j02 swe beny friend presentation

8/14/2019 j02 Swe Beny Friend Presentation

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Introductionntroduction Crop production estimates in many countries are generally

based on conventional techniques of data collection throughfield visits and reports, which is labour intensive and timeconsuming

The advent of satellite remote sensing provides opportunitynot only in identifying crop classes but also of estimating cropyield (Mohd et al. 1994);

Yield estimation can also easily derived from single date dataduring at panicle initiation and heading stages of the crop

Forecasting crop yield well before harvest is crucial forplanners and decision makers.

Monitoring of crop development , crop growth, and earlyyield prediction are generally important .


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ObjectivesObjectives

To discriminate crop types and wheat acreage using

IRS-P6-LISS-III(3rd March,2005) data during Rabi

season

To investigate the relationship between NDVI and fieldlevel crop yield in wheat

To investigate the relationship between wheat yield and

NDVI combining with land and management factors

for yield estimation at field level


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Study AreaStudy Area

The study area of Saharanpur district ,Uttar Pradesh

State is situated in N 2934 19to 30 2358latitude

and E 77 07 24 to 77 57 10longitude.

The Saharanpur district is apart of the Indo-Gangeticalluvial plain with the alluvium belonging to the

Pleistocene as well as sub-recent and recent time

Saharanpur is primarily agricultural district and 70%

of the land is used for agriculture.

The climate of study area is sub-tropical, semi-arid,

receive rains from south west monsoon from july to

september,

The mean annual rainfall is 1170mm and temperature

is 24.79C.(31.37C in summer and 15.12C in winter)


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Study AreaStudy Area


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Data UsedData Used

IRS-P-6- LISS-III High Resolution Satellite Data

1 : 50000 scale Topo-sheet (Survey of India)

1 : 250000 Soil Type Map of NBSS & LUB

Land management factors such as:drainage,erosion,texture of the surface soil, soil of the land, climate, coarsefragments, soil PH, EC, etc,

Field data was collected from farmers by interviewmethod and actual crop harvest at randomly selected cropcutting experiments (CCE) through GPS.


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MethodologyMethodology

Atmospheric and radiometric correction-The reflectance of various land use / land cover features

reaching the satellite sensor is attenuated by atmosphere.

-Dark object subtraction was used to correct the image for

atmospheric effects.

Rectification of the corrected image

-Corrected image was georeferenced in UTM projection using

ground control points (GCPs) from the Survey of India

topographical maps at 1:50,000 scale.

- Georeferenced images was then resampled to 23.5m pixel sizeusing nearest neighbour technique.


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Atmospheric correction of satelliteAtmospheric correction of satellite

datadata

Haze correction is computed from the dark object

values (Chavez 1996):

L ,haze = L ,min - L ,1%

= * d2 * (L sat - L haze) / ESUN * cos2

0

2

4

6

8

10

12

-0.2 -0.1 0.0 0.1 0.2 0.4

NDVI corr -NDVI toa

pixels

(103)


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Spectral response of crop healthySpectral response of crop healthy

Green plants have a unique spectral reflectanceinfluenced by their structure and composition.

The proportion of radiation reflected in different parts ofthe spectrum depends on the state, structure andcomposition of the plant

In the visible part of the spectrum (0.4 m 0.7 m),plants absorb light in the blue (0.45 m)and red (0.6 m)regions and reflect relatively more in the green portion ofthe spectrum due to the presence of chlorophyll

High photosynthetic activity will result in lowerreflectance in the red region and high reflectance ininfrared region of the spectrum.

In the near-infrared portion of the spectrum (0.7 2.5m), green plants reflectance increases to 40 60%.


