Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
Introduction: The study of green leaf phenology is useful in the tracking of biological response to
climate change. At the start of the season (SOS) in spring, greening of the vegetation begins. The
normalized difference vegetation index (NDVI) is a remote sensing method that allows one to display
greenness of vegetation (relative biomass), and can be used to determine initial green-up of the
vegetation. In this project, students will use Landsat data and NDVI to compare NDVI values in April and
May of 2009 to determine SOS in the Finger Lakes National Forest of central New York. The Landsat
data dates and location have already been acquired. One could modify this lab by requiring students to
use GLOVIS and acquire the specific Landsat data for April and May of 2009. To be consistent with other
research, a green-up threshold of 0.17 will be implemented. Using ArcGIS 10.1, students will create
composite bands, create a geodatabase, extract a mask, image analysis, and spatial analyst to compare
NDVI values in April and May of 2009.
Methodology: Research green-up phenology studies that utilize Landsat data. Acquire appropriate
Landsat images from April and May of 2009. Determine the SOS in the Finger Lakes National Forest in
the year 2009. Develop an introductory PowerPoint™ presentation for the exercise and a step-by-step
tutorial for the students that use ArcGIS 10.1.
Objective: Compare NDVI values April and May of 2009 to assess green leaf phenology in the Finger
Lakes National Forest.
Resources:
ArcGIS 10.1 (could be easily adapted for 10.2.2)
GLOVIS http://glovis.usgs.gov/
Author: Jonathon Little
Monroe Community College, Rochester, NY
Email: jlittle@monroecc.edu
Funded by National Science Foundation Advanced Technological Education program
[Grant # DUE #12005069]. Jonathon Little’s opinions are not necessarily shared by NSF.
Date completed: June 25, 2014
Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
Part 1: Acquiring and Understanding Landsat Data
1. Landsat data was downloaded from http://glovis.usgs.gov/ and unzipped for you in April and
May of year 2009. This data is from the USGS/EROS center.
To acquire other dates with the same resolution (30m), go to GLOVIS at http://glovis.usgs.gov/ (use
chrome). Try to find dates with little to no cloud cover. You’ll unzip this data. For instructions, go to:
http://ibis.colostate.edu/WebContent/WS/ColoradoView/TutorialsDownloads/CO_RS_Tutorial1.pdf
2. The data’s filename looks like this: LT50160302009107GNC01_B1. The information below
describes how to read this filename.
Landsat Scene Identifier : LXSPPPRRRYYYYDDDGSIVV
L = Landsat
X = Sensor (M = MSS, T = TM, E = ETM)
S = Satellite
PPP = WRS Path
RRR = WRS Row
YYYY = Year of Acquisition
DDD = Day of Acquisition Year
GSI = Ground Station Identifier
VV = Version
3. In this example: LT50160302009107GNC01, the LT5 stands for Landsat Thematic mapper 5. The
path of the satellite is 16 and the row is 30. The year is 2009, and the day of acquisition is 107
which is April 17, 2009.
4. Note that the other file LT50160302009139GNC01_B1.TIF. For this file, complete the
information below:
a. Landsat sensor = ____________________________
b. Row = ____________________________________
c. Path = ____________________________________
d. Year of acquisition = ________________________
e. Day of acquisition Year = _______________________
f. Using the Julian calendar at: http://landweb.nascom.nasa.gov/browse/calendar.html
we can figure out that this day in 2009 is: ____________
5. The data has already been found and unzipped for you. You can view it at:
____ Greenup_Project\DATA\2009\TAR\04_17_2009
6.
Open the April 2009 folder and view. The last 2 digits are the band for this data. For example,
B1 is band 1. The files look like:
Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
7. Open the readme.gtf file with Microsoft Word. Using the readme file, what does GeoTIFF stand
for? We will be using band 1, 2, 3, and 4.
8. Copy all of the data from Greenup Project and paste it on your flash drive, desktop, or some
other location you have access to.
Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
Part 2: Using ArcGIS Image Analyst to make Composite Bands of the April and May 2009 Data
1. Open ArcMAP and add a blank map.
2. Add the Finger Lakes National Forest Park Boundary polygon shapefile from
Greenup_Project\DATA. The file is named: Finger_Lakes.shp.
Background: The Finger Lakes National Forest encompasses 16,212 acres nestled between
Seneca and Cayuga Lakes in the Finger Lakes Region of New York State. The Forest has over 30
miles of interconnecting trails that traverse gorges, ravines, pastures and woodlands. For more
