Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module 2.2 Monitoring activity data for forests remaining forests (incl. forest degradation) V1, March 2015 Creative Commons License 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 2: Forest Change Detection using SMA fractions and NDFI  Part 3: Decision Tree Classification using a time-series of SMA and NFDI 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 2 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 3 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 4 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 5 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 6 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 7 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 8 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 9 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 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 Part 2: 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 11 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 12 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 13 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 14 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 15 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 16 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 17 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