Forest / Non-forest (FNF) mapping for Viet Nam using PALSAR-2 time series images 2019/01/22 Truong Van Thinh – 201726070 Master’s Program in Environmental.

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Presentation transcript:

Forest / Non-forest (FNF) mapping for Viet Nam using PALSAR-2 time series images 2019/01/22 Truong Van Thinh – 201726070 Master’s Program in Environmental Sciences Supervisor: Prof. Kenlo Nishida Nasahara

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

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

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

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

The differences in JAXA FNF 6 2015_JAXA 2016_JAXA 2017_JAXA

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 2014 - 2018

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

Method Satellite images Output map Input data Algorithm Training data

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

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: 184 2018 survey) 2017: 218 SRTM USGS 26 2002 DEM 30 m 11

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

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

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

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

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

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

Compare with JAXA FNF N22E104_ScanSAR N22E104_JAXA

+ 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

THANK YOU FOR YOUR LISTENING!

Supplementary

Forest gain and lost between 2015 and 2018

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