on the Finger Lakes National Forest, go to: http://www.fs.usda.gov/fingerlakes
3. Zoom in to the Finger Lakes National Forest.
4. To have a better idea as to where we are, add the NY county shapefile named cty036.shp
from: ____Greenup_Project\DATA\CEN2000nycty.
5. Double click on the rectangle circled below, and choose hollow for counties. Increase the
outline width to 2. Make Finger_Lakes.shp hollow as well.
6. Also add cities.shp from __Greenup_Project\DATA\usa_Cities. Change the symbology.
7. Save your project as a .mxd file.
8. We are going to now create a geodatabase to store some of the work we will do later. Click
on add data
.
Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
9. Add a folder where you saved the data within the greenup_project/data folder. Click on the
folder as shown to add a folder and call it geodatabase.
___Greenup_Project\DATA\Geodatabase\New File Geodatabase.gdb
10. Next add a new geodatabase by clicking on the icon.
11. Name the new geodatabase “greenup”.
12. Click add and then ok.
13. Add band 1, 2, 3, and 4 from ____ Greenup_Project\DATA\2009\TAR\04_17_2009. You’ll
do the same for May 2009 later. If you are using Landsat 8, other bands will need to be
added.
14. If asked, do not add pyramids for each raster.
15. Uncheck all layers and view each one of them. Not there are some clouds, not ideal, but
workable. In this region of the country, finding a day in the spring that is cloud free can be a
challenge! There were some fairly cloud free days in 2010, 2012, and 2013. However, this
was LANDSAT 7 which had some malfunctions. Scan Line Corrector (SLC), which
compensates for the forward motion of Landsat 7 failed. Thus, 2009 data was used in this
exercise.
16. Open the Image Analysis Window (Windows > Image Analysis)
Background: We want to analyze NDVI so we are going to make a 3-channel color image of Near
Infrared (NIR). NIR displays vegetation as bright red since healthy vegetation reflects infrared light. Ice,
snow, and clouds are white, while urban areas are cyan blue and soils can be dark to light brown.
Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
Evergreen forests will usually appear darker red than deciduous forests. Typically, darker red indicates
healthier vegetation.
17. Highlight the Single Band Images within the Image Analysis Window that you would like to
us for your composite band image. Since we are using Landsat 5 raster images, we want to
highlight and check band 1, 2, 3, and 4. The order matters!
18. Once those four bands are highlighted and checked, select the Composite Bands Button on
the Image Analysis window under processing as shown below.
19. A composite layer should have appeared in your table of contents. You can rename this
Composite_ _April17_2009, or leave as is.
What is this? We are viewing the NIR (false color) composite (4, 3, 2 in Landsat 5) which displays
vegetation in bright red because green vegetation readily reflects infrared light energy! These
bands are the same as in 2012 and Landsat 7. Be aware that if you use Landsat 8, these bands
will likely be different.
20. Add the 2009 data from Greenup_Project\DATA\2009\TAR\05_19_2009. Do not add
pyramids.
21. You will use the same steps as part 2 above to create a composite of the May 19. 2009
bands.
22. Save!
Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
Part 3: Using Image Analyst to determine NDVI
Background: You will use Image Analysis to determine NDVI. Image Analysis automatically scales the
NDVI values between 0 and 200 but this is not the typical scientific value range of -1 to 1 that is used by
researchers. Low NDVI below 0.1 and represent barren sand, rock, or snow. Moderate NDVI values
between 0.2 and 0.3 and represent shrub and grassland or start of season. Higher NDVI values (greater
than 0.6) values represent temperate or tropical forests. The equation for is NDVI = ((IR - Red)/(IR +
Red)).
23. To use image analysis and determine NDVI values between -1 and 1, change a setting in the
Image Analysis window to get the scientific values. To do this, select the Options button in
the upper left corner of the Image Analysis window.
24. Select the NDVI tab > place a check in the "Scientific Output" option and click ok.
25. Using Image Analyst, highlight the composite for April 17, 2009 and click the green maple
leaf. By clicking on the green maple leaf, we are determining the NDVI!
26. In the table of contents a new layer appears called NDVI_Composite as shown below. Note
the values are between -1 and 1. Make sure you have values between -1 and 1. If you do,
continue on. If not, ask for some assistance or try again.
Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
Background: Low NDVI below 0.1 and represent barren sand, rock, or snow. Moderate NDVI