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Spectral reflectance curve of healthySpectral reflectance curve of healthy

vegetationvegetation

Wavelenth micrometer

P

ercentreflectan

ce


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Spectral Response Curve (LISS_ III)

0

20

40

60

80

100

120

140

160

Band_

1

Band_

2

Band_

3

Band_

4

(DNValue)

Wheat Sugarcane Orchard Fallow_land

forest Settlement water_body

Spectral response of different land useSpectral response of different land use

/ land cover/ land cover


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Class_

Name WheatSugar

cane Orchard

Fallow

land Forest

Riverine

Forest

Plantation

Forest

Settle-

ment

River

Bed

Water

Body

Wheat 0 1999 2000 2000 2000 2000 2000 2000 2000 2000

Sugarcane 2000 0 1967 1992 1999 1995 1908 1985 2000 1985

Orchard 2000 1967 0 1591 1283 1653 1995 1978 2000 2000

Fallow_land 2000 1992 1591 0 1533 1991 2000 2000 2000 2000

Forest 2000 1999 1283 1533 0 1964 2000 2000 2000 1999

Riverine

Forest2000 1995 1653 1991 1964 0 1995 1895 2000 1999

PlantatioForest

2000 1908 1995 2000 2000 1995 0 1837 2000 2000

Settlement 2000 1999 1978 2000 2000 1895 1837 0 1918 1997

River Bed 2000 2000 2000 2000 2000 2000 2000 1918 0 2000

Water Body 2000 1985 2000 2000 1999 1999 2000 1997 2000 0

Seperability of land use classesSeperability of land use classes


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Digital Classified Map of rabi seasonDigital Classified Map of rabi season


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Class_

Name WheatSugar

caneOrch-

ard

Fallow

landForest

Riverine

Forest

Plantation

Forest

Settle

-ment

River-

BedWater

BodyTotal

Accuracy

%

Wheat 2047 0 0 8 0 0 0 0 0 1 2056 99.56

Sugarcane 19 254 1 0 0 1 12 9 0 3 299 85.62

Orchard 0 0 198 6 16 12 0 9 0 3 244 81.15

Fallow_land 0 0 6 651 36 0 0 0 0 0 693 93.94

Forest 0 0 13 7 793 0 0 0 0 0 813 97.54

Riverine_

Forest0 0 3 1 0 792 0 41 0 4 841 94.17

Plantation

Forest0 7 0 0 0 0 493 43 0 1 544 90.63

Settlement 0 0 1 6 0 18 7 1628 66 0 1726 94.32

River_Bed 0 0 0 0 0 0 0 31 758 0 789 96.07

Water_Body 0 0 0 0 0 1 0 0 0 429 430 99.77

Total 2066 261 222 679 845 824 512 1761 824 441 8435 93.28

User

Accuracy%99.08 97.32 89.19 95.88 93.85 96.12 96.29 92.45 91.99 97.28 94.94 95.49

Accuracy assessmentAccuracy assessment


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Land Use Area (ha) Area %Wheat 187298.00 50.89 %

Sugarcane 22200.10 6.03 %

Orchard 50411.40 13.70%

Fallow land 34057.80 9.25%

Forest 28772.70 7.82%

Riverine forest 2635.83 0.72%

Plantation forest 2051.39 0.56%

Settlement 28602.60 7.77%

River bed 9479.76 2.58%

Water body 2508.82 0.68%

Total Area (ha) 368018.40

Classified Land use AcreagesClassified Land use Acreages


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Crop Yield estimationCrop Yield estimation

Crop yield prediction models are necessary for assessing the

production of particular crop in region. Hence, a present study

focuses on following hypotheses

Yield = ( NDVI )

NDVI = (Land, Management)

Yield = (NDVI, Land, Management) )


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Schematic diagram for crop yieldchematic diagram for crop yieldestimation in wheatstimation in wheatSatellite Image NDVI Image

Wheat Crop

Masked

Classified Image

Wheat Crop masked

NDVI

Land Factors-LPI ,Sys

Extraction of Mean 3x3 Pixel

Window Pertaining to Sample

Sites

GPS Location

Sample Sites

Management

Factors- Irrigation,

Fertilizer

Empirical yield model

development

Crop Cutting

Experiments

Training Signature

Generation

Validation


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Field data collectionField data collection


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CCE and NDVI of wheatCCE and NDVI of wheat


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YIELD(Q/ha)

504540353025201510

Fre

quency

10

8

6

4

2

0

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

0 10 20 30 40 50 60

Observed value

Expectednormal

Sd = 10.187

Mean = 33.05

Kolmogorov_Smirnov Z test=0.815

Sd = 10.187

Mean = 33.05

Kolmogorov_Smirnov Z test=0.815

Histogram fitted normal curve and ZHistogram fitted normal curve and Z

score of field datascore of field data


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0

10

20

30

40

50

60

0.2 0.4 0.6 0.8 1

NDVI

yield(Q/h

Single date NDVI-Yield relationshipSingle date NDVI-Yield relationship

Yield (Q/ha) = 60.84*NDVI 9.895

(Adj. R2 = 0.521, SEE = 7.142 N = 44)


