real problem: bantul

1

Soil Science and Land Resources, Bogor Agricultural University ~~~

Special Topics: Rice Mapping and Monitoring

Data FusionClassification Techniques

SAR Analyses

Bambang H. Trisasongko

Department of Soil Science and Land Resources,Bogor Agricultural University. Bogor 16680. Indonesia.

Email: [email protected]


Real Problem: Bantul

Green: Bakosurtanal data; Blue: interpretation


Role of RS for Rice Monitoring

• Van Niel & McVicar (2001):

– Crop identification

– Area Measurement

– Yield estimation

– Disturbance

– Water exploitation

– Water efficiency

Van Niel, T.G., and T.R. McVicar. 2001. “Remote sensing of rice-based irrigated agriculture: a review”. Technical Report of CRC-Rice. CSIRO, Australia


Design of Information Extraction

• Requirements:

– Baseline data – Sawah Baku (VHR satellites)

• Real data

• Fused data

– Monitoring:

• Irregular (HR satellites)

• Regular (low resolution)

2


Data Fusion

• To combine advantages from both datasets:

– Panchromatic – High spatial resolution

– Multispectral – Lower spatial resolution

• Methods:

– Color Cube (IHS, HSV, dll.)

– Brovey

– Wavelet (Daubechies, Mallat, dll.)

– Dll.


ALOS/PRISM


Color Normalization

• Jim Vrabel (1996)

• Implemented in many remote sensing s/w:

– Linear

– Quick

0.10.3

0.3*)0.1(*)0.1(

i

i

ii

MSI

PANMSICN

Vrabel, J. 1996. Multispectral Imagery Band Sharpening Study. Photogrammetric Engineering and Remote Sensing. 62:9. pp. 1075-1083


Color Cube

3


Shih (1995) Approximation

• Forward Transforms:

)(3

1BGRI

)],,[min()(

31 BGR

BGRS

2/12

1

)])(()[(

)]()[(2

1

cosBGBRGR

BRGRH

Note: Hue is undefined if S = 0 and S is undefined if I = 0

Shih, T-Y. 1995. Reversibility of Six Geometric Color Spaces. PhotogrammetricEngineering and Remote Sensing. 61:10. pp. 1223-1232.


Shih (1995) Approximation

• Backward Transforms:


Principal Components

Pande et al. 2009. J. Ind. Soc. Rem. Sens. 37: 395-408


Some results

4


Advanced Data Fusion

• Wavelets: Discrete Wavelet Transform (DWT)

• Ehlers > Erdas Imagine 2010

• Specific-purpose data fusion

• Studies on texture shift due to fusion


Specific-purpose Leaf Area Index


Specific-purpose Classification


5


Decision Trees

• Why?

– Ease replication (on calibrated data)

– Adaptive to errors (atmosphere, sensor imbalance)

• Methods:

– C-4.5

– ID3

– RandomTree

– QUEST+CRUISE


QUEST and CRUISE

Acc = 93.9% Acc = 89.3%

Tjahjono et al. 2009. JurnalIlmiah Geomatika


Spectral Angle Mapper (SAM)

• Based on Hyperspectral data

• Interestingly applicable to multi spectral data

• Requires surface reflectance level processing

Alpha minimum >> similar spectrum


Spectral Angle Mapper

Acc = 96%

KELAS BERA GENERATIF VEGETATIF AWALMUSIM

BERA 100 0 0 0

GENERATIF 0 100 12.8 0

VEGETATIF 0 0 87.2 0

AWALMUSIM 0 0 0 100

Note on unclass (black)

6

Soil Science and Land Resources, Bogor Agricultural University ~~~ Soil Science and Land Resources, Bogor Agricultural University ~~~

POLSAR for Rice Monitoring

• Providing baseline status of rice growth using fully polarimetric datasets through:

– Backscatter analysis

– Polarimetric decompositions

– Compact polarimetric modes (only for mapping purposes)


Bo x P lo t (Sprea dsheet1 10 v*2 261 c)

M edi an

25%-75%

1 %-99 %

Ou tl ie rs

Extre m es

6 8-70

76-80

8 1-85

86 -90

91-95

9 6-1 00

10 1-105

106 -11 0

1 11-114

AGE

-24

-22

-20

-18

-16

-14

-12

VH


M edi an

25%-75%

1 %-99 %

Ou tl ie rs

Extre m es

6 8-70

76-80

8 1-85

86 -90

91-95

9 6-1 00

10 1-105

106 -11 0

1 11-114

AGE

-18

-16

-14

-12

-10

-8

-6

-4

VV


M edi an

25%-75%

1 %-99 %

Ou tl ie rs

Extre m es

6 8-70

76-80

8 1-85

86 -90

91-95

9 6-1 00

10 1-105

106 -11 0

1 11-114

AGE

-14

-12

-10

-8

-6

-4

-2

0

2

4

HH

HH is a good indicator for growth periodsSome outliers are associated with infestations


Entropy

76-80

81-85

86-90

91-95

96-100

101-105

106-110

111-115

115-120

121-125

126-130

131-135

Umur

2007 Outliers Extremes


0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

En

tro

pi

76-8081-85

86-9091-95

96-100101-105

106-110111-115

115-120121-125

126-130131-135

Umur



0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

Entr

op

i

Severe infestations create a drop in Entropy plots

Median

Mean

Entr

opy

Entr

opy

Age

Age

7


Rice Growth Based onEntropy Model

• Tends to saturate on mature period

Median

80 90 100 110 120 130 140

Umur

y=(-0,38087)+(0,014443)*x+(-0,51e-4)*x 2̂

R2=0,93286014

0,30

0,35

0,40

0,45

0,50

0,55

0,60

0,65

0,70

Entr

op

i

Age

Entr

opy


AlphaAngle

76-80

81-85

86-90

91-95

96-100

101-105

106-110

111-115

115-120

121-125

126-130

131-135

Umur



30

35

40

45

50

55

60

65

70

Su

dut A

lfa

76-8081-85

86-9091-95

96-100101-105

106-110111-115

115-120121-125

126-130131-135

Umur



30

35

40

45

50

55

60

65

70

Sudu

t A

lfa

Median

Mean

Alp

ha A

ngle

Alp

ha A

ngle

Age

Age


Rice Growth Based onAlpha Angle Model

Median

80 90 100 110 120 130 140

Umur

y=(122,817)+(-1,2191)*x+(0,004592)*x 2̂

R2=0,73617564

35

40

45

50

55

60

Sudut A

lfa

Lacks on PLR data

Age

Alp

ha A

ngle


C-P Plot – Migration due to Plant Growth

Basic TrendBasic Trend

Alp

ha A

ngle

Entropy

Medium entropyvegetation scattering

Medium entropysurface scattering

Improving Ishitsuka (2011) work…

Dipole

Double

Specular

Single

8


Compact Polarimetry

Biases were too highNot recommended for quantitative analysis

Images © JAXA, METISoil Science and Land Resources, Bogor Agricultural University ~~~

Summary on POLSAR

• HH is suitable for Ciherang cultivar

• Polarimetric Decompositions are also helpful

• No recommendation for Compact Polarimetry (needs some workouts)

• However:

– Insufficient PLR data


Leggi, in nome del tuo Signore che ha creatoHa creato l’uomo da un’aderenzaLeggi, ché il tuo Signore è il GenerosissimoColui che ha insegnato mediante il càlamoChe ha insegnato all’uomo quello che non sapeva

real problem: bantul

Documents