Download - 04 I Nengah Surati Jaya 20120917
9/10/2012
1
I Nengah Surati Jaya,Syaiful DaulayMukalilAyub M Buce SalehLilik B Prasetyo (IPB)
I Nengah Surati Jaya,Syaiful DaulayMukalilAyub M Buce SalehLilik B Prasetyo (IPB)Lilik B Prasetyo (IPB)Yoshio AwayaMasanobu ShimadaKiyono YoshiyukiShigeru OnoIns‐[email protected]
Lilik B Prasetyo (IPB)Yoshio AwayaMasanobu ShimadaKiyono YoshiyukiShigeru OnoIns‐[email protected]
BACKGROUND
METHODS
STUDY SITES
I Nengah Surati Jaya,Syaiful DaulayMukalilAyub M Buce SalehLilik B Prasetyo (IPB)
RESULTS & DISCUSSION
CONCULSION
y ( )Yoshio AwayaMasanobu ShimadaKiyono YoshiyukiShigeru Ono
9/10/2012
2
A. RADAR SYSTEM Becoming more popular coupling optical data ALL WEATHER DATA
1. RADAR IS AN ACTIVE SENSOR PENETRATE CLOUDE AND HAZE
2. LONGER WAVELENGTH PENETRATE FOREST CANOPY
3. BACKSCATTER = F (WAVELENGTH OF THE SENSOR, ROUGHNESS OF OBJ BEING SENSED.
QUESTIONS: WHY we need to explore the backscatter
characteristic in tropical forest (1) ?1. Vegetation canopy interacts with
λ fl d b hλ as a group influenced by the leaves, branches and tree trunks.
2. The degree of backscatter of veg. related to the volume of canopy (biomass). Backscatter will be high if the wavelength usedl be high if the wavelength usedclose to the average size of the vegetation component.
9/10/2012
3
QUESTIONS: WHY we need to explore the backscatter
characteristic in tropical forest (2) ?1 λ of 2 5 cm is good1. λ of 2‐5 cm is good
to recognize agric. crops and trees. 2. λ of 10‐30 cm surface
land surface or soil backscatter contribute larger than the leaves or branchesleaves or branches
3. Backscatter from VEG. can be amplified by using cross‐Polarization
• Penetration radar is a function of the amount of biomass in the canopy
• A longer λ penetrate deeper into the ground.• Shorter λ is more influenced by a small canopy components
(e g K X and C bands): leaves and twigs(e.g. K, X, and C bands): leaves and twigs• Longer λ is ore influenced by a larger canopy components
(eg L, P, and VHF bands): trunk, branches and soil surface
Radar 1 m Wavelength
Radar 1 cm Wavelength
9/10/2012
4
Backscatter Backscatter of vegetation of vegetation ((11/2/2))
1. Like polarization HH and VV can penetrate vegetation, thus it senses thesurface under vegetation.
2. In areas with no vegetation, the condition of the surface roughness, surface pattern of systematic (eg. grooves) and soil texturesystematic (eg. grooves) and soil texturewill affect the backscatter strength
Backscatter Backscatter of vegetation of vegetation ((2/2)2/2)
1. Vegetation and dry soil has a dielectric constant of approximately 1‐10. If the water content of vegetation height it will be the appearance on the image isbright due to higher dielectric constant ‐very useful to recognize healthy anddead plants.
2. Instead, clean water will appear darker because the water surface is speculareflector, so that the reflected energy coming away from the sensor.
9/10/2012
5
RS in Indonesia9. Now, remotely sensed data had been a
major data source for forest monitoringmajor data source for forest monitoring in Indonesia since 1990.
10.The MoF uses the 3‐yrs interval Landsatdata to map out indonesia forest cover using 23 classes, since 2003.
OPTICAL SATELLITE DATA PROBLEMS
1. Indonesia forestry sector is now mainly depended on optical data to map out forest cover. For 3‐yrson optical data to map out forest cover. For 3 yrs mapping use Landsat data, while for annual mapping use Modis Data.