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Yield mapYield map

45 1486.60 0.79

Total 187297.99

Yield(Q/ha) Area(ha) Area %


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Soil type map of study areaoil type map of study area


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3922253N=

SOILTYPE

UP_C

L

LP_FL

AP_FS

AP_FL

AP_C

L

AFP_SS

yiel

d(Q/ha)

60

50

40

30

20

10

0

Soil types and its relation to yield andoil types and its relation to yield andNDVIDVI


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Land/soil suitability indicesand/soil suitability indices Land Productivity Index ( LPI)

-LPI is based on general characteristics of thesoil profile, texture of the surface soil, soil of theland, climate and other physical factors

affecting use of land.( LPI) = A*B*C*X*Y

A = General characteristics of soil profile

B = Texture of the surface soil

C = Slope of the land

X = Miscellaneous factors; reaction of

surface soil , fertility , erosion

Y = Average annual rainfall

Classes Ranges

Excellent

(Class I)80 100

Good (Class II) 60 80

Fairly Good

(Class III)40 60

Average

( Class IV )20- 40

Poor ( Class V ) 10 20

Very Poor

( Class VI )< 10


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Pedon/profile No. A B C X Y LPI LPI Remarks

Xa Xb Xa, Xb

P26` 50 85 100 95 90 85 31 40.87 Fairly Good

P23 90 85 100 95 95 90 62 55.75 Good

NP1 100 95 100 100 100 95 90 92.6 Excellent

P12 100 95 100 100 100 95 90 92.6 Excellent

P16 100 100 100 100 100 95 95 95 Excellent

P14 100 95 100 100 100 95 90 92.6 Excellent

P18 100 100 100 100 100 95 95 95 Excellent

P20 100 95 100 100 100 95 90 92.6 Excellent

P31 80 90 100 90 90 95 55 54 Fairly Good

P1 55 90 100 100 100 98 49 40.37 Fairly Good

P3 50 90 100 90 100 98 40 45.76 Fairly Good

P36 90 90 100 100 100 98 79 84.52 Excellent

P5 90 100 100 95 100 98 84 86 Excellent

P6 90 100 100 89 100 98 78 86.8 Excellent

P7 90 100 100 100 100 98 88 89 Excellent

P9 90 100 100 100 100 98 88 89 Excellent

P11 90 100 100 100 100 98 88 89 Excellent

P2 85 85 100 85 90 98 54 72.56 Good

Values of different rating factors of LPI for allValues of different rating factors of LPI for all

pedonspedons


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Sys Indexys Index Sys is a parametric approach of FAO frame work of

land evaluation in which numeral rating of the differentlimitation levels of the land characteristics in a numeralscale from maximum ( normally 100) to a minimum

values is assigned.

Sys Index = A * B/100 * C/100* .

( A, B and C are ratings of soil and land

characteristics) An important characteristics is rated in a wide scale

( 100 25) , a less important characteristics in anarrower scale ( 100 60)


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Soil depth depth depth_fr Weight (100cm)

pH (1:2) E.C. O.C. O.M (%) Texture text_rat CEC ESP BS (%)

