# ![](media/image1.jpeg){width="6.0in" height="2.4166666666666665in"}LAND COVER CHANGE DETECTION: 

# STUDYING VEGETATION FROM SPACE

## **Purpose**

To create the Normalized Difference Vegetation Index (NDVI) images
necessary to complete the vegetation change detection lesson.

## **Overview** 

![](media/image2.png){width="2.061023622047244in"
height="1.3728346456692913in"}![](media/image3.png){width="2.0188976377952756in"
height="1.344488188976378in"}![](media/image4.png){width="4.375196850393701in"
height="2.9161417322834646in"}In this project, students will work with
Landsat data and MultiSpec image processing software as they determine
the extent and types of land cover change that have occurred in the
community over a seventeen year period.

SVS figure 1. In the detailed images urban sprawl in San Antonio, Texas
is visible between 1974 and 1988. Pronounced urban/suburban expansion is
visible on the west edge of the city. In this set of color composite
images vegetation is red and urban areas are gray and blue. [USGS Earth
shots](http://edcwww.cr.usgs.gov/earthshots/slow/SanAntonio/SanAntonio),

The growth of cities and suburbs changes the vegetative land cover in an
area. Schools, housing developments, asphalt, and athletic fields
replace meadows, forests and farmlands. In arid areas, deserts become
green with irrigation-supported lawns and golf courses. Changes in the
pattern (distribution) and type of vegetation can be characterized
through the use of Landsat data. Figure 1 illustrates some of the
vegetative land cover changes that take place in an expanding urban
area. (see SVS figure 1)

Not all land cover change is a result of urban expansion. Many natural
processes can significantly alter vegetation patterns in an area. The
[Knife River
Delta](http://edcwww.cr.usgs.gov/earthshots/slow/Knife/Knife) in Canada
(SVS Figure 2) illustrates an example of one of these natural processes.
These images show vegetation loss along the shoreline of Hudson Bay in
central Canada. The ground cover was eaten bare by growing numbers of
snow geese. In these Landsat images, red signifies vegetation and water
appears green to dark green to black. In the first image, taken August
14^th^ 1973, the highlighted patch of shoreline is densely vegetated. In
the second image, taken July 18^th^ 1996, a large portion of the same
area is no longer covered with vegetation. The bright green patch of
land in the second image was eaten bare by snow geese.

![](media/image5.png){width="3.3in"
height="2.3755905511811024in"}![](media/image6.png){width="3.3in"
height="2.3755905511811024in"}

SVS Figure 2. Bookmark
[www.usgs.gov/Earthshots](http://www.usgs.gov/Earthshots) for
*Earthshots,*5th ed., 14 August 1999, from the [EROS Data
Center](http://edcwww.cr.usgs.gov/) of the [U.S. Geological
Survey](http://www.usgs.gov/), a bureau of the [U.S. Department of the
Interior.](http://www.doi.gov/)

Knife River Delta, Canada 1973, 1996, visited 4-25-00

**BACKGROUND INFORMATION**

**SATELLITE IMAGE VEGETATION INDICES**

[![](media/image7.png){width="4.8728346456692915in"
height="3.230708661417323in"}](http://www.globe.gov/sda-bin/wt/ghp/tg+L(en)+P(implementation/remotesensing))Landsat
images can be manipulated to better distinguish plants from other types
of land cover through the use of imaging processing software. This
manipulation is possible due to the way plants reflect light back into
space. Plants have a unique spectral signature that offers a reflected
light "fingerprint." The most unique portion of the spectral signature
or "fingerprint" of vegetation or a typical plant is seen in graph 1.
Note that the largest contrast in reflectance is between the red (in the
visible wavelengths) and the near infrared. The red band or channel
records a low percentage of reflectance (10%). Most red light (90%) is
absorbed by the plant's chlorophyll. The lower the red values
reflectance, the higher the chlorophyll content. (see graph 1) The near
infrared (NIR) band or channel records the reflectance of NIR by the
leaves' cell structure. If the reflectance of a plant's NIR is high it
shows more intense growth (Sabins, 1997). This contrast makes it
possible to create an image that separates vegetative land cover from
nonvegetative land cover.

