Agricultural Drought Severity Analysis Using Landsat 8 ...kvkale.in/wp-content/uploads/2019/10/Viva-March-2016.pdf · Introduction of Remote Sensing “A remote sensing technique

Sandeep Gaikwad

Agricultural Drought Severity Analysis Using Landsat 8 Data of Vaijapur Tehsil.

117 April 2016

M.Phil. (Computer Science)

Sandeep Gaikwad

Presented By:Sandeep Vasant Gaikwad

Dr.Babasaheb Ambedkar Marathwada University, Aurangabad. 431004, Mah, India.

217 April 2016

Agricultural Drought Severity Analysis Using Landsat 8 Data of Vaijapur Tehsil

Guided By.

Prof. K.V. Kale,Professor, Director B.C.U.D.

Department of Computer Science & Information Technology, Dr. Babasaheb Ambedkar Marathwada University,

Aurangabad, (MS), India

Outline of presentation

17 April 2016 Sandeep Gaikwad 3

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Introduction of Remote Sensing

“A remote sensing technique is used for observing objects without being touched or without interfering with the object.”

4Sandeep Gaikwad17 April 2016

Fig 2. Elements of Remote Sensing Fig 1. Data acquisition

Introduction of Topic

According to Palmar, (1965), “Drought is an interval of time, generally of the order of

month of the year in duration during which the actual moisture supply at a given place

rather consistently falls short of the climatically expected or climatically appropriate

moisture supply” (palmar, 1965).

• Drought is least understood natural phenomena and it has a direct impact on livelihood,

and economy.

• Drought is consider as one of the main natural disasters, which have been occurring in

almost all climatic zones and damage to the environment and economies of several

countries.



• The government bodies spend time and money for drought survey, but this

process is a time consuming and challenging. Advantage of Geospatial

Technology is that, it is helpful to understand the drought prone area and its

severity level through satellite images.

• Drought has a high impact on economy of the country, because it is very hard to

balance between food demand and food supply. This issue has attracted the

attention of scientific community, government planner and society.

• Maharashtra is an agricultural state, where two-third of population is

engaged in agriculture and earn livelihood directly from this occupation.

Moreover, agriculture provides indirect employment to large portion of

population in agro-based occupations.




Figure 4. Type of droughts in detail.

(Source: National Drought Mitigation Centre, http://drought.unl.edu/whatis/concept.htm)

Type of Drought1) Meteorological Drought2) Hydrological Drought3) Agricultural Drought4) Socieoeconomic Drought

Fig 3. Drought Types

Objective of the Research

A) Main objective of the research is that identification of drought severity of Vaijapur tehsil using satellite based drought indicators.

B) To analyze changes in vegetation cover due to variation in rainfall using multi-date satellite imagery and generate maps of Land use and Land Cover (LULC).

C) Use of vegetation indices as an indicator for drought severity classification.


Problem Statement

The Vaijapur tehsil is suffering from the drought since decade. According to the Agricultural office of Vaijapur, yield capacity of cotton is surprisingly decreasing due to deficit rainfall and reduced soil quality because of drought.

It also causes so many problems like insect infestations, plant disease and wind erosion.

The entire taluka has received seasonal rainfall, which is less than average rainfalls. It causes to lowering the water level of the wells and borwell.

Water tanker has provided to every village in year 2013-2014.

The manual survey of drought and the identification drought severity level is a very tedious task and it requires much time than expected.


Motivation

• The two-third of population the Vaijapur is engaged in agriculture and earn livelihooddirectly from this occupation.

• Agricultural development in the tehsil is to a large extent dependent on availability ofwater.

• Arid climatic conditions in Vaijapur, characterized by erratic rainfall and successivedrought years together with high rates of city development and excessive water mininghave adversely affected in production levels thereby increasing the drought risk.

• Since there is not much scope to bring additional land under cultivation in Vaijapur,evaluation of probable risk arising out of drought in the region would help indeveloping better management plans for mitigating drought impacts.


Image Ref: http://www.satimagingcorp.com/satellite-sensors/

Study Area

• Vaijapur is a taluka in Aurangabaddistrict of Maharashtra state, India.Vaijapur taluka head quarter is aVaijapur town. It belongs toMarathwada region. Vaijapur tehsilwhich is located at latitude of19°40’ to 20°15’ north andlongitude of 74°35’ to 75°00’ east,covering an area of approximately1510.5 sq. km, it is shown in table 1and figure 1.3.



Fig 5. Study area.Fig 5. Study area.

Study Area..

Vaijapur Taluka information.


Serial No

Title Detail

1 Average Rainfall 500.20mm2 Average Temperature 34 ° C to 42° C

Elevation 514m (1,666 ft)3 Total Area 1,54,378 H.4 Agricultural Land 1,21,830 H5 Sowing Land 1,03,440H6 Kharif Land 74,477H7 Rabbi Land 28963H8 Crops Onion, sugarcane, Jwar, Bajra, Corn, Cotton, Wheat.

