converting the image to ERMapper format for subsequent processing.

IV.1.3 Key References:

IV.2 Image Enhancement

IV.2.1 Contrast Enhancement

Contrast stretching is important because regions which are spatially contiguous often have similar spectral signatures. Take for example a desert, ice cap, or ocean. Data points across the image will all have very similar values, and will result in a bleak image unless the contrast between these values is enhanced.

Figure 14, figure 15, and figure 16 graphically illustrate a few contrast enhancement strategies. We use a plot with a double y-axis to illustrate the contrast enhancement. [9] The x-axis is simply all possible values of the original data. In these examples, the original data ranges from 0-10. One y-axis represents the frequency that each x-axis value occurs in the original dataset (the number of pixels with each x-axis value in the dataset). This generates a histogram of dataset values which is plotted as a filled in curve. The second y-axis lists the possible range of output values, in these cases 0-255. The second curve on each of these plots, which does not have the area underneath it filled in, represents the transform from input values to display values. The linear transform stretches a range of given input values equally. The histogram equalization function stretches the input values more in the ranges that have a higher concentration of pixels. The threshold function takes all input pixels below a certain value and assigns them the same output value, and all input pixels above that value to a different output value.

IV.2.2 Spatial Enhancement

To perform these enhancements, one operates on the entire dataset with a filter (kernel in ERMapper) using a process called convolution. A filter is simply a small array of numbers carefully chosen to relate a pixel with its neighbors in a certain way. Convolving the filter with the dataset simply means to multiply each value in the filter by its corresponding value in the dataset, sum up the results and divide by the sum of the values in the filter. This process of applying a filter to a dataset is often called the "sliding window" method, because you center the filter (window) around a pixel, convolve it with the dataset values falling within the window, then slide the filter to center around the next pixel. Convolution is not the same as matrix algebra, because one computes a new value for only one pixel at a time, regardless of the size of the filter.

An example helps. In this case consider a 3x3 piece of a much larger dataset which contains a locally high value surrounded by smaller values:

Original Pixel and Neighbors
10	12	9
11	20	9
9	10	10

We'll convolve this windowed out piece of the dataset with a 3x3 average filter (see Figure 17) and a 3x3 sharpen filter (see Figure 18).
The computation for the average filter is (1x10 + 1x12 + 1x9 + 1x11 + 1x20 + 1x9 + 1x9 + 1x10 + 1x10) / 9 = 11.1.
The computation for the sharpen filter is (-1x10 + -1x12 + -1x9 + -1x11 + 14x20 + -1x9 + -1x9 + -1x10 + -1x10) / 9 = 23.2. The resulting windows are:

Figure 17		Figure 18
Average Filter Formula    1 1 1 1  1  1 1 1 1		Sharpen Filter Formula   -1 -1 -1 -1 14 -1 -1 -1 -1
Pixel After Average Filter 10 12 9 11 11 9 9 10 10		Pixel After Sharpen Filter 10 12 9 11 23 9 9 10 10

Note that only the center pixel has changed. After convolving with the average filter, the pixel with the locally high value is more like its neighbors, whereas the sharpen filter enhances the difference between the center pixel and its neighbors. In a real dataset, this process would be repeated for each pixel and its neighbors that fall within the window of the filter. These filters can be of any size but must usually have an odd number of values in each direction so that the window is symmetric around the pixel in question.

IV.3 Multi-Spectral Analysis

IV.3.1 Mathematical Combinations

IV.3.2 Band Combinations

Table 9 lists some of the useful TM band combinations. The common convention used for describing the band combinations used in a given image is to list the bands used in Red, Green, and Blue order. In other words, the image created by using the red gun to display band 4, the green gun to display band 2, and the blue gun to display band 1 would be called "4,2,1 - R,G,B" for short.

IV.3.3 Classification

IV.3.3.1 Supervised Classification

For an example, consider a supervised classification performed on bands 3 and 4 of a TM dataset. First the analyst selects homogeneous groups of pixels representing the regions of interest. In our example, we'd like to find all the pixels falling into three groups, vegetation, bare soil and water. The values of each band for the pixels used for these training groups may be plotted against each other (see figure 19). In this plot each point represents a pixel, its position is determined by its value in band 3 and 4 of the dataset. Notice that the pixels in a training region do not all have identical values in these two bands, their values fall over some range in each band, forming a cloud in this spectral space.

For each pixel, the computer then computes which of these groups is most likely to contain it. Several common methods are currently used to make this decision. The most straightforward method, called the minimum distance method, simply places the pixel in question into the group with the closest training region mean. The parallelepiped method compares the value of each pixel to the maximum and minimum values of the pixels in each training region and assigns the pixel to a group only if it falls within this range. The maximum likelihood algorithm takes the distribution of pixels in a training region into account when deciding how to group a pixel. Think of it as a minimum distance method that also considers the distribution of the other pixels in the group. Suppose all the pixels in one training region have very similar spectral values, like the water region in our example. Imagine also that another training region contains pixels with a wide spread of values, like the vegetation group in our example. Let's say the pixel in question is located an equal distance from the means of each of these groups, it is more likely that the pixel belongs to the group with a larger variance in values of its training members.

IV.3.3.2 Unsupervised Classification

V. Selected Literature in Remote Sensing

For references to works on specific topics see the extensive bibliographies following each chapter in Lillesand and Kiefer. Also see the "Key References" sections earlier in this Guide. In addition to this list, we have included copies of the annual index from the journals Photogrammetric Engineering and Remote Sensing and Remote Sensing of Environment in Section VI. Abstracts from these and most of the other journals listed below are searchable on cdrom in the Geology library; ask the librarians for help.