2. Now, Landsat program is not presently operating at its full capacity; 2 satellites remain in orbit: Landsat 5 ( operating more than two decades beyond its original 3‐year mission, and Landsaty g y ,7, which suffered a malfunction in 2003 (strippings) but still continues to provide critical data. in 2012, New Landsat is planned to be launched.
9/10/2012
6
THE USE OF RADAR
1. The use of RADAR (PALSAR) is still at ( )the begining stage INTERPRETATION MANUAL VISUAL METHODS? was just developed by JICA, IPB,MoF (2009‐2011)
2 Th f RS t h l j l2. The use of RS technology major role GHG monitoring system MRV
OPTICAL SATELLITE DATA PROBLEMS (2)
1. Veg. structure and species are composition of tropical ecosystem arecomposition of tropical ecosystem are quite diverse
2. The advent of ALOS PALSAR data in 2006, Many scientists had explore its capability to derive land cover informationinformation.
3. The knowledge backscatter characteristics is quite poor NEED TO BE EXPLORED
9/10/2012
7
STUDY SITE AND DATA
DATA1. ALOS PALSAR of NORTH SUMATERA
Spatial Res 50 m x 50 m, 12.5 x 12.5 m, 6.25 x 6.25 m Band HH and HV, rec in 2008 (copyright: JAXA)
2. Ground Truth Data performed in 2009 and 2010
3. Landsat‐based land cover map of Kalimantan (2006)
9/10/2012
8
STUDY SITE
1. The study was performed in NORTH y pSUMATERA – surrounding Toba Lake
2. This study site covers: High land trpical forest, plantation forest, rubber and oil palm
STUDY SITES
STUDY SITES
9/10/2012
9
9/10/2012
10
50 m 6.25 m
9/10/2012
11
RUBBEROIL PALM
50‐M 12.5‐M
9/10/2012
12
PRE PROCESSING (SMOOTHING)
LAND COV MAP OF KAL (LANDSAT
BASED)
START
CLUSTERING
MERGING &
DATA OF GROUND TRUTH
DISCRIM ANAL
DENDRO EVAL
MERGING & LABELLING
END
IDENTIFIED STAND/ VEG. VAR.
9/10/2012
13
NATURAL FORESTPLANTATION FORESTPLANTATION FOREST
GROUND TRUTH
ESTATE CROP
BIOMASS MODELLING
INITIAL CLUSTERING
1. Unsupervised Classification: K‐pmeans method and measured with Euclidean Distance.
2. The dendrogram was drawn using Single Linkage Method
3. INITIAL CLUSTERING: 20 CLASSES
4. MERGED INTO several CLASSES
9/10/2012
14
NAT TROPICAL FOREST
CLUSTERING OF 50M X 50M
1. ONLY 6 CLUSTER FOREST
9/10/2012
15
SINGLE LINKAGE – NAT TROP FOREST
FOREST CLUSTER
FOREST CLUSTER
CLUSTER
WHAT VARIABLES AFFECTING THE MOST?
1DENSITY OF SAPLING 14BIOMASS OFPOLE2DENSITY OF POLE 15BIOMASS OF TREE
3DENSITY OF TREE 16CROWN TICKNESS OF SAPLING
6.2550 M
3DENSITY OF TREE 16CROWN TICKNESS OF SAPLING4DBH OF SAPLING 17CROWN TICKNESS OF POLE5DBH OF POLE 18CROWN TICKNESS OF TREE
6DBH OF TREE 19CROWN DIAMETER OF SAPLING7HEIGHT OF SAPLING 20CROWN DIAMETER OF POLE8HEIGHT OF POLE 21CROWN DIAMETER OF TREE
9HEIGHT OF TREE 22CROWN CLOSURE OF SAPLING9HEIGHT OF TREE 22CROWN CLOSURE OF SAPLING
10BASAL AREA OF SAPLING 23CROWN CLOSURE OF SAPLING
11BASAL AREA OF POLE 24CROWN CLOSURE OF SAPLING12BASAL AREA OF TREE 25LEAF AREA INDEX (LAI)13BIOMASS OF SAPLING
6.25 M
6.2550 M
9/10/2012
16
NATURAL FOREST VARIABLESEXAMINED
BASAL AREA THICKNESS OF POLE CROWN DIAMETER OF CROWN SAPLING
BIOMASS POLE BIOMASS SAPLING DENSITY
TREE HEIGHT TREE CROWN DIAMETER DBH OF SAPLING
DBH OF POLE SAPLING HEIGHT SAPLING CROWNTHICKNESS
CROWN CLOSURE SAPLING BIOMASS CROWN DIAMETER OF POLE
CROWN THICKNESS POLE DENSITY BASAL AREA OF POLE
DBH SAPLING BASAL AREA LEAF AREA INDEX
POLE HEIGHT TREE DENSITY BASAL AREA OF TREE
CLASSIFICATION RESULT OF NATURAL FOREST
1. Res 50 M X 50 M3 CLASSES OF BASAL AREA/BIOMASS
91%
1. RES 6.25 M2 CLASSES 60% OF BASAL
AREA/BIOMASS MANY NOISESAREA/BIOMASS MANY NOISES
9/10/2012
17
WHAT IS THE RESULT INFOREST PLANTATION?