values between 0.2 and 0.3 and represent shrub and grassland or start of season. Higher
NDVI values (greater than 0.6) values represent temperate or tropical forests.
27. Make the NDVI layer permanent by right-clicking on it in the Table of Contents> selecting
Data > selecting Export Data. Change the name so it says Export in it. Click save.
28. This may take a minute or so. WAIT! Yes, add the exported data to your map. Once you’ve
been asked to add the exported data, it has been exported, and you can move on to the
next step.
29. Repeat this process for May 2009. You’ll need to highlight the composite for May 2009 and
click the green maple leaf. Then make the NDVI layer permanent by exporting the data as in
the last step.
30. Close Image Analyst.
31. Save!
Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
Part 4: Using Image and Spatial Analyst to compare NDVI values in April and May 2009.
Background: We are trying to determine start of season (SOS) phenology of the Finger Lakes National
Forest. Most researchers use an NDVI value of 0.17 as a signal of SOS. We will determine average NDVI
values April and May of 2009 in the Finger Lakes National Forest. We will need to extract by mask to
zero in on this specific boundary. Then, we will use spatial analyst tools to determine average NDVI
within that boundary.
1. We may need to enable the Spatial Analyst extension before you can do extract the
FingerLakes.shp. Click customize, extensions.
2. Check spatial analyst and click close.
3. To limit the NDVI data to the Finger Lakes National Forest region, we will use the polygon
feature class depicting the area's boundary. In other words, we will use FingerLakes.shp to clip
the NDVI raster with the Extract By Mask tool. Use the Extract By Mask tool within ArcToobox.
Open ArcToolbox. This may take a moment.
4.
In ArcToolbox open > Spatial Analyst > Extraction > Extract By Mask.
Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
5. The extract by mask box appears.
a. For your input raster, choose NDVI_Composite_April17_2009_EXPORTED (Day 107).
Make sure it is the exported one!
b. For your feature mask data choose Finger_Lakes.
c. For your output raster, click on the folder and it should open to the geodatabase. Give
this a name like Extract_April2009. Click Save
d. It should look something like the image below. Your input raster name may be
something like NDVI_Composite_April 19. In any case, click ok. This may take a minute
or two. Wait! It may seem like nothing is happening but it is in the background. Do not
click anything until you see a new layer appear.
6. The new layer might be called Extract NDVI_DATE.
7. Repeat these same steps for May 2009. Use the extract by mask for the exported May 2009
NDVI composite.
8. You should now have two NDVI layers just within the Finger Lakes National Forest. One is for
April 2009, and the other is for May 2009.
Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
9. Change the color ramp for the April and May 2009 extracted NDVI values so the higher values
are green, and the lower values are red. You may have to find the green to red color ramp and
then choose invert.
10. The table of contents may look something like this. Note that we have a high and low value as
well as a range. Right click on of these layers, and click zoom to layer.
11. Save!
12. After extracting the area of interest, you can use a variety of tools to do further compare the
NDVI values for the two dates. We will calculate the average NDVI for each date.
13. Launch the Zonal Statistics tool (ArcToolbox > Spatial Analyst Tools > Zonal).
a. Use the Finger_Lakes polygon layer as the 'feature zone data'.
b. Select a 'zone field' that is the same value for all polygons in the Finger Lakes layer. We
can use "Name."
c. Select the Extracted_April2009 NDVI layer for one of the dates as the 'input value
raster.'
Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
d. Specify the output raster by clicking on the folder and naming it “Mean_April 2009”.
e. Select "MEAN" as the 'statistic type.'
f.
It should look something like…
8. Execute the tool by clicking ok. A new layer should appear.
The Result: The result will be a raster that contains a single value for all cells, which represents
the average NDVI value for that date. Right click on the Composite Band > Properties >
Symbology tab
9. Repeat the process for May 2009.
10. We can do further analysis with Image Analyst. Open Image Analyst, and highlight the two
extracted layers.
11. In Image Analyst, under processing, choose the difference calculator. It computes the change
pixel to pixel between the two layers and creates a temporary layer. If you want to save this
layer, you need to export it. When this layer appears, note the low and high end difference in