9 Water Project Narangi Dam, Bordahegaon dam, Shivana Takli, Manyad, Bhatana, Kholi.

10 Survey of India Toposheet No

47 I/9,47 I/13, 46 L/16

Table 1. Information of Study area

Review of Literature


No. Author/Year Country Sensor Study

Period

Technique

1 D. Nithya et. al, 2014. Srivilliputhur Taluk Of

Virudhunagar District,

Tamil Nadu

Landsat ETM+,

LISS III.

1990, 2000

and 2011

This paper is focused on SPI, NDVI and NDWI are very useful for

early detection of agricultural vulnerability and hence should be a

better methodology for remote sensing based vulnerability

assessment studies.

2 Sashikkumar

et. al, 2013.

Chittar sub basin,

Thamiravaruni,

Tamilnadu

1982,1986,

1989, 1999,

and 2001

Drought is assessed on the basis of Percentage Deviation of

rainfall from long term annual mean precipitation.

3 Sumanta Das, Malini Roy

Choudhury & Sachikanta

Nanda.

2013.

Bankura District, West

Bengal.

Landsat ETM+

(2000),

Landsat TM

(2005),

Landsat TM

(2010).

It is found that the temporal variations of NDVI anomaly, VCI,

TCI and MSI are closely linked with SPI and a strong linear

relationship exists between them. Satellite derived drought-

monitoring indices have also been correlated with precipitation

index to see how vegetation stress condition and consequently

agricultural production yield is changing with the variability of

rainfall.

4 Kipterer John Kapoi,

Omowumi Alabi, 2012.

Nakuru, Rift Valley of

Keniya.

NOAA-

AVHRR

2000,2010,2

011

The use of temperature and vegetation index provides adequate

means for mapping drought extended over agricultural field. Land

Surface Temperature (LST), NDVI, Water Supplying Vegetation

Index (WSVI). Precipitation data of 10 year used for area of

study.

5 Chaoudhary, Garg, Ghosh.

2012.

Bhopalgarh Tehsil,

Jhodpur, India

Landsat-7

ETM+

2000-01,

2001-02,

2009-10

This paper has presented drought indices like NDVI, Vegetation

Condition Index (VCI), Soil Adjusted Vegetation Index (SAVI),

Vegetation Condition Index(VCI), Land Surface

Temperature(LST), Temperature Condition Index(TCI). The

analysis of Landsat Image Data of Pre and Post monsoon of Year

2000, 2002, 2010 was used to detect drought affected area.

Click here for literature Survey.Table 2. Literature Survey.

Review of Literature

According to literature survey, following indices are useful for agricultural drought indices.

• NDVI (Normalised Difference Vegetation Index)• VCI (Vegetation Condition Index)• SAVI(Soil Adjusted Vegetation Index)• TCI (Temperature Condition Index)

• The meteorological indices like PDSI, SPI index which is required 30+ years ofprecipitation record for computation.

• The cited paper has suggested that, the satellite based drought indices like NDVI, VCI,SAVI, and TCI is an important for drought severity analysis.



Click here for literature Survey.

Meteorological DataAncillary Data

Satellite Data

Rainfall &

TemperatureGround Truth

Collection

Multi-date Landsat 8

Images

Atmospheric correction,

Radiometric correction.

Extraction of NDVI,

VCI, SAVI, TCI

indices.

Analysis of Rainfall &

Temperature data

Result and Analysis

Drought Severity

Identification

Vegetation Cover

extraction using

Supervised ML

Classifier.

Agriculture sown area

statistics

Sample Training (Pure pixel)

Classified LULC map

Accuracy Assessment

Fig 6. Methodology

Define Sensor

Select Atmosphere

Load Image

Calibration File

Visibility

Reference spectrum

SPECTRA Scene

spectrum

a) Edit cal file

b) Inflight calibration

Cal Ok?

Image Processing,

-constant / variable vis

-constant / variable wv

P, TP, (nadir)

Classification

Spectral PolishingValue Adding

ρ, T

P, (nadir)ρ, (nadir)

No

Yes

BRDF cor

Atmospheric Correction ModelFig 7. Atmospheric Correction model

Digital numbers from L8-TM

thermal band 10 and Band 11

Surface Emissivity

Band 10 and Band 11 Scaling

Factor

Atmospheric correction parameterAtmospheric transmission, Upwelling radiance, Downwellingradiance

Top of the atmosphere

radiance

At Spectral radiance

Temperature leaving the

earth surface (Kelvin)

Temperature Extraction Process from Landsat 8.

Fig 8. Temperature Extraction Model

Dataset


Sensors OLI and TIRS

Spectrum Band 1 - Coastal aerosol (0.43 µm - 0.45 µm),

Band 2 - Blue (0.45 µm - 0.51 µm)

Band 3 – Green (0.53 µm - 0.59 µm)

Band 4 - Red (0.64 µm - 0.67 µm)

Band 5 - NIR (0.85 µm - 0.88 µm)

Band 6 - SWIR 1 (1.57 µm - 1.65 µm)

Band 7 - SWIR 2 (2.11 µm - 2.29 µm)

Band 8 – Pan (0.50 µm - 0.68 µm)

Band 9 - Cirrus (1.36 µm - 1.38 µm)

Band 10 - Thermal Infra (TIRS) 1(10.60 µm -

11.19 µm)

