Web viewThe Landsat data dates and ... Funded by National Science Foundation Advanced Technological Education program ... Open the readme.gtf file with Microsoft Word

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 LittleMonroe Community College, Rochester, NYEmail: [email protected]

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

mailto:[email protected]

http://glovis.usgs.gov/


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 : LXSPPPRRRYYYYDDDGSIVVL = LandsatX = Sensor (M = MSS, T = TM, E = ETM)S = SatellitePPP = WRS PathRRR = WRS RowYYYY = Year of AcquisitionDDD = Day of Acquisition YearGSI = Ground Station IdentifierVV = 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:

http://landweb.nascom.nasa.gov/browse/calendar.html

http://ibis.colostate.edu/WebContent/WS/ColoradoView/TutorialsDownloads/CO_RS_Tutorial1.pdf




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.


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 .

http://www.fs.usda.gov/fingerlakes


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.


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!


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.


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!


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.


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.


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.'


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.


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,


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!

MAPS1. Create one map for April 2009.

a. The map should allow one to see all of the Finger Lakes National Forestb. It should include a:

i. Titleii. north arrow

iii. legendiv. cartographer’s namev. scale bar

vi. grid linesvii. Data source

c. Minimize white spaced. 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 Forestb. It should include a:

i. Titleii. north arrow

iii. legendiv. cartographer’s namev. scale bar

vi. grid linesvii. Data source

c. Minimize white spaced. 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.


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?


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 CenterGLOVIS

http://landsat.usgs.gov/band_designations_landsat_satellites.php

http://www.fs.usda.gov/fingerlakes

http://www.youtube.com/watch?v=sPnFkp-25fM

http://www.youtube.com/watch?v=sPnFkp-25fM

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Web viewThe Landsat data dates and ... Funded by National Science Foundation Advanced Technological Education program ... Open the readme.gtf file with Microsoft Word