In graph 2, the reflectance difference is apparent if you compare the
spectral differences of dry bare soil and green vegetation between the
0.6μm and 0.8 μm wavelengths. The difference in reflection (%) is much
greater for green vegetation than dry bare soil. A quick way to identify
vegetation in a spectral signature graph is to look at the slope of the
line between red (0.6μm) and NIR (0.8μm). A steep positive slope between
these points will often indicate the presence of vegetation.

Vegetation indices are used to isolate vegetation's presence and
photosynthetic activity from other types of land cover. The vegetation
index that is used in this project is the Normalized Difference
Vegetation Index (NDVI), which is defined by an algebraic formula:

near IR band - red band

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ = NDVI

> near IR band + red band

![](media/image9.png)

![](media/image11.png)

![](media/image12.png)

The NDVI index is "preferred to the simple index \[or band ratio e.g.
IR/R\] for global vegetation monitoring because the NDVI helps
compensate for changing illuminations conditions, surface slope, aspect,
and other extraneous factors" (Lillessand and Kiefer, 1994). This is an
important aspect to consider. When the Landsat satellite acquires images
of one season in different years some of the "illuminations conditions"
may be different. The NDVI index improves the comparison between the two
or more images.

NDVI pixel values created using the above algebraic formula range from
-1 to 1. The values for vegetation range from a low of 0.05 to a high of
0.66 (Lillessand and Kiefer, 1994). Clouds, snow water and bright
nonvegetated surfaces have a NDVI value of less than zero. The highest
value in the vegetation range represents the maximum
"greenness."(Lillessand and Kiefer, 1994) Greenness is a measure of
plant biomass that is actively producing chlorophyll
([GreenReport](http://www.kars.ukans.edu/products/greenreport.htm),
1998). The vegetation value range used in the NDVI indices illustrates
the state of health and or development of vegetation. High values
usually shown as dark green areas (in MultiSpec they are white)
represent areas of healthy, abundant vegetation in a mature state of
development. Lower values, usually depicted with lighter green (in
MultiSpec they are gray and black), show areas with emerging or lesser
amounts of vegetation (GreenReport, 1998).

## **The Mathematics of NDVI**

In order to understand the math formula used to create an NDVI image,
you must know the reflectance values for each channel of a given pixel.
You can use MultiSpec to find and compare the reflectance values of two
pixels. Once an image is opened, click on a chosen pixel and pull down
the "processor" menu. Select the "list data" function and a window
titled "SET LIST DATA SPECIFICATIONS" will appear. The coordinates of
the selected area (pixel) will be automatically entered. Check the
"include line and column" box in the options section of the window and
click on OK.

![](media/image14.png){width="4.927083333333333in"
height="4.354166666666667in"}

Next, pull down the "window" menu and select "text output". A list of
pixel image data values will appear at the end of the text output
window. Each channel's grayscale value will be displayed separately.
MultiSpec uses a 0-255 grayscale, a value of 0 is displayed as black and
a value of 255 is displayed as white, with varying shades of gray in
between. In the example below two different pixels have been selected
for comparison, one from a forested area and the other from an
urban/residential area.

## **Forest land cover pixel**

QASJUL97 Multispectral Image Data Values

Using the data below, specifically the red and the near infrared
wavelength channel data, it is possible to see how an NDVI image is
created.