Band 11 - Thermal Infra(TIRS) 2 (11.50 µm- 12.51

µm)

Resolution 30m PAN, 30m Multi

Swath 185km

Revisit Time 16 days

On-board of LANDSAT 8

Distributors USGS, NASA

Fig 9. Landsat 8Table 2. OLI and TIRS Sensor Detail

Dataset


Table 3.14. Landsat8 product information

Landsat 8 dataset

Serial Feature Detail

1 Sensor Operational Line OLI (OLI), ThermalInfrared Sensor (TIRS)

2 Product Type Level 1T (terrain corrected)

3 Output format GeoTIFF

4 Swath 185KM

5 Pixel Size Panchromatic 15M, Multispectral 30M,Thermal 100M

6 Map Projection UTM (Polar Stereoscopic for Antarctica)

7 Datum WGS84

8 Orientation North-up (Map)

9 Resampling Cubic Convolution

10 Accuracy OLI: 12 meter circular error, 90 percentconfidence,TIRS: 41 meter circular error, 90 percentconfidence

11 Data management and Preprocessing

United State Geological Survey (USGS)

ASTER DATASET

SerialNo

Feature Detail

1 Sensor ASTER2 Data format GeoTIFF3 Resolution 1 arc-second (30 m) grid4 Projection WGS84/EGM96 geoid5 Accuracy 20M6 Confidence

level95 % confidence for vertical dataand 30 meters at 95 % confidence inhorizontal data

Landsat 8 dataset of Path/ Row 147-46Serial

No2013 2014 Month of Year

2013 & 2014Season

1 21/06/2013 10/6/2014 June Kharif

2 7/7/2013 26/07/2014 July Kharif

3 8/8/2013 11/8/2014 August Kharif

4 25/09/2013 12/9/2014 September Kharif

5 27/10/2013 1/10/2014 October Rabbi

6 12/11/2013 1/11/2014 November Rabbi

7 14/12/2013 1/12/2014 December RabbiTable 3. Landsat 8 Dataset

Table 4. ASTER Dataset

Table 5. Dataset Detail.

Ground Truth Collection-Field Visit


Its drought Survey.

Fig 10. Ground Truth collection.

Ground Truth Collection-Field Visit


Figure 3.1. Ground truth collection in the western region.

Click here for Ground Truth (91)

Fig 11. Ground Truth Collection.

Satellite Based Drought Indices


Normalised Difference Vegetation Index (NDVI).

• The NDVI is a word-wide popular index, used for vegetation analysis. NDVI index is used for the vegetation cover study of the earth, it is also used for monitoring drought condition.

• The NDVI is an index of vegetation health and density, it was suggested by tucker in 1979 and it has been used for mapping of agricultural drought assessment

𝑁𝐷𝑉𝐼 =(𝑁𝐼𝑅−𝑅𝐸𝐷)

(𝑁𝐼𝑅+𝑅𝐸𝐷)eq 1.

Where, NIR= near infrared band, RED= Red band;

Serial Title Values

1 NDVI Range -1 to +1.

2 Normal Condition Location based

3 Severe condition -1

4 Healthy Condition +1

Table 6. NDVI Severity classification criteria

Table 6. NDVI severity Criteria



Vegetation Condition Index (VCI).

Kogan (1990) developed the Vegetation Condition Index (VCI) using the range of NDVI which is a good indicator for assessing the severity of agricultural drought. It is defined in eq 2.

𝑉𝐶𝐼 =(𝑁𝐷𝑉𝐼−𝑁𝐷𝑉𝐼𝑚𝑖𝑛)

(𝑁𝐷𝑉𝐼𝑚𝑎𝑥+𝑁𝐷𝑉𝐼𝑚𝑖𝑛)∗ 100 eq 2.

Where, the NDVI is the actual value of NDVI, NDVI max and NDVI min are calculated from long term records.

Table 7. VCI Severity classification criteria

Serial Title Values

1 VCI Range 0% to 100%

2 Normal Condition 50%

3 Severe condition 0%

4 Healthy Condition 100%



Soil Adjusted Vegetation Index (SAVI).

The SAVI was developed as a modification of the Normalized Difference Vegetation Index to correct for the influence of soil brightness when vegetative cover is low. The soil moisture percentile is commonly used to measure the agricultural drought

𝑆𝐴𝑉𝐼 =(𝑁𝐼𝑅−𝑅𝐸𝐷)

(𝑁𝐼𝑅+𝑅𝐸𝐷+𝐿)∗ (1 + 𝐿) eq (3)

Where, NIR is the near Infrared band, RED is the red band, and L is the soil brightness correction factor. Generally, an L=0. 5 works well in most situations and it is the default

value used for SAVI index.

Table 8. SAVI Severity classification criteria

Serial Title Values

1 SAVI Range -1 to +1.

2 Normal Condition Location based

3 Severe condition -1

4 Healthy Condition +1



Temperature Condition Index (VCI).

