JAXA’s MRV- current status and future envision Masanobu ShimadaJapan Aerospace Exploration Agency
Earth Observation Research Center
MRV symposium at Tokyo ForumFeb. 17, 2011

•Concept•Satellite data
•Data analysis: system correction and radar backscatters•Definiens and SVM•Validation•Convert to the Biomass and Carbon•Remarks in future•Summary

MRV system

Necessary steps for MRV
1. Satellite Observation (remote sensing) and classification forthe land use and land use changes
2. Ground truth data collection (LULUCF and biomass)
3. Estimation of Biomass or carbon as 1. x 2.
Several models exist
1. Use the matured remote sensing method for the classification in severalclasses

JAXA’s approach for MRV establishment•Fully utilize the L-band SAR data archives (JERS-SARand PALSAR) - time series analysis andcomplementally supported by the optical data.
•Goals:•Global Forest/Non-forest, Land use and their changes
and the related carbon values•Validate the products at the validation sites, i.e.,
Sumatra, Borneo and several test sites (.i.e., Riauprovince)•Use the ALOS/PALSAR as the main sensor

Step-1

Step-2Step-3

Satellite data acquisitions
PALSAR- FBD/FBS
Processing
Calibration
Ortho-rectification
Slope correction
Mosaicking
Multi-year
Classification-multi layers
SVM+eCognition
Aggregation toforest/nonforest
Learning
Validation
Production of Horizons 1a~1d

Ground truth data
Land Use
Biomass
Lidar
Forest height

Carbon Estimate
Several models
M1
Model

Providers
Under negotiation
Horizon1a:Forest/Non-forest
Horizon1b:Land Use
Horizon1c:Forest/Nonforest change
Horizon1d:Land Use changeIndonesiaia and Australia ND’s
ALOS PALSAR 2009
coverageCycle 37(31 Jul - 14 Sep, 2010)

Indonesiaia and Australia ND’s
ALOS PALSAR 2009
coverageCycle 38(15 Sep – 30 Oct, 2010)

1) Sumatra/PALSAR

Essential components for SAR data analysis
Radiometric slope correction
Needs high accuracy DEM
Ortho-rectification
Needs accurate Doppler and range shift model
depending on the DEM

Orthorectification and Slope-Correction
West Sumatra

Orthorectification

050100km

Slope-Correction

100m spacing

01020kmGeometric accuracies
Co-registration: 0.261,0.277
Accuracy: 34.14: Landsat-mosaic
Accuracy: 11.00:CR-mosaic

1) Land Use classification -1
Land Use classification
Target area: Sumatra, Borneo and the others
Method : SVM (Nicolas Long´ep´e, Preesan Rakwatin, Osamu Isoguchi, M.
Shimada, Yumiko Uryu, “Assessment of ALOS PALSAR 50m Orthorectified FBD Data forRegional Land Cover Classificationby using Support Vector Machines,” IEEE Trans. GRS, in)
Data: PALSAR data 50m resolution web based
dataset for 2007 and 2009
DEM: SRTM3 (90 m resolution)
Training data: provided by WWF for Riau province ofSumatrapresss.

Mosaic Product Example (Sumatra 2009)
1) Slope-Corrected Mosaic (HH)

2) Slope-Corrected Mosaic (HV)SUMATRA
2009

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.

Mountain forest
Lowland forest
Peat/Non-Peat swamp forest
Riverine forest
Shrubs/Grassland
Agriculture/Bare soil
Plantation
Mangrove/Nipah
Degraded / Secondary forest
Recently Burnt / Clear cut
Water
Unclassified

Land use (horizon 1b)

Courtesy to WWF

ESTIMATION RESULT： Across Sumatra 2009 2/2
Overall Accuracy
83.5 %JAXAWWF
Non-Natural Forest

Natural Forest

User
Accuracy

Non-NaturalForest

82.4 %

16.0 %

68.2 %Natural Forest

17.6 %

84.0 %

92.0%

WWF
NaturalForest

Natural Forest
onSwampyMangrove Forest

NonForestUserAccuracy

Natural Forest

86.5 %

21.3 %

1.6 %

13.5 %

64.4 %Natural Forest
onSwampy

0.4 %58.0 %

2.3 %

2.2 %

65.8 %

Mangrove
Forest

0.0 %

0.3 %

15.7 %

0.3 %

50.3 %

Non-Forest

13.1 %

20.4 %

80.3 %

84.0 %

92.0 %Overall Accuracy
82.2 %JAXA

BORNEO
2009

1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.

Mountain forest
Lowland forest
Peat/Non-Peat swamp forest
Riverine forest
Shrubs/Grassland
Agriculture/Bare soil
Plantation
Mangrove/Nipah
Degraded / Secondary forest
Recently Burnt / Clear cut
Water
Unclassified

ESTIMATION RESULT : All Indonesia 2009

– Forest classification at 50m resolution over Riau
province, Indonesia– Size: 102 178 km²
40.871.201 pix. (86.5 %)Accuracy 35.338.102 /Summary•Systematic global observation using ALOS/PALSAR isunder way.
•Land use classification accuracy using the calibrated
SAR data is under evaluation. SVM gives higher
accuracy than the segment based thresh-holding forforest/non-forest classification.
•Conversion from the forest land use to biomass is thnext issue. (Next issue)•Collection of the training dataset as well as biomass
are important for MRV.

Global forest monitoring system using 18 year SAR
dataset (JERS-1 SAR ~ ALOS/PALSAR)
Product: Ortho-rectified and slope corrected highresolution global forestry data (gamma-naught,classification, (biomass))
Resolution: 10m
Sensor: JERS-1 SAR/ALOS-PALSAR
Reprocessing all the EORC/JAXA SAR archives
Dates: 1994-1996-1998~2007,2009
Products:Horizon1/2 + Mosaic
Aug. E, 2010

10m Global Mosaic 2009

10m Global Mosaic 2007

10m Global Mosaic 1995

10m Forest/Non-Forest Map 2009

10m Forest/Non-Forest Map 200710m Forest/Non-Forest Map 1995