V.1 Journals and Periodicals

1. Photogrammetric Engineering and Remote Sensing
   v47+ (1981) in Forestry: TR693 P46
   v59+ (1993) and selected 1991/1992 issues in CEO lab
   See 1998's annual index in Section VI.
2. Remote Sensing of Environment
   v.1+ (1968) in Kline: QE33.2R4 +R45
   See 1993's annual index in Section VI.
3. International Journal of Remote Sensing
   v1+ (1981) in Geology: G70.4 I56
4. I.E.E.E. Transactions on Geoscience and Remote Sensing
   v18+ (1980) in Becton: QC801 +I2(LC)
5. Earth Observation Magazine
   v1.+ (Apr 1992) in CEO lab
6. ERMapper Forum
   v1+ (1993) in CEO lab
   See ERMapper's web page: http://www.ermapper.com.

V.2 Bibliography

1. Asrar, Ghassem, ed., Theory and Applications of Optical Remote Sensing, New York: John Wiley and Sons, 1989.
   Becton: G70.4 T47 1989 (LC)
2. Colwell, Robert N., ed., Manual of Remote Sensing, Falls Church, Virginia: American Society of Photogrammetry, 1983.
   Forestry: G70.4 M36 1983 (LC)
3. ERDAS Inc., ERDAS Field Guide, Atlanta, Georgia: 1991.
   106 KGL
4. Elachi, Charles, Introduction to the Physics and Techniques of Remote Sensing, New York: John Wiley and Sons, 1987.
   106 KGL
5. Gluhosky, Paul A., An Introduction to UNIX, New Haven: 1994.
   Reproduced on the CEO WWW server: www.yale.edu/ceo/Documentation/UnixManual/unix_talk.html
6. Lillesand, Thomas M. and Ralph W. Kiefer, Remote Sensing and Image Interpretation, New York: John Wiley and Sons, 1994.
   Kline: G70.4 L54 1987
7. Rees, W. G., Physical Principals of Remote Sensing, Cambridge: Cambridge University Press, 1990.
   Geology: G70.4 R44X 1990 (LC)
8. Richards, J. A., Remote Sensing Digital Image Analysis: An Introduction, Berlin: Springer-Verlag, 1986.
   Geology: G70.4 R53 1986 (LC)  and  Becton: G70.4 R53 1986 (LC)
9. Sabins, Floyd F., Remote Sensing Principles and Interpretation, New York: WH Freemand & Company, 1996.
   Geology: G70.4 S15X 1996 (LC)
10. Short, N. M., The Landsat Tutorial Workbook, NASA Ref. Publ. 1078, U.S. Government Printing Office, Washington, DC, 1982.
    Forestry, QB637 +S56 (LC)
11. Snyder, John P., Map Projections - A Working Manual, U.S. Geological Survey Professional Paper 1395, U.S. Government Printing Office, Washington, D. C., 1987, 383 pp.
    Geology: QE75 +P7 1395 (LC)  and  SML Map collection GA 110 +S577 1987 (LC)
12. U. S. Geological Survey (USGS), Landsat Data Users Handbook Revised, USGS, Sioux Falls, SD, 1979.
    106 KGL
13. U. S. Geological Survey (USGS), Landsat 4 Data Users Handbook, USGS, Sioux Falls, SD, 1984.
    106 KGL
14. Verbyla, David L., Satellite Remote Sensing of Natural resources, Boca Raton: Lewis, 1995.
    Geology: G70.4 V47X 1995 (LC)
15. Williams, Jonathan, Geographic Information from Space, Processing and Applications of Geocoded Satellite Images, Chichester: John Wiley & Sons, 1995.
    Geology: G102.4 R44 W55X 1995 (LC)

VI. Sample of a Remote Sensing Journal Annual Index

VI.1 Photogrammetric Engineering and Remote Sensing:

PE & RS Annual Index by Author

Footnotes

1. Water depth dominates the spectral signature of water bodies only in the case of diffuse reflection. Specular reflection will dominate the spectral signature of water bodies if the sensor - water body - light source geometry is correct. In this case, the spectral signature measured will be that of the light source. Specular reflection off water bodies is known as sunglint and can be useful or annoying in satellite images depending on your application.
2. The MSS bands were renumbered between landsats 3-4, though the actual sensors remained the same. Therefore bands 4,5,6,7 for landsat 1-3 correspond exactly to bands 1,2,3,4 for landsats 4,5.
3. The thermal IR band (6) has a larger (120m²) IFOV, but it is subsampled to 30m² by the ground stations to provide consistency between bands.
4. Snyder, p4.
5. In many cases, imagery ordered with processing of only basic sensor corrections comes with the information on satellite position and pointing that is necessary for the user to make these basic navigational and geometric corrections. Note however that this sort of warp does not use the ground control point technique discussed in the section on rectification and requires additional software to perform the correction. At this time, the CEO does not have software that can make use of this information.
6. Note that the numbering scheme for labling levels of processing varies from sensor to sensor. The numbers presented here do not necessarily represent those actually used by any particular vendor. Rather, they are generic levels of processing which different vendors call different names.
7. p58.
8. Actually there are five zones for the decending portion of the orbit and five for the ascending portion, however the ascending portion of the orbit images the dark side of the Earth and relatively few images are acquired during this portion of the orbit.
9. ERMapper uses the same strategy for its contrast stretching, however the axes are not labeled, so the plot appears more confusing.