The TCI was also suggested by Kogan (1997). It was developed to analysis, vegetation response to temperature, i.e. the higher the temperature more extreme the drought .The TCI is used for analysis of temperature-related vegetation stress and stress caused by excessive wetness

𝑇𝐶𝐼 =(𝐵𝑇𝑚𝑎𝑥−𝐵𝑇)

(𝐵𝑇𝑚𝑎𝑥−𝐵𝑇𝑚𝑖𝑛)∗ 100 eq 4.

Where, BT, BT max and BT min are smoothed weekly brightness temperature, absolute maximum and its minimum.

Table 9. TCI Severity classification criteria

Serial Title Values

1 TCI Range 0% to 100%

2 Normal Condition 50%

3 Severe condition 0%

4 Healthy Condition 100%

Land Use and Land Cover


To analysis the remote sensing data with maps, Geographic Information Systems (GIS) technology plays a vital role with related entities that have geographic distribution. On the other hand trees, concrete highways, seas, rivers, deserts, and natural forests are examples of land covers.

Fig 12. Supervised Algorithm .Fig 13. LULC classes

Supervised Classification Algorithm


Maximum Likelihood ClassifierLandsat 8 data of December month of year 2013 and 2014 were used with pixel based supervised maximum likelihood algorithm through ERDAS Imagine V. 14 (2014) and ArcMap 10.3.2 (2014) computer software for data image processing and GIS analysis.

Figure 14. Maximum Likelihood classifier.


Result and Discussion

FCC of year 2013.


Land Use and Land Cover.(LULC)

Figure 3.19. Maximum Likelihood

classifier.

Fig 15. FCC of Vaijapur 2013. Fig 16. LULC map of Vaijapur 2013

FCC of year 2014.


Land Use and Land Cover.(LULC)

Figure 3.19. Maximum Likelihood

classifier.

Fig 17. FCC of 2014 Fig 18. LULC of 2014

Land Use and Land Cover (LULC)


Land Use and Land Cover (LULC).In the present study, Landsat 8 satellite image of December month of year 2013 and 2014 were used for Land use and Land cover classification.

0

10000

20000

30000

40000

50000

60000

70000

Settlement Barren Land Water Vegetation Hill WithRocks

HarvestedLand

Misclassified

9530.73

37341.63

751.86

65293.83

20063.34

26220.78

829

6802.2

21817.71

351.63

45309.51

32439.6

52481.52

2976

Are

a H

ect

re

LULC Classes

LULC of Vaijapur Tehsil

Year 2014 Year 2013

Category of Ground TruthClasses TotalUnclassified 0

Vegetation 26Harvested Land 11Settlement 30Barren Land 8Hill with Rocks 9Water body 7Total 91

Fig 19. LULC of Vaijapur Tehsil

Table Ground Truth classes

Land Use and Land Cover


Land Use and Land Cover (LULC).

Settlement4% Barren Land

14%Water

0%

Vegetation28%

Hill With Rocks20%

Harvested Land32%

Misclassified2%

LULC OF VAIJAPUR 2013

Settlement

Barren Land

Water

Vegetation

Hill With Rocks

Harvested Land

Misclassified

Settlement6%

Barren Land23%

Water0%

Vegetation41%

Hill With Rocks13%

Harvested Land16%

Misclassified1%

LULC OF VAIJAPUR 2014

Settlement

Barren Land

Water

Vegetation

Hill With Rocks

Harvested Land

Misclassified

Fig 20. LULC of Vaijapur 2013 Fig 21. LULC of Vaijapur 2014.

Accuracy Assessment of year 2013.

Table 4.4. Producer’s accuracy, Users Accuracy for Maximum Likelihood classifier (In Pixel) of year 2013.


Land Use and Land Cover.(LULC)Error matrix resulting from classifying Training set pixels ML Classifier (Ground Truth)

Classes Vegetation Harvested Land

Settlement Barren Land

Hill with

Rocks

Water Body

Total

Unclassified 0 0 0 0 0 0 0

Vegetation 22 2 0 2 0 0 26

Harvested Land

1 9 0 1 0 0 11

Settlement 0 0 23 4 3 0 30

Barren Land 1 2 0 5 0 0 8

Hill with Rocks 0 0 1 0 8 0 9

Water body 0 0 0 0 0 7 7

Total 24 13 24 12 11 7 91

Commission/Omission of ML classifier 2013

Classes Commission Pixel

Omission Pixel

Vegetation 4/26 2/24

Harvested Land

2/11 4/13

Settlement 7/30 1/24

Barren Land 3/8 7/12

Hill with Rocks

1/9 3/11

Water body 0/7 0/7

Accuracy Assessment of ML Classifier 2013

ClassesProd. Acc

(Pixel)

User Acc

(Pixel)

Prod. Acc

(Percent)

User Acc

(Percent)Overall

accuracy

kappa

coefficient

Vegetation 22/24 22/26 91.67% 84.62%

81.31% 0.811

Harvested Land 9/13 9/11 69.23% 81.82%

Settlement 23/24 23/30 95.83% 76.67%

Barren Land 5/12 5/8 41.67% 62.5%

Hill with Rocks 8/11 8/9 72.73% 88.89%

Water body 7/7 7/7 100% 100%

Table 10. Error Matrix

Table 11. Accuracy assessment of ML

Table 12. Commission/ Omission

Accuracy Assessment of year 2014.



