1. Forest / Non-forest (FNF) mapping for Viet Nam using PALSAR-2 time series images
2019/01/22
Truong Van Thinh – Master’s Program in Environmental Sciences Supervisor: Prof. Kenlo Nishida Nasahara

2. The importances of forest
Climate change (GHG’s emission)
Biodiversity
Forest
Ecosystem
Livelihood

3. Forest mapping and its issues
Hydrological study
Natural disasters study
Climate modeling
Input
Forest map
Satellite images
Survey mapping
Aircraft photos
Low cost,
Large area coverage,
Less human labor intervention.
Hight cost,
Limited area,
Laborious work 3

4. Satellite sensors and their advantage
Optical satellite
Landsat 8 (NASA)
Synthetic Aperture Radar (SAR)
ALOS-2 (JAXA)
cloud cover
Optical image
No cloud
SAR image 4

5. Forest / Non-Forest products JAXA (Japan Aerospace Exploration Agency)
JAXA Forest / Non-forest maps 5
Global Forest cover (Hansen et al. 2013)

6. The differences in JAXA FNF6
2015_JAXA
2016_JAXA
2017_JAXA

7. Objectives 1. To produce forest / non -forest maps for Viet Nam:
with high accuracy
consecutive maps from 2014 to 2018
Forest monitoring and management in Viet Nam
2. To analyze forest cover change for Viet Nam during

8. Study area Located in Southeast Asia Total area: 332,698 km2
~ 42 % of land area is covered by forest
(GSO, 2017) ● ● ●
Google terrain map of Vietnam

9. Method
Satellite images
Output map
Input data
Algorithm
Training data

10. Backscatter value (HH, HV, HH/HV, HH-HV)
Method (cont.)
SRTM
MODIS
ScanSAR time series
Google Earth
Field GPS photos
DATA
Convert DN to Backscatter
Slope
NDVI
Backscatter value (HH, HV, HH/HV, HH-HV)
Training Data
Validation Data
DATA PROCESSING
Median filter
SACLASS
CLASSIFICATION
Land cover maps
OUTPUT MAPS
Forest / Non- forest maps
Validation 10

11. Satellite data Geological 2016: 184 2018 survey) 2017: 218 Data
Provider
Quantity (scene)
Time
Band
Resolution
PALSAR-2
JAXA
2014: 42
2014, 2015,
HH, HV
50 m
(ScanSAR)
2015: 176
2016, 2017,
2016: 184
2017: 218
2018: 174
2018
MODIS-
USGS
NDVI
250 m
(U.S.
Geological 2016:
survey) 2017: 218
SRTM USGS DEM 30 m 11

12. Reference data Visual interpretation on Google earth
48,957 training point
21,452 validation points
The distribution of training data
The distribution of validation data 12

13. Results and Discussion
Land cover map 2014 Land cover map 2015 Land cover map 2016
Land cover map 2017
Land cover map 2018
1 3

14. Integration of land cover maps into FNF maps
Reclassification
Land cover maps classes
FNF classes
1 4

15. Results of Forest / Non-forest maps
FNF map 2014 FNF map 2015 FNF map 2016
FNF map 2017
1 5
FNF map 2018

16. Forest area calculation based on FNF maps
No.
FNF map
Number of forest pixels
Forest area (ha)
Total area (ha)
Forest coverage (%) 1
2014
36,697,345
9,174,336
16,440,050
55.8 2
2015
38,649,189
9,662,297
58.8 3
2016
38,145,176
9,536,294
58.0 4
2017
38,387,864
9,596,966
58.4 5
2018
38,965,344
9,741,336
59.3

17. Overall accuracy (OA) assessment
No.
Year
OA of LULC
(%)
OA of FNF (%) 1
2014 64 86 2
2015 74 90 3
2016 73 91 4
2017 5
2018

18. Compare with JAXA FNF
N22E104_ScanSAR
N22E104_JAXA

19. + Visual interpretation + Field trip to Viet Nam on March 2019
Future work
Continuing to make training data for the central region and southern Viet Nam by:
+ Visual interpretation
+ Field trip to Viet Nam on March 2019
Do FNF classification for the rest part of Viet Nam
Analyzing forest change and compare with statistical data of Viet Nam and other FNF global maps 19

20. THANK YOU FOR YOUR LISTENING!

21. Supplementary

27. Forest gain and lost between 2015 and 2018

28. 29

29. 3 0