0-22 22 22 1.75 7.28 0.53 0.75 1.29 sil 100 16.93 3.08 99.27

22-37 15 3 1.75 8.37 0.25 0.3 0.52 cl 100 16.32 6.66 100.95

12 1.25 8.37 0.25 0.3 0.52 100 16.32 6.66 100.00

37-53 16 13 1.25 8.4 0.26 0.07 0.12 cl 100 17.36 5.76 84.74

13 0.75 8.4 0.26 0.07 0.12 100 17.36 5.76 84.74

53-79 26 12 0.75 8.56 0.24 0.15 0.26 sicl 100 17.58 5.44 89.84

14 0.25 8.56 0.24 0.15 0.26 100 17.58 5.44 89.84

79-98 19 11 0.25 8.78 0.33 0.15 0.26 cl 100 18.01 12.3 94.81

wt_text wt_ph wt_ec wt_oc wt_om wt_cec wt_bs wt_esp

3850 280.28 20.41 28.88 49.78 651.65 3822.02 118.60

0 0 0.00 0.00 0.00 0.00 0.00 0.00

525 43.9425 1.31 1.58 2.72 85.68 529.99 34.94

1500 125.55 3.75 4.50 7.80 244.80 1500.00 99.90

1625 136.5 4.23 1.14 1.96 282.10 1377.07 93.55

975 81.9 2.54 0.68 1.18 169.26 826.22 56.16

900 77.04 2.16 1.35 2.33 158.19 808.56 48.95

350 29.96 0.84 0.53 0.91 61.53 314.44 19.04

275 24.145 0.91 0.41 0.71 49.53 260.74 33.83

10000 799.3175 36.135 39.0575 67.37713 1702.744 9439.027 504.9662

100 7.99 0.36 0.39 0.67 17.03 94.39 5.05

Physio-chemical characteristics of pedon and its weighted

average for SYS calculation (pedon P1)


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The effect of management on yieldhe effect of management on yield

554N =

UREA (kg/ha)

360.00270.00180.00

Yield(Q/ha)

50

40

30

20

1 2 3 4

Irrigation frequency

0.00

10.00

20.00

30.00

40.00

50.00

yield

(Q/ha)

n= 3

n= 11

n= 10

n= 4

Yield = 11.85 + 8.13*Irrigation applied

R2 = 0.51

Error bars (95% CL of Mean)


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Correlation of RS, Land andorrelation of RS, Land andManagement Factorsanagement Factors

806040200806040321.8.6.4

60

40

200

40200

80

60

40

3

2

1

.8

.6

.4

60

40

20

0

YIELD

NDVI

IRRI

LPI

YIELD

SYS

NDVI IRRI LPI SYS


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Variable Count Min Max Mean SD

Pearson

Correlation

Remote Sensing

NDVI 44 0.44 0.88 0.719 0.118 0.729**

Land factors

L P I 18 40.4 95.0 76.7 19.9 0.609**

SYS Index 18 15.0 79.0 61.0 17.19 0.661**

Management input

Urea applied

(Kg/ha)14 180 360 276 74.5 0.446

Irrigation

frequency18 1 4 2.57 0.87 0.176**

Descriptive Statistics of Casual Variables & Its Correlation With WheatDescriptive Statistics of Casual Variables & Its Correlation With Wheat

YieldYield


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VariablesCo

untModel fit R 2

Adj

R2SE

Ep

Remote Sensing

NDVI 44 -9.895+60.84*NDVI 0.532 0.521 7.142 0.001

NDVI 18 -1.715+49.0*NDVI 0.432 0.403 8.046 0.003

RS& Land factors

NDVI,L P I 18-10.689+37.604*NDVI+

0.222*LPI0.596 0.542 7.049 0.001

NDVI, SYS 18-4.925+31.495*NDVI+

0.256*SYS Index 0.561 0.502 7.34 0.002

RS , Land Factors& Management Inputs

NDVI , L P I

,Irrigation

frequency18

-6.148+12.981*NDVI+

0.197* LPI+5.694*Irri 0.722 0.663 6.049 0.00

NDVI, SYS,

Irrigation Input 18

-0.787+13.534*NDVI-

0.178*SYS

index+5.116*Irrigation

0.653 0.579 6.758 0.002

Model of RS, land and management factorsModel of RS, land and management factors


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Validation of estimated yield inalidation of estimated yield inwheat(Model heat(Model NDVI,LPI,IrrigationDVI,LPI,Irrigationfrequencyrequency ))


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Conclusiononclusion Wheat crop is highly separable and can be discriminated

with more than 95% accuracy using high resolutionmulti_spectral LISS-III on board IRS-P6 satellite data.

Single date image which is taken at panicle initiation andheading stages is also provide good information for yield

prediction. linear and non-linear empirical relation of NDVI and

land, management factors has shown possibility of usingsatellite NDVI for retrieving yield model.

The combination of NDVI, land and management factors

can approve field level yield prediction than NDVI alonemodel.


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j02 swe beny friend presentation

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