DBHLAI
TREE HEIGHTTREE NUMBERSCROWN DIAETERCROWN AREA
50 M
CROWN AREASTAND DENSITY BASAL AREABIOMASS
6.25 M
CLASSIF RESULTS ON PLANTATION FOREST
1. Res 50 M X 50 M2 CLASSES OF TREE HEIGHT 61%
1. RES 6.25 M HOMOGENOUS VEG (LESSER NOISES)
3 CLASSES 85% OF STAND DENSITY & CROWN COVERAGECROWN COVERAGE
9/10/2012
18
VARIABLES ON RUBBER PLANTATION AGE OF PLANTATIONSPACING DISTANCEDENSITY PER HAAVERAGE OF TREE DIAMETERAVERAGE OF TREE HEIGHTBASAL AREAVOLUME PER HACROWN DIAMETER SIZECROWN THICKNESSCROWN AREA
50 M
RATIO SPACE OF TREE‐ CROWN AREABIOMASS PER HALAIDBH
6.25 M
50 M
VARIABLES ON OIL PALM PLANTATION AGE OF PLANTATIONSPACING DISTANCEDENSITY PER HAAVERAGE OF TREE DIAMETERAVERAGE OF TREE HEIGHTBASAL AREAVOLUME PER HACROWN DIAMETER SIZECROWN THICKNESSCROWN AREA
12.5 M
50 M
RATIO SPACE OF TREE‐ CROWN AREABIOMASS PER HALAIDBH
9/10/2012
19
CLASSIF OF RUBBER & OIL PALM
1. RUBBER
• ON 50‐M 2 CLASSES (CROWN DIAMETER) 75% ACC
• ON 12.5‐M, 3 CLASSES 65% ACC
2. OIL PALM• 50 M: 2 CLASSES (CROWN DIAMETER) 92%50 M: 2 CLASSES (CROWN DIAMETER) 92%
ACC
• 12.5‐M: 3 CLASSES (TREE HEIGHT) 65% ACC
CLASSIFICATION OF OIL PALM using alos palsar 12.5‐m
CLASS CROWN COVERAGE
MEAN HH MEAN HVCOVERAGE
1 < 8500 m2 ‐10.02 ‐18.31
2 8500‐16.750
‐7.36 ‐15.42
3 > 16.750 ‐9.24 ‐14.65
•ACC: 65%.
