Objectives

• To understand and identify how vegetation spectral response changes over time.
• Become familiar with the Spectral Profile tool in IMAGINE

The use of remotely sensed imagery is becoming a valuable tool in many agricultural and other plant applications. For example, satellite and airborne imagery can be used as an effective tool for estimating vegetative characteristics such as plant health, stress mapping, identifying disease, and estimating yield. In this lab we will use SPOT 4 and Landsat TM data to look at crop characteristics such as change in the phenological cycle of a crop and identifying the spectral differences between major crops. The SPOT data comes from research conducted by Chris Locke and Dr. Greg Carbone in the Department of Geography (Locke, 1999).

Part I. Vegetation Spectral Change Detection

Sometimes it is necessary to determine the coordinates and brightness values of specific pixels in an image. The inquire cursor tool in IMAGINE allows you to do this. Go into the Quick View menu of the IMAGINE Viewer by clicking the right mouse button while inside the viewer and selecting Inquire Cursor. This will open a pixel information menu that allows you to move a crosshair cursor on the Viewer. You can use the black arrows to move the crosshair cursor in any pixel increment you set. For now leave the increment at 1.00 and note that the increment is variable between the file and map coordinate system. You can move the crosshair cursor using the black arrows or by pressing and holding the lmb while the mouse cursor touches the crosshair cursor. For "fine tuning" use the keyboard arrows to move the cursor. The black circle will move the crosshair cursor back to the center of the Viewer.

Coordinate values for the image can be obtained in either map, paper, file, or lat/long as long as this data exists in the image file. For example, the file spot-0804.img has map and file coordinates, either of which can be selected by clicking on the button in the top left of the Inquire Cursor box that says Map. Notice that the coordinate system is defined for you. The image projection is also shown but if you have not selected the Map option that may not necessarily be the x, y coordinate system. The table shows the R,G,B pixel brightness values for both the image file (FILE PIXEL) and the color lookup table (LUT VALUE). Move the Viewer cursor and notice how the values change. To move the crosshair cursor using the mouse you must initially place the arrow cursor at the center of the crosshairs and click on the lmb. Keep the left mouse button depressed to move the crosshair cursor.

You can also create a magnifying window by either choosing View - Create Magnifier or accessing the QuickView menu and selecting Zoom - Create Magnifier. This brings up an additional window that corresponds to your Area-of-Interest (AOI) box in your Viewer. The AOI box can be resized by dragging on the corners. To close the magnifier, place your cursor inside it and select the Close Window option in the QuickView menu.

REMOTE SENSING OF SOYBEANS

The images used in this part of the exercise are SPOT 4 scenes of a soybean field in Florence, SC maintained by the USDA Coastal Plains Soil, Water, and Plant Research Center. The field measures about 280 x 280 m. We will be looking at two pixel locations in a soybean field and investigating the change in spectral response over a period of four months (from August to October).

The six images we are using in this lab are listed in the table below. When each image is displayed in your viewer (RGB=321), click on the Start Profile Tools icon (next to the hammer icon) in the Viewer tool bar. Another way to access the Profile Tools is to go to Raster - Profile Tools in the Viewer menu bar. Select Spectral and click OK. This tool is useful for identifying changes in spectral response on a pixel-by-pixel basis. You can edit the Y-axis by clicking on Edit - Chart Options. It is also possible to GeoLink several viewers together by using the GeoLink command in the QuickView menu. This is useful when you want to simultaneously query pixels between images. In Imagine, it is not possible to link multiple viewers to one Spectral Profile tool.
 

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Location of Soybean Field A (within the blue circle)	Soybean Field A: August 14, 1998	Soybean Field A: October 31, 1998
Image: spot-0804.img Date Acquired: August 4, 1998 4:06 pm Sample LAI: 2.56	Image: spot-0819.img Date Acquired: August 19, 1998 4:17 pm Sample LAI: 6.13	Image: spot-0914.img Date Acquired: September 14, 1998 4:17 pm Sample LAI: 6.71
Image: spot-0925.img Date Acquired: September 25, 1998 4:06 pm Sample LAI: 5.96	Image: spot-1010.img Date Acquired: October 10, 1998 4:17 pm Sample LAI: 4.94	Image: spot-1026.img Date Acquired: October 26, 1998 4:10 pm Sample LAI: 2.91

ASSIGNMENT

1. After analyzing all six images with the inquire cursor and spectral profile tool, identify two pixels in Soybean Field A that represent a) high soybean yield;  and b) low soybean yield throughout the growing season (the same pixel for each of the six dates). To do this, you might want to use the Inquire Cursor or the Spectral Profile tool to randomly investigate the spectral trends in each image over time. Once you choose your pixel locations, use the same coordinates for each of the six images. You may want to zoom in on the image for a more detailed analysis. After choosing two pixels that represent high and low soybean yield, create two graphs (high and low yield) showing reflectance curves for the six dates that represent change in spectral response over the growing season.

2. Create a simple phenological cycle for soybeans using the dates of these images and the graphs you have created. You may want to compute the NDVI for each image and use the resultant values to show the 'greening up' and the 'senescing down' of the plant over time. This function is found under Interpreter (on the Icon Panel) > Spectral Enhancement > Indices. Define your Input and Output files, select SPOT XS as your sensor, and then specify the NDVI function. The Output image Data Type should be Float Single which yields (no pun intended) a value between 0 and 1.

Part II. Analysis of Landsat TM Imagery

We will now use the Landsat TM image imperialvalleyTM.img acquired on December 10, 1982 of a portion of the Imperial Valley in California. The imagery represents four land cover types: sugarbeets, alfalfa, cotton, and fallow.

 

RGB = 321	RGB = 432
RGB = 532	RGB= 732


ASSIGNMENT

3. Create a graph using Landsat TM's seven bands depicting the spectral profile for a) sugarbeets, b) alfalfa, c) cotton, and d) fallow fields. After careful analysis of each of the spectral curves, choose the band(s) that provide the best discrimination between these four land cover types.

Locke, C., 1999, Estimating Biophysical Properties of Soybeans Using Field Data, Crop Modeling, and Remote Sensing. Columbia: Univseristy of South Carolina, Unpublished Masters Thesis, 74 p.

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