9

  ------ ----- -------- ------- -------- --------- ------- ---------- -------
  Line   Col   Blue     Green   Red      NIR (Ch4) MIR     Thermal    MIR
               (Ch1)    (Ch2)   (Ch3)              (Ch5)   (Ch6)      (Ch7)

  252    204   121      43      **42**   **120**   64      126        19
  ------ ----- -------- ------- -------- --------- ------- ---------- -------

![](media/image15.png){width="1.2625in"
height="1.2625in"}![](media/image16.png){width="1.30625in"
height="1.30625in"}

![](media/image15.png){width="1.2625in" height="1.2625in"}

near IR band - red band channel 4 -- channel 3

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ = NDVI or
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ = NDVI

near IR band + red band channel 4 + channel 3

[4 -- 3]{.underline} ~=\ NDVI\ →~ [120 -- 42]{.underline} ~=\ NDVI\ →~
[78]{.underline} ~=\ 0.48~ (which is the NDVI value)

4 +3 120 + 42 162

######### 97 NDVI Multispectral Image Data Values

  -------------- ---------------- ---------------------------------------
  Line           Col              NDVI value (Ch 1)

  252            204              0.48 \*255=122.4⇒123
  -------------- ---------------- ---------------------------------------

The image data value for this calculation is 123 since all calculations
are multiplied by 255 for display purposes. The reason is that the
software uses a 0-255 grayscale. In order to display the NDVI image the
NDVI value for each pixel needs to be multiplied by 255 to spread the
NDVI pixel values over the entire grayscale range. If the MultiSpec NDVI
pixel has a value of 123 the true NDVI value would be 0.48 because 123 /
255 = 0.48.

## Urban/Residential land cover pixel

QASJUL97 Multispectral Image Data Values

  ------- ------- ------- ------- -------- -------- ------- --------- -------
  Line    Col     Blue    Green   Red      NIR      MIR     Thermal   MIR
                  (Ch1)   (Ch2)   (Ch3)    (Ch4)    (Ch5)   (Ch6)     (Ch7)

  226     128     147     59      **72**   **87**   132     143       68
  ------- ------- ------- ------- -------- -------- ------- --------- -------

######### 97 NDVI Multispectral Image Data Values

+--------------+------------+-----------------------------------------+
| #  Line      | #  Col     | NDVI value (Ch 1)                       |
+--------------+------------+-----------------------------------------+
| 226          | 128        | 0.09 \*255=22.95⇒23                     |
+--------------+------------+-----------------------------------------+

![](media/image17.png){width="1.2645833333333334in"
height="1.2645833333333334in"}![](media/image18.png){width="1.2645833333333334in"
height="1.2645833333333334in"}

If the same process is applied using the above urban/residential data,
it is possible to see how to mathematically separate vegetative land
cover from nonvegetative land cover. If the above pixels' data values
are entered into the NDVI equation the resulting math occurs:

near IR band - red band channel 4 -- channel 3

\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ = NDVI or
\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_ = NDVI

near IR band + red band channel 4 + channel 3

[4 -- 3]{.underline} ~=\ NDVI\ →~ [87 -- 72]{.underline} ~=\ NDVI\ →~
[15]{.underline} ~=\ 0.09~ (which is the NDVI value)

4 +3 87 + 72 159

By comparing these two pixel NDVI values it is possible to see
mathematically why the NDVI equation is able to separate or "classify"
land cover by how much photosynthesis is occurring. If a pixel has a
great contrast between the near infrared (NIR) and the red channels and
the NIR is the higher of the two, then the NDVI value will be high.
Conversely, if the contrast is low between the NIR and the red channels
then the NDVI value will be low.

## **Time**

-   1-2 class periods to create the needed images.

-   Time out of class can be used for the data analysis.

-   Additional time will be necessary for the advanced level to
    understand how light reflectance works and possibly conduct a
    radiometer lab using plant reflectance

## **Level**

### Intermediate and Advanced 

## **Key Concepts**

> Communication skills
>
> Read and understand images produced by others
>
> Produce images for other to interpret
>
> Find and describe locations on maps and computer-generated images
>
> Technology/Software Mastery
>
> Use computer software to generate images
>
> Use computer software to scale images
>
> Use computer software to interpret images
>
> Computation and Estimation
>
> Use simple algebraic formulas
>
> Use, interpret, and compare numbers
>
> Estimate distances from maps and actual sizes of objects from scale
> drawings
>
> Decide what degree of precision is adequate
>
> Critical-Response
>
> Be aware that there may be more than one good way to interpret a given
> set of findings