NDVI pixel by pixel.
Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
12. We can compare the two dates visually using the swipe tool in image analyst. Bring the two
extracted layers to the top of your table of contents.
13. In image analyst, highlight one of the extracted layers. Under display, click on the swipe tool.
14. Click on the map and you should see an arrow key. It may take a minute or so to be ready.
Wait.
15. Move the swipe tool back and forth to compare.
16. Save your work!
17. Add world imagery to your may by clicking on File  Add data  Add data from ArcGIS online.
In the search box, type imagery and click search.
18. Add world imagery.
19. You can also use the identify tool to determine an exact NDVI value for a pixel. Click on the
identify tool.
20. Using the identify tool, click on a region in the Finger Lakes National Forest that has a low NDVI
value. What is the NDVI value?
21. Unfortunately, you can’t swipe between World Imagery and the extracted NDVI layer. However,
you still can dig a little deeper. What type of land cover has a lower NDVI? To determine,
Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
uncheck world imagery and then check it. Is it forest, farmland?
22. Using the identify tool, click on a region in the Finger Lakes National Forest that has a higher
NDVI. What is the NDVI value?
23. What type of land cover has a higher NDVI? Is it forest, farmland?
Part 5: SUBMIT maps and your questions in one email!
MAPS
1.
Create one map for April 2009.
a. The map should allow one to see all of the Finger Lakes National Forest
b. It should include a:
i. Title
ii. north arrow
iii. legend
iv. cartographer’s name
v. scale bar
vi. grid lines
vii. Data source
c. Minimize white space
d. Add a photo that shows what the region looks like.
e. Add a sentence or two that explains what we are looking at.
2. Create a map for May 2009.
a. The map should allow one to see all of the Finger Lakes National Forest
b. It should include a:
i. Title
ii. north arrow
iii. legend
iv. cartographer’s name
v. scale bar
vi. grid lines
vii. Data source
c. Minimize white space
d. Add a photo that shows what the region looks like.
e. Add a sentence or two that explains what we are looking at.
3. Export these two maps as jpgs.
Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
ANALYSIS QUESTIONS
Use a word document to answer these questions.
1. What is the NDVI?
2. What is the equation for NDVI?
3. If there is more green vegetation in day X compared to day Z, which day should have a higher
NDVI? Explain in terms of the amount of NIR reflection.
4.
Using the identify tool and the May 2009 extracted layer:
a. Find a region in the Finger Lakes National Forest with a low NDVI value. What is the
NDVI value and what type of land cover is it?
b. Find a region in the Finger Lakes National Forest with a high NDVI value. What is the
NDVI value and what type of land cover is it?
5. Zoom in to a region of the Finger Lakes National Forest as was done in this lab and use snippit to
compare May 2009 NDVI values with the world imagery. Paste these into your word document
or email. For example, see below.
6. What is the high and low NDVI value, and range for April 17, 2009?
a. High =
b. Low =
c. Range =
7. What is the high and low NDVI value, and range for May 19, 2009?
a. High =
b. Low =
c. Range =
8. What is the average NDVI value for the Finger Lakes National Forest?
9. Does the NDVI value for April and May 2009, make sense? In other words, in May, would you
expect more or less NIR reflection?
10. Many research scientists use 0.17 as a NDVI threshold indicating the start of the season. Using
the average NDVI value found in the Finger Lakes National Forest, which date is closer to the
start of season in 2009?
11. Based on this information, do you think start of season occurred before or after April 19, 2009?
Remote Sensing Observations of Central New York Green-up Phenology in April and May of 2009
Additional Resources:
 iGETT Concept Module Part 1 of 2:
Image Analysis using NDVI to Assess Vegetation Greenness
http://www.youtube.com/watch?v=sPnFkp-25fM (12 minutes)

iGETT Concept Module Part 2 of 2:
Image Analysis using NDVI to Assess Vegetation Greenness

Finger Lakes National Forest http://www.fs.usda.gov/fingerlakes

USGS Landsat http://landsat.usgs.gov/band_designations_landsat_satellites.php

ArcGIS 10.1 http://www.youtube.com/watch?v=F3rsSq0-mmE (11 minutes)

Grab citizen data and Phenology data visualization tool to compare
climates http://www.youtube.com/watch?v=2BJxS1md9Xs

USGS toolkit phenology and MODIS data to get data
http://www.youtube.com/watch?v=NNjXJvTHDiQ

How does greenup phenology relate to climate change
http://www.youtube.com/watch?v=Kpp9NE1i2zM
Attribution:
USGS/EROS Center
GLOVIS