Error Matrix

Error matrix resulting from classifying Training set pixels (Maximum Likelihood classifier) Ground Truth (Pixels) 2014

Classes Vegetation Harvested Land

Settlement Barren Land

Hill with

Rocks

Water Body

Total

Unclassified 0 0 0 0 0 0 0

Vegetation 21 3 0 2 0 0 26

Harvested Land

2 8 0 1 0 0 11

Settlement 0 0 24 2 4 0 30

Barren Land 1 2 0 4 1 0 8

Hill with Rocks

0 1 1 0 7 0 9

Water body 0 0 0 0 0 7 7

Total 24 14 25 9 12 7 91

Commission/Omission for MLC 2014.

Classes Commission Pixel

Omission Pixel

Settlement 6/30 1/25

Barren Land 4/8 5/9

Hill with Rocks 2/9 5/12

Water body 0/7 0/7

Accuracy Assessment of ML Classifier 2014.

ClassesProd.

Acc(Pixel)

User Acc

(Pixel)

Prod.

Acc(Percent)

User Acc

(Percent)Overall

accuracy

kappa

coefficien

t

Vegetation 21/24 21/26 87.05% 80.77%

78.02% 0.773

Harvested Land 8/14 8/11 57.14% 72.73%

Settlement 24/25 24/30 96% 80%

Barren Land 4/9 4/8 44.44% 50%

Hill with Rocks 7/12 7/9 58.33% 77.78%

Water body 7/7 7/7 100% 100%

Table 13. Error Matrix of 2014

Table 14. Accuracy assessment of 2014.

Table 15. Commission/ Omission of 2014.



Agricultural drought analysis of Kharif season of 2013-2014.

Rainfall and Temperature Analysis



Jun Jul Aug Sep

2013 (mm) 45 70 27.9 50

2014(mm) 50 81 70 134

0

20

40

60

80

100

120

140

160

RAINFALL OF KHARIF SEASON 2013-2014

2013 (mm) 2014(mm)

Jun Jul Aug Sep

Mean 2013 22.76 21.93 21.51 21.73

Mean2014 25.1 22.7 21.9 21.43

19

20

21

22

23

24

25

26

TEM

P. C

ELSI

OU

S MONTH

MEAN TEMP OF KHARIF SEASON 2013-2014

Mean 2013 Mean2014

Fig 22. Rainfall of Kharif season 2013 Fig 23. Temperature of Kharif Season

Sown Area Report



0

20000

40000

60000

80000

100000

120000

140000

160000

Area(H) Cropland(H) Sown area(H)

Hec

tre

Land type

Agriculture Sown area report - Vaijapur Tehsil year 2013 and 2014

Kharif 2013

Kharif 2014

Fig 24. Sown area report of Kharif season 2013-2014

Normalised Difference Vegetation Index (NDVI)



Jun Jul Aug Sep

Year 2013 0.12 0.139 0.067 0.092

Year 2014 0.141 0.171 0.182 0.312

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

ND

VI V

ALU

ES

MONTH

NDVI OF KHARIF SEASON 2013-2014

Year 2013 Year 2014

Fig 25. NDVI Values Fig 26. NDVI images

Vegetation Condition Index (VCI)



Jun Jul Aug Sep

Year 2013 40.17 41.21 23.612 37.89

Year 2014 45.964 48.042 37.178 65.2

0102030405060708090

100

VC

I VA

LUES

MONTH

VCI OF KHARIF SEASON 2013-2014

Year 2013 Year 2014

Fig 27. VCI of Kharif season Fig 28. VCI of Kharif Season

Soil Adjusted Vegetation Index (SAVI)



Jun Jul Aug Sep

Year 2013 0.182 0.205 0.1 0.137

Year 2014 0.208 0.251 0.268 0.462

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

SAV

I VA

LUES

MONTH

SAVI OF KHARIF SEASON 2013-2014.

Year 2013 Year 2014

Fig 29. SAVI of Kharif SeasonFig 30. SAVI Images

Temperature Condition Index (TCI)



Jun Jul Aug Sep

Year 2014 19.585 41.995 40.528 40.471

Year 2013 48.073 53.151 15.095 8.325

0

10

20

30

40

50

60

TCI V

ALU

E

MONTH

TCI OF KHARIF SEASON 2013-2014

Year 2014 Year 2013

Fig 31. TCI of Kharif Season Fig 32. TCI of Kharif Season

Drought condition of Kharif season of year 2013-2014.



0

1000

2000

3000

4000

5000

6000

7000

8000

June July August September

6153.35821.83

7639.477323.12

2758.232363.94

2104.02 1850.58

1137.42

1863.18

270.18875.25

Are

a (H

ectr

e)

Month

Drought Condition of Vaijapur tehsil of Kharif season of year 2013

Severe Normal Healthy

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

June July August September

9321.21

8276.31 8265.06

5802.03

571.86 1197.811122.03

1433.25

199.98 574.83 661.86

2813.67Are

a in

Hec

tor

Month

Drought Condition of Vaijapur tehsil of Kharif season of year 2014


Fig 33. Drought Condition of Kharif season 2013 Fig 34. Drought Condition of Kharif season 2014



Agricultural drought analysis of Post Monsoon season of 2013-2014.