9/10/2012
20
CLASSIFICATION OF OIL PALM using alos palsar 50‐m
CLASS CROWN di t
MEAN HH MEAN HVdiameter
1 < 8.5 m ‐14.7 ‐24.76
2 8.5 ~ 15.2 ‐7.49 ‐14.72
ACC: 92%
RUBBER CLASSIF ON PALSAR 12.5‐M
CLASS
DBH RAT TREE SPAC/CRO
BIOMASS (T/HA)
MEAN HH
MEAN HVSS SPAC/CRO
WN AREAS (T/HA) HH HV
1 0‐15.49 0.63‐9.88 <4.15 ‐13.65 ‐22
2 15.49‐19.99
0.51‐1.51 4.16‐8.4 ‐6.5 ‐18.36
3 > 20.00 0.23‐1.57 > 8.41 ‐7.97 ‐15.67
Acc : 72%
9/10/2012
21
RUBBER CLASSIF ON PALSAR 50‐M
CLASS
DBH BASAL ARE3A
MEAN HH
MEAN HVSS ARE3A HH HV
1 0‐15.5 0 ~ 8.49 ‐14.73 ‐24.76
2 15.51‐20.99
8.49 ~ 15.59
‐7.46 ‐14.72
3 21 –26 52
15.6~ 23 57
‐2.03 ‐11.7226.52 23.57
Acc : 75%
GENERAL PATTERN
SO MANY NOISES PROVIDING HIGHER CONFUSION IN HIGHER RESOLUTION
IN HIGHER RESOLUTION, HIGHER NUMBER OF STAND VARIABLES AFFECTING BACKSCATTER
9/10/2012
22
PLANTATION FOREST (EUC GRANDIS)
OIL PALM AND RUBBER AREA
9/10/2012
23
NAT FOREST
MANY SMALL TREES ANDMANY SMALL TREES AND SAPLING
NATURAL FOREST
MANY SMALL TREES AND SAPLING
9/10/2012
24
PLANTATION : YEAR 1
PLANTATION (EUC GRANDIS) YEAR 2
9/10/2012
25
PLANTATION (EUC GRANDIS) YEAR 3
PLANTATION (EUC GRANDIS) YEAR 4
9/10/2012
26
RUBBER
OIL PALM
9/10/2012
27
CONCLUSION ON NATURAL FOREST
1. BACKSCATTER MAGNITUDE AND VARIATION ARE AFFECTED BY STAND VARIABLES
2. ON THE 6.25M‐RES BASAL AREA, BIOMASS AND HEIGHT CLASSES
3. ON THE 50‐M RES, BASAL AREA AND TREE BIOMASS
CONCLUSION ON FOREST PLANTATION
1. ON PALSAR 50‐M RES VARIATION OF BACKSCATTER TREE HEIGHTOF BACKSCATTER TREE HEIGHT
2. ON PALSAR 6.25‐M, BY STAND DENSITY AND CROWN COVERAGE.
3. ON PALSAR 6.25‐M 3 CLASSES WITH 85%
4 ON PALSAR 50‐M CAN ONLY BE4. ON PALSAR 50 M, CAN ONLY BE CLASSIFIED INTO 2 CLASSES WITH 61.7%
9/10/2012
28
CONCLUSION ON RUBBER
1. BACKSCATTER MAG OF RUBBER IS AFFECTED BY:
• DBH SIZE AND BASAL AREA FOR ALOS 50‐M
• DBH SIZE, RATIO TREE‐DISTANCE AND CROWN AREA AND BIOMASS VOLUME FOR ALOS 12.5‐M
2. ON 50‐M AND 12.5‐M, 3 CLASSES CAN BE IDENTIFIED WITH 75% ACC AND 72% ACC
CONCLUSION ON OIL PALM
3. BACKSCATTER MAG OF OILPALM IS AFFECTED BY:
• CROWN DIAMETER FOR ALOS 50‐M 2 CLASSES 92%
• TREE HEIGHT FOR 12.5‐M 3 CLASSES 65% .
4. BACKSCATTER IN HIGHER RES MUCH NOISE4. BACKSCATTER IN HIGHER RES MUCH NOISE NO SIGNIFICANT IMPROVEMENT FOR CLASSIFICATION
9/10/2012
29
CONCLUSION ON BIOMASS ESTIMATION MODEL
1. Biomass (carbon stock), particularly ( ), p yRUBBER BIOMASS could be estimated using ALOS PALSAR DATA either using original (raw) data or backscatter data
2. OIL PALM AND NATURAL FOREST tend t h d l ti hi ith thto have a good relationship with the backscatter value of ALOS PALSAR.
FUTRHER RESEARCH REQUIRED
More comphrehensiveMore comphrehensive data and analysis should be performed in various forest typeforest type.
