Exercises in Advanced Research EG2 (2007 Spring Term) Keio University, Graduate School of Media and Governance

Tomoko Doko (TA, PhD course in Media and Governance), Hiromichi Fukui

Thursday 3 (13:00～16:30) @ Z3F

4th Exercise Introduction to Erdas Imagine

Topics:

To provide practice in displaying and inspecting an image

Learning objectives:

In this exercise you will explore spatial data in Erdas Imagine; you will learn how to view

satellite imageries in Erdas Imagine, to make color composites, and to calculate NDVI.

Self-study exercise:

1. Make a true-color composite of a satellite imagery (RGB=321).

2. Make NDVI imagery.

You don’t need to submit this exercise.

Datasets:

Content Name of file File type Extent Original data

Landsat

ETM+ Landsat1234.img Imagine file Tokyo Bay GLCF

Acquire data from:

http://www.sfc.keio.ac.jp/~dokochan/landsat1234.zip

and unzip file.

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1. Open Erdas Imagine

Select ERDAS IMAGINE from the Start menu. Your screen will look like this. You see

the main menu in icons on top and the viewer.

Main bar

Icon Panel

Viewer

In Viewer, select Open/Raster Layer.

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Navigate folder to go to landsat234.img.

Click OK

Right-click and select Fit Image To Window. Then you will see an imagery fully.

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2. Make color composites

In one single band from the Landsat 7 ETM sensor, the difference in energy levels between

various land cover classifications may not be discernible. Since comparing the spectral

characteristics of land features in multiple bands provides a better separation, or contrast,

between different land surfaces, Landsat data from multiple bands can be combined to create a

data product known as a composite image. Landsat composite images are often called

three-band composite images since they are created using the measured energy level in each of

three ETM+ spectral bands to control the amount of red, blue, and green in a color output image.

RGB composite

2.1. False color composite/Near Infrared Composite (432)

A Near Infrared composite eliminates the visible blue band and uses a Near Infrared (NIR) band

to produce the image. The resulting composite does not resemble what the human eye will see

(for example, vegetation is red instead of green); however it is very useful to researchers. The

mapping of color to band is:

Band 4 (NIR) = red

Band 3 (Visible red) = green

Band 2 (Visible green) = blue

Click Raster/Band Combinations

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Choose [:band4] for Red, [:band3] for Green, [:band2] for Blue.

Click OK

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2.2. True-color composite (321)

True color composite images are created by combining the ETM spectral bands that most closely

resemble the range of vision of the human eye. A true-color composite uses the visible red (band

3), visible green (band 2), and visible blue (band 1) channels to create an image that is very

close to what a person would expect to see in a photograph of the same scene. The band to

color mapping for a 321 Composite are:

• Band 3 (Visible red) = red

• Band 2 (Visible green) = green

• Band 1 (Visible blue-green) = blue

Let’s make a true-color composite.

Reference: http://chesapeake.towson.edu/data/all_composite.asp

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3. Normalized Difference Vegetative Index (NDVI)

3.1. Introduction to NDVI

（Refer l.htm，

Reference (right)：http://web.sfc.keio.ac.jp/~kipinga/RSI/rs6/）

ily

to

net and will perhaps help us understand the impact of mankind

on our natural biological cycles.

a

DVI).

o, in red and

near-infrared bands, the contrast between vegetation and soil is at a maximum.

ence (left)：http://www.crisp.nus.edu.sg/~research/tutorial/optica

Assessing the type, extent, and condition of vegetation over a region is a primary goal of land

use investigations. Researchers use data from Landsat and other environmental satellites to

determine the number of acres of certain crop types in a region, locate vegetation that is heav

impacted by natural or man-made stresses such as pests, fire, disease, and pollution, and to

delimit boundaries between such areas as wetlands or old growth forest. Such sets of data,

taken over time intervals and compared, can also help us understand how vegetation changes

over time. Satellite data can be used to detect vegetative change from one growing season

the next, from year to year, or from decade to decade. These types of data help us better

understand the ecology of our pla

A vegetative index is a value that is calculated (or derived) from sets of remotely-sensed dat

that is used to quantify the vegetative cover on the Earth's surface. Though many vegetative

indices exist, the most widely used index is the Normalized Difference Vegetative Index (N

The NDVI, like most other vegetative indices, is calculated as a ratio between measured

reflectivity in the red and near infrared portions of the electromagnetic spectrum. These two

spectral bands are chosen because they are most affected by the absorption of chlorophyll in

leafy green vegetation and by the density of green vegetation on the surface. Als

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3.2. Produce NDVI

The NDVI transformation is computed as the ratio of the measured intensities in the red (R) and

near infrared (NIR) spectral bands using the following formula:

NDVI = (NIR - red) / (NIR + red)

The resulting index value is sensitive to the presence of vegetation on the Earth's land surface

and can be used to address issues of vegetation type, amount, and condition. Many satellites

have sensors that measure the red and near-infrared spectral bands, and many variations on the

NDVI exist. The sensor that supplies one of the most widely used NDVI products is on board the

National Oceanic and Atmospheric Administration (NOAA) meteorological satellites. This sensor,

known as the Advanced Very High Resolution Radiometer (AVHRR), is a 5 channel radiometer

with channels in the red (channel 1) and near infrared (channel 2) potion of the spectrum.

From Icon Panel, select Interpreter / Spectral Enhancement / Indices

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Select landsat1234.img as Input File

Type landsat_ndvi.img as Output File

Select Landsat ETM in Sensor

Remain NDVI in Select Function

Click OK

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Optional: How to Acquire dataset

Go to GLCF download website:

http://glcfapp.umiacs.umd.edu:8080/esdi/index.jsp

Click Map Search.

Check ETM+ and TM for Landsat Imagery (left corner)

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Click around Japan to zoom in Tokyo Bay. (Repeat clicking until it becomes large enough

to see Tokyo Bay.)

You can zoom in by clicking the left button on this icon

as well.

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Click Select Window icon . You will see the changes from “No Images in

selection” to “11 image(s) in selection”.

Click Preview & Download.

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You will see a following window.

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Click 040-641 for ID column, which covers Tokyo Bay.

Click Download.

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Download all files.

Unzip files in one folder.

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In Erdas Imagine, with Layer Stack under Interpreter | Utilities you can combine the

different bands in one image.

Under Input File: select ~nn10.tiff (=band1)

Click Add

Continue above procedure for ~nn20.tiff ~ ~nn80.tiff (=band2~band8)

Specify Output File: landsat_multi.img

Click OK

4. landsat_multi.img

1. ~nn10.tiff

2. Click Add

3. Continue till ~nn80.tiff

5. Click OK

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