Module 2.2 Monitoring activity data for forests
remaining forests (incl. forest degradation)
Module developers:
Carlos Souza, Imazon
Sandra Brown, Winrock International
Frédéric Achard, EC Joint Research Centre
Exercises:
Monitoring degradation processes: logging


Part 1: Introducing ImgTools

Part 3: Decision Tree Classification using a
time-series of SMA and NFDI images
Part 2: Forest Change Detection using
SMA fractions and NDFI
V1, March 2015
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
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REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
Creative Commons License
Monitoring Degradation Processes: Logging
Part 2: Forest Change Detection
● In this exercise you will learn:
● NDFI slicer to generate a forest mask for the
baseline year
● Use Change Detection module forest change
detection (i.e., deforestation and forest
degradation)
● Interpret the results of the forest change
detection
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
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Part 2: Forest Change Detection
 Data set
● For this exercise, we will use a time-series of NFDI
images from Sinop region, Mato Grosso state, Brazil
● The images were acquired every year from 2009 to
2011, available from the folder:
\ImgData\Exercise 2
• 226_68_090822w_ndfi
• 226_68_100724w_ndfi
• 226_68_110812w_ndfi
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
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Part 2: Forest Change Detection
1. Open ImgTools
● Double clik ImgTools icon to launch it
2. Select NDFI slicer in the Classification deck
This will launch xNDFISlicer program interface
NDFI Slicer allows to segment
the NDFI image into Non-Forest,
Degradation, Regeneration and
Forest classes. Cloud and Water
masks are applied previously to
NDFI image. The images used in
this example are cloud free –
only water masks were applied
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
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Part 2: Forest Change Detection
2. Load NDFI baseline image
•
Go to the folder \ImgData\Exercise 2\
•
Select the Add button at the
xNDFISlicer interface. This will
pop up the Choose NDF Image
window
•
Select the 226_68_090822w_ndfi
image (baseline year 2009)
•
The NDFI images shows up in the
NDFI images list. Double click or
right-click the mouse and select
Open to visualize it
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
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Part 2: Forest Change Detection
3. Explore the xNDFISlicer
•
NDFI image can be
visualized using the
scroll, image and zoom
windows as showed on
the left
•
Right-click the mouse
image window and select
the Preview option. This
will load the NDFI slicer
classification using the
default threashold values
that appear on the NDFI
histogram
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
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Part 2: Forest Change Detection
3. Adjust the NDFI threshold slices
•
Click on the histogram bars
(right on the color edges) to
change the threshold values.
Selcect, for example, the
threshold value for
degradation 174 and move it
to 180. This will change the
areas classified as forest
degradation (orange color
class)
• Right-click the mouse botton and select the Link option. This
will allow to togle on and off the NDFI and classification images
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
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Part 2: Forest Change Detection
3. Define the final threshold to create a forest
baseline
• We will exclude the regeneration class
by setting up the degrataion class
ranging from 155 to 189. Intact Forest
will have NDFI from 190 to 200 and
Non-Forest below 154. The water and
cloud classes are classified using the
masks from NDFI image
• Apply the spatial filter: Right-click the mouse button and set 1 and 2
as minimum and maximum area values. The spatial filter will
reclassify groups of pixels with size between 1 (min. value) and 2
(max. value) pixels together
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
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Part 2: Forest Change Detection
4. Generate the final forest baseline
classification to be used in the Change
Detection Module
•
•
Right-click the NDFI
image
(226_68_090822w_ndfi)
and select the Add to List
option
•
Click the Execute botton,
and select the folder to
save the results
When done, the classified NDFI slice image will
show in the Results file list
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
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Part 2: Forest Change Detection
5. Generate the change detection classification
in the Change Detection Module
•
Select Change Detection in the Classification deck
This will launch xChangeDetection program interface
Change Detection allows to
detect the changes in the
previous
forest
pixels.
The
program detects changes from
forest
to
degradation
and
to/deforestation;
and forest
degradation to deforestation.
The xChangeDetection program
needs at least two NDFI images
of different dates and the forest
baseline image for the first one
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
10
Part 2: Forest Change Detection
6. Load NDFI images and baseline classification
•
Go to the folder \ImgData\Exercise 2\
•
Select the Input image path button at
the xchangeDetection interface. This
will pop up a dialog window to select
the folder where NDFI images are
located
•
Navigate to where you saved your
image data and find it in the folder
\ImgData\Exercise 2\
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
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Part 2: Forest Change Detection
6. Load the baseline image

Select the Baseline image button
at the xchangeDetection interface
This will pop up the dialog window
to choose the baseline
classification file

Select the 226_68_090822w_Cls
classification (2009 baseline year)
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
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Part 2: Forest Change Detection
6. Load NDFI images
• A ROI mask is optional. This exercise will not apply ROI mask
• Fill the fields Path=226 and Row=68
• Fill the field NDFI images
(suffix)=_ndfi
•
•
Fill the field Result (suffix) = _chg
Click the Add button, then the NDFI
images shows up in the NDFI images
list. Double click or right-click the
mouse from the second image and
select Open to visualize it
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
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Part 2: Forest Change Detection
7. Explore the xChangeDetection
•
NDFI image can be visualized
using the scroll, image and
zoom windows as showed on
the left
•
Right-click the mouse in image
window and select the Preview
option. This will load the
Change Detection classification
results using the default
threshold values that appear on
the histogram obtained with
the NDFI differencing results
from the two dates
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
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Part 2: Forest Change Detection
8. Adjust the NDFI change threshold
•
•
Click on the histogram bars
(right on the color edges) to
change the threshold values
•
Select the threshold value for
degradation -20 and move it
to -10. This will change the
areas classified as forest
degradation (cyan color
class)
Right-click the mouse button and select the Link option. This
will allow to toggle on and off the NDFI (t0, t1, and
RGB[t0t1t1]) and classification images. Use this technique to
inspect your results
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
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Part 2: Forest Change Detection
9. Use spatial filter to get the final forest
change detection classification
• To apply the spatial filter, right-click the mouse button and set 1
and 2 as minimum and maximum area values
•
•
Right click on NDFI [t1] image
and update baseline. This process
will save the threshold values and
update the baseline with new
deforestation areas, then select
the next NDFI image to be
classified
Click the Execute button, and select the folder to save the results.
When done, the classified NDFI images will appear in the Results file
list. This is a simple and fast way to generate forest change maps
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
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Recommended modules as follow up
 Module 2.2: Continue with Exercise ImgTools Part 3 to apply
the image classification algorithms in ImgTools to map forest
degradation
Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation)
REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF
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