9/10/2012
30
DATA EXPLORATION
1. NATURAL FOREST, FOREST ,PLANTATION AND OIL PALM CAN’T BE EXAMINED LACK OF DATA VARIATION
2. GOOD DATA RECORDS RUBBER BIOMASS ESTIMATIONBIOMASS ESTIMATION
Rubber BIOMASS ESTIMATION using 50‐m res
y = 75.76e0.384x
R² = 0.67260 00
70.00
80.00HH vs biomas 50 m
0.00
10.00
20.00
30.00
40.00
50.00
60.00
‐20.00 ‐15.00 ‐10.00 ‐5.00 0.00 5.00
HH vs biomas 50 m
Expon. (HH vs biomas 50 m)
156 3 0 256x 140 00
160.00
180.00
HV vs BIOM 50 m
Good model can be developed using 50‐m res
y = 156.3e0.256x
R² = 0.788
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
‐25.00 ‐20.00 ‐15.00 ‐10.00 ‐5.00 0.00
HV vs BIOM 50 m
Expon. (HV vs BIOM 50 m)
9/10/2012
31
Rubber BIOMASS ESTIMATION using 12.5 m res
Better models are provide
y = 68.59e0.320x
R² = 0.75870.00
80.00
HH vs biomas 12.5 m
are provide using 12.5 res
0.00
10.00
20.00
30.00
40.00
50.00
60.00
‐20.00 ‐15.00 ‐10.00 ‐5.00 0.00 5.00
HH VS HV BIOM 12.5 M
Expon. (HH VS HV BIOM 12.5 M)
y = 157.8e0.235x
R² = 0.821160.00
180.00
HV VS BIOM 12.5 M
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
‐30.00 ‐25.00 ‐20.00 ‐15.00 ‐10.00 ‐5.00 0.00
HV VS BIOM 12.5 M
Expon. (HV VS BIOM 12.5 M)
Oil palm BIOMASS ESTIMATION using 50‐m res
y = 367.6e0.336x 400 00
hh vs biom 50 mLack of data variation
yR² = 0.327
‐
50.00
100.00
150.00
200.00
250.00
300.00
350.00
400.00
‐15 ‐10 ‐5 0 5
hh vs biom 50 m
Power (hh vs biom 50 m)
Expon. (hh vs biom 50 m)
y = 657.4e0.207x
R² 0 143700.00
hv vs biom 50 m
Lack of data variation
R² = 0.143
‐
100.00
200.00
300.00
400.00
500.00
600.00
‐25 ‐20 ‐15 ‐10 ‐5 0 5
hv vs biom 50 m
Power (hv vs biom 50 m)
Expon. (hv vs biom 50 m)
9/10/2012
32
Oil palm BIOMASS ESTIMATION using 12.5 m res
y = 1412.e0.448x
R² = 0 4801,400.00
1,600.00
R = 0.480
‐
200.00
400.00
600.00
800.00
1,000.00
1,200.00
‐12 ‐10 ‐8 ‐6 ‐4 ‐2 0
Series1
Power (Series1)
Expon. (Series1)
y = 10569e0.359x
R² = 0.251
8 000 00
10,000.00
12,000.00
Lack of data variation
‐
2,000.00
4,000.00
6,000.00
8,000.00
‐25 ‐20 ‐15 ‐10 ‐5 0
Series1
Power (Series1)
Expon. (Series1)
Natural FOREST on palsar 50mTHERE IS RELATIONSHIP
BETWEEN THE INCREASE OF BASALINCREASE OF BASAL AREA AND BACK
SCTATTER IN NATURAL FOREST
1. BIOMASS vs Backscatter of HH & HV
9/10/2012
33
Natural FOREST on palsar 6.25THERE IS RELATIONSHIP
BETWEEN THE I C AS O IO ASSINCREASE OF BIOMASS AND BACK SCTATTER IN
NATURAL FOREST
1. BIOMASS vs Backscatter of HH & HV
NATURAL FOREST ON PALSAR 6.25‐M
MANY NOISE ONLY 2 CLASSES
9/10/2012
34
NATURAL FOREST ON PALSAR 6.25‐M