Rainfall & Temperature of Post Monsoon season of year 2013-2014.



Oct Nov Dec

2013 (mm) 95.6 2.4 60

2014(mm) 12 21.4 4.2

0

20

40

60

80

100

120

Rai

nfa

ll in

Mill

imet

er(m

m)

Month

Rainfall of Post monsoon season 2013-2014

2013 (mm) 2014(mm)

Oct Nov Dec

Mean 2013 20.9 15.73 13.03

Mean2014 20.29 17.43 12.75

0

5

10

15

20

25

TEM

P (

CEL

CIO

US)

MONTH

Temperature of post monsoon season 2013-2014

Mean 2013 Mean2014

Fig 35. Rainfall of Post Monsoon Season 2013-2014.

Fig 36. Temperature of Post Monsoon Season 2013-2014.

Sown area of Post monsoon season 2013-2014.



0

20000

40000

60000

80000

100000

120000

140000

160000

Area(H) Cropland(H) Sown area(H)

159390

129193.6

23801

159390

129193

23401

Hec

tre

Area

Vaijapur Sown area of Rabbi Season 2013 & 2014

Rabbi 2013 Rabbi 2014

Fig 37. Sown Area of Post Monsoon Season 2013-2014.

NDVI of Post Monsoon season of year 2013-2014.



Oct Nov Dec

Year 2013 -0.047 0.446 -0.143

Year 2014 0.613 0.496 0.479

-0.2

-0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

ND

VI V

alu

e

Month

NDVI Of Post Monsoon Season Of Year 2013-2014

Year 2013 Year 2014

Fig 38. NDVI of Post Monsoon Season 2013-2014.

Fig 39. NDVI images of Post Monsoon Season 2013-2014.

VCI of Post Monsoon season of year 2013-2014.



Oct Nov Dec

Year 2013 22.778 96.954 6.229

Year 2014 93.768 73.986 72.045

0

10

20

30

40

50

60

70

80

90

100

VC

I Val

ue

Month

VCI of post monsson season year 2013-2014

Year 2013 Year 2014

Fig 40. VCI of Post Monsoon Season 2013-2014. Fig 41. VCI images of Post Monsoon Season 2013-2014.

SAVI of Post Monsoon season of year 2013-2014.



Oct Nov Dec

Year 2013 -0.069 0.659 -0.212

Year 2014 0.905 0.733 0.705

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

SAV

I Val

ues

Month

SAVI of post monsoon season 2013-2014

Year 2013 Year 2014

Fig 42. SAVI values of Post Monsoon Season 2013-2014. Fig 43. SAVI images of Post Monsoon Season

2013-2014.

Drought condition of Post Monsoon season of year 2013-2014.



0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

October November December

9557.64

90.63

9891.4499

419.13

255.6131.85

10.71

9680.49

5.31

Are

a(H

ectr

e)

Month

Drought Condition of Vaijapur tehsil of post monsoon season of year 2013


0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

October November December

193.59

1054.621732.68

548.46

3648.42 3414.51

9306.9

5345.914901.76

Are

a H

ectr

e

Month

Drought Condition of Vaijapur tehsil of post monsoon season of year 2014


Fig 44. Drought Condition of Post Monsoon Season 2013.

Fig 45. Drought Condition of Post Monsoon Season 2014.

Conclusion



• The advantage of Landsat 8 data is that, it has 30m resolution, which is important for analysis of small areas.

• This research is helpful for government agencies and government disaster planning bodies to create a drought mitigation plan.

• It is observed that, the farmer has shown twice due to failure of crop and late arrival of monsoon rain. Sown area was lowered by 11227.3H due to insufficient rainfall in Kharif season of year 2013. The sown area in Rabi season was decreased by 400H compared to 2013.

• The NDVI-rainfall correlation was found to be highly influenced by mean rainfall conditions and vegetation types. It is therefore concluded that temporal variations of NDVI are closely linked with precipitation. The NDVI index shows that vegetation condition can be used as an indicator for drought condition of an area.

• The NDVI and VCI are closely related to each other.

• The value of SAVI was less than 0.5 throughout the Kharif season and the post monsoon season, i.e. show that, the soil moisture was not enough to maintain crop health in the study area.

Conclusion..



• The value of TCI of the month July, August, September of 2013 was lower than 2014, which is indicating that year 2013 was affected by extreme drought.

• It is observed that, rainfall throughout the Kharif season was below the average, so that it is responsible to generate drought condition in the study area.

• It is also recommended that, 2 year data of meteorological index is not enough to calculate the meteorological index because it required historical data about 30-50 years of time scale of 1 month and 2 months, which should be considered to identify meteorological drought risk.

• The vegetation indices have indicated that the year 2013 was affected by extreme drought than 2014.

• Hence it is proved that Landsat 8 OLI and TIRS sensor data can be used for drought severity identification and classification.

Future Work


In this research study, we have faced some problem with availability of ancillary data and meteorological data. Still, some of the portion of the study like hydrological drought and meteorological drought analysis could not be handled and can be taken up in the further research study.