## **Skills** 

*Identifying* images

*Managing* time

*Collecting* data

*Integrating* information

*Analyzing* data

*Interpreting* data

*Drawing* conclusions

*Using* computers

## **Materials and Tools**

[MultiSpec
software](http://ltpwww.gsfc.nasa.gov/growsmart/software.html) (latest
version)

Georeferenced [Landsat
images](http://ltpwww.gsfc.nasa.gov/growsmart/data.html), a current and
past image

Color printer

Notepaper

## **Teacher Preparation**

-   Download the appropriate version of
    [MultiSpec](http://dynamo.ecn.purdue.edu/~biehl/MultiSpec/) from
    their web site if you do not already have a version.

-   **To conduct this activity with the provided data sets:**

-   Download
    [1984.lan](http://ltpwww.gsfc.nasa.gov/growsmart/1984_datadownload.html)

-   Download
    [1997.lan](http://ltpwww.gsfc.nasa.gov/growsmart/1997_datadownload.html)

-   **To conduct this activity with your school's** data obtain an older
    Landsat image that is georeferenced to the current image. The older
    the image the greater the difference will be. If at all possible a
    10-year difference will give some significant results. ( Note: the
    GLOBE program will be providing georeferenced Landsat image sets to
    teachers who have been trained by the GLOBE program. Follow the link
    to become a [GLOBE](http://www.globe.gov/) teacher.)

-   Complete the NDVI calculations on your version of MultiSpec prior
    to the lesson to make sure any slight difference in the software
    doesn't interfere with the lessons

-   If time constraints are an issue you can download the created NDVI
    images already created.

-   Download [84
    NDVI](http://ltpwww.gsfc.nasa.gov/growsmart/1984_datadownload.html)

-   Download [97
    NDVI](http://ltpwww.gsfc.nasa.gov/growsmart/1997_datadownload.html)

####### Normalized Difference Vegetation Index (NDVI) calculation

######### Step by step process to calculate NDVI 

-   Open MultiSpec

```{=html}
<!-- -->
```
-   Open the image to be converted into a NDVI image. **QASAUG84.img**
    or

########  **QASJUL97.img**

-   Select the processor function from the top title bar

-   Select the reformat function from the pull down menu

-   The Reformatting Utilities window will appear; the default option
    should be "Change Image File Format." Select the circle if it is not
    selected and click OK

-   The "Set Image File Format Change Specifications" window will
    appear. Select the "Transform Data..." box. Automatically the "Set
    Reformat Transform Parameters" window will appear.

    ![](media/image19.png){width="4.944444444444445in"
    height="4.039583333333334in"}

-   Select the "New Channel from General Algebraic Transformation"
    button.

-   Below the "New Channel from General Algebraic Transformation" button
    a workspace will appear.

> ![](media/image20.png){width="4.802083333333333in"
> height="3.8020833333333335in"}

-   Change the default formula shown above to the following formula:
    type in C4 -- C3

> 0 + ^\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_^ X 255
>
> C4 + C3
>
> ![](media/image21.png)

-   Click OK when it is complete

```{=html}
<!-- -->
```
-   Click OK on the "Set Image File Format Change Specifications" window

-   Save the image.

-   MultiSpec will now create the image; the percent of completed job
    should be shown in a Status window.

-   Once created, NDVI image needs to be opened. The "Set Display
    Specifications for:" should be set to 3-channel color from the pull
    down menu. Then click OK.
    ![](media/image22.png){width="4.864583333333333in"
    height="4.354166666666667in"}

-   The "Set Histogram Specifications" window will appear to compute a
    new histogram or the statistics used to display the image. Make sure
    the interval is set to 1 and not 15 for both the lines and columns.
    Click OK.

-   Once the histogram is created, it will not need to be recreated. The
    histogram description is recorded in the text window; it can be
    saved along with the image for future information. The algebraic
    formula and the highest and lowest calculated values will be
    displayed.

-   Under the file function of the top title bar the function "Save Text
    Output As..." will allow you to save the information for future
    review.