The analysis of drought risk estimation of every farm can be done by using very high resolution images.

The integration of geospatial technology in drought monitoring and decision support system can deliver better result of drought assessment in agricultural sector.

This is research would be helpful to Town Planner, Municipal Corporation, Agriculture

Department, Rural Development Department, Govt. Departments, NGO’s, etc.

Acknowledgement


• I would like to begin by saying how greatly indebted I am to my guide Prof. K. V. Kale, Professor and BCUD, Dr. Babasaheb Ambedkar Marathwada University Aurangabad, who taught me Remote Sensing and encourage me to do research in this domain.

• It is my profound privilege to acknowledge gratitude to Prof. R.R. Deshmukh, Head of Department of Computer Science and Information Technology, Dr. Babasaheb Marathwada University, Aurangabad for his constant encouragement throughout the course of investigation.

• I especially thankful to Dr. S. C. Mehrotra, Srinivasa Ramanujan Geospatial Chair, Prof. S. N. Deshmukh, Prof. B. W. Gawali, Dr. Ramesh R. Manza, Dr. C. N. Mahender, Dr. M. G. Dhopeshwarkar, Dr. Seema Kawathekar, Dr. S. B. Kulkarni, Mrs. M. R. Baheti, Dr. Sunil Nimbhore, Mrs. Vaishali Ingle for their encouragement and academic guidance.

• I am very thankful to my colleagues, research fellow, students, All the peoples who is associated with department.

• I would like to acknowledge and thanks to University Grants Commission (UGC), India for granting UGC SAP (II) DRS Phase-I & Phase-II F. No. 3-42/2009 & 4-15/2015/DRS-II for Laboratory facility to Department of Computer Science and Information Technology

• I am very thankful to Panchyat Samiti, Vaijapur Tehsil office and the Agricultural office for providing meteorological and sown area data. I am also thankful to USGS for providing all the satellite images requested on their Timeline.

Am I research endeavor ?


Some interesting Problem I faced in my research.

1. Selection of Dataset. LISS II Vs Landsat 7 Vs Landsat 8 ?2. Landsat 7 ETM+ has caused SLC problem. My Village was lost in the tile.3. Landsat 8 started its data collection in May -2013.4. The data collection from govt offices is very time consuming task.5. Inconsistent meteorological data in Tehsil office. 6. The work without Atmospheric correction give you better accuracy. Is it true?7. Research deleted.8. Start from the Scratch. 9. Need of Ground truth.10. Unviability of tool for Landsat 8 preprocessing operation. 11. Divide by zero error. 12. K1, K2, Values of temperature coefficient problem. Waited for NASA’s Space Shuttle

program. 13. Why TCI is not calculated of the Post monsoon season of 2014.14. Data lost due to Dense cloud cover or some natural phenomena. 15. Work done successfully in November 2015.

Sandeep Gaikwad

References :

• Research Paper• Websites• Books

5517 April 2016

References


[1] M.C. Sashikkumar and O. Ganesh Babu. (2013). Assessment of Meteorological Drought for Chittar Sub-basin Using Geographical Information System. International Journal of Advanced Remote Sensing and GIS, 2:1, 271 -279, Article ID Tech-141 ISSN 2320 - 0243.

[2] Surendra Singh Chaoudhary, P. K. Garg, S. K. Ghosh. (2012). Mapping of agriculture drought using Remote Sensing and GIS. International Journal of Scientific Engineering and Technology, 1:4, 149-157.

[3] Caccamo, G., Chisholm, L. A., Bradstock, R. A., & Puotinen, M. L. (2011). Assessing the sensitivity of MODIS to monitor drought in high biomass ecosystems. Remote Sensing of Environment, 115, 2626–2639.

[4] C. Bhuiyan. (2008). Desert Vegetation During Droughts: Response And Sensitivity. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B8. Beijing.

[5] C. S. Murthy, M. V. R. Sesha Sai, V. Bhanuja Kumari & P. S. Roy. (2007). Agricultural drought assessment at disaggregated level using AWiFS/WiFS data of Indian Remote Sensing satellites. Geocarto International, 22:2, 127-140, DOI: 10.1080/10106040701205039

[6] Dalezios, N. R., Bampzelis D., and Domenikiotis, C. (2009). An integrated methodological procedure for alternative drought mitigation in Greece. European Water, 27:28, 53–73.

[7] D. Nithya and R. S. Suja Rose (2014). Assessing Agricultural Vulnerability Using Geomatic Technology: A Case Study of Srivilliputhur Taluk of Virudhunagar District, Tamil Nadu. International Journal of Advancement in Remote Sensing, GIS and Geography, 2:2, 11-17.

[8] Diego Renza, Estíbaliz Martinez, Agueda Arquero, and Javier Sanchez. (2010). Drought Estimation Maps by Means of Multidate Landsat Fused Images. Remote Sensing for Science, Education, Rainer Reuter (Editor) and Natural and Cultural Heritage EARSeL.

References ..


[9] Hasan Murad and A. K. M. Saiful Islam. (2011). Drought Assessment Using Remote Sensing And GIS In North-West Region Of Bangladesh. 3rd International Conference on Water & Flood Management (ICWFM-2011).

[10] K. Prathumchai, Kiyoshi Honda, Kaew Nualchawee. (2001). Drought Risk Evaluation Using Remote Sensing and GIS: A Case Study in LOP Buri Province. Paper presented at the 22nd Asian Conference on Remote Sensing, 5 - 9 November 2001, Singapore.

[11] Kundu Arnab, Dutta Dipanwita, (2011). Monitoring desertification risk through climate change and human interference using Remote sensing and GIS techniques. International Journal of Geomatics And Geosciences, 2:1, 21-33.

[12] Kogan, F.N. (1990). Remote sensing of weather impacts on vegetation in non-homogeneous areas. International Journal of Remote Sensing, 11:8, 1405-1419.

[13] Kömüşcü AÜ, Erkan A, Turgut E. (2002). The geographic analysis of formation of drouhtness in Turkey used Standardized Precipitation Index Method. Edition of Turkish State Meteorological Service, Ankara, Turkey

[14] M. Ebrahimi, A. A. Matkan, R. Darvishzadeh. (2010). Remote sensing for drought assessment in arid regions ( A case study of central part of Iran,”Shirkooh-yazd”)”. Wagner W., Székely, B. (eds.): ISPRS TC VII Symposium – 100 Years ISPRS, Vienna, Austria, IAPRS, Vol. 38, Part 7B.

[15] M. Erol Keskin, Özlem Terzi2, E. Dilek Taylan and Derya Küçükyaman. (2011). Meteorological drought analysis using artificial neural networks. Scientific Research and Essays. 6:21, 4469-4477, DOI: 10.5897/SRE10.1022

[16]

Publication


1. Sandeep V. Gaikwad, K. V. Kale, Sonali B. Kulkarni, Amarsinh B. Varpe and Ganesh N. Pathare., "Agricultural Drought Severity Assessment using Remotely Sensed Data: A Review" ,International Journal of Advanced Remote Sensing and GISVolume 4, Issue 1, pp. 1195-1203, April-2015, Article ID Tech-440 ISSN 2320-0243.

2. Sandeep V. Gaikwad, Kale K.V., Rajesh K. Dhumal and Amol D. Vibhute, "Analysis of TCI Index Using Landsat8 TIRS Sensor Data of Vaijapur Region", International Journal of Computer Sciences and Engineering, Volume-03, Issue-08, Page No (59-63), Aug -2015, E-ISSN: 2347-2693.

3. Sandeep V. Gaikwad, Kale K. V., Agricultural Drought Assessment of Post Monsoon Season Of Vaijapur Taluka Using Landsat8, International Journal of Research in Engineering and Technology, Volume: 04 Issue: 04, pp: 405-412, August-2015, eISSN: 2319-1163 | pISSN: 2321-7308.

4. Amarsinh Varpe, Yogesh Rajendra, Amol Vibhute, Sandeep V. Gaikwad, and k. V. Kale., Identification of Plant Species using Non-Imaging Hyperspectral Data, International Conference on Man and Machine Interfacing(MAMI 2015). (Accepted)

5. Ajay D. Nagne, Rajesh K. Dhumal, Amol D. Vibhute, Yogesh D. Rajendra, Sandeep V. Gaikwad, K. V. Kale, S. C. Mehrotra, Performance Evaluation of Urban Areas Land Use classification from Hyperspectral Data by Using Mahalanobis classifier, IEEE 3rd International Conference on Electronics and Communication Systems (ICECS) – 25th 26th of February 2016, Coimbatore, Tamilnadu, India. Accepted.

Sandeep Gaikwad

References : Websites

1]http://aurangabad.nic.in/newsite/LANDACQUISITION.htm2]http://www.mrsac.gov.in/en/projects/agriculture-and-land-resources3]http://www.portal.gsi.gov.in/portal/page?_pageid=108,1011820&_dad=portal&_schema=PORTAL 4]http://www.mrsac.gov.in/en/projects/high-resolution-data-base-mapping/national-urban-information-system-nuis5] http://www.aurangabad.nic.in/newsite/history.htm

6]http://earthobservatory.nasa.gov/Features/change_cover_3.php7]http://geography.tamu.edu/class/aklein/geog361/lecture_notes/lecture11_dip.pdf8]http://www.satimagingcorp.com/applications/natural-resources/8] http://bhuvan.nrsc.gov.in

5917 April 2016

http://earthobservatory.nasa.gov/Features/MeasuringVegetation/measuring_vegetation_3.php






http://geography.tamu.edu/class/aklein/geog361/lecture_notes/lecture11_dip.pdf

http://www.satimagingcorp.com/applications/natural-resources/agriculture/agriculture-agrowatch-green-vegetation-index-es/

Sandeep Gaikwad

References & Help ! ……

6017 April 2016

Books1) Rafael C. Gonzalez, Richard E. Woods.

Digital Image Processing 3rd edition. Pearson pub.2) Emilio Chuvieco, Alfredo Huete. Fundamentals of satellite remote sensing. CRC press.3) Heywood, Introduction to GeographicsInformation System .

Sandeep Gaikwad

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6117 April 2016

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