CERTIFICATE

The present study on “ CROP ACREAGE ESTIMATION OF BULANDSHAHR” is carried out by me, which is based on secondary data. To the best of my knowledge the study is original.

DATE:25/06/2015 SABBAH NAWAZ

PGDRS II SEMESTER

Department of Geography

JamiaMilliaIslamia

CERTIFICATE

This is to certify , that the project work entitled “CROP ACREAGE ESTIMATION OF BULANDSHAHR” submitted by SABBAH NAWAZ ,student of P.G Diploma in Remote Sensing and G.I.S Application, Department of Geography, JMI, New Delhi, is a bonafide record of the project work carried out by him, under my supervision.

Dated: 25/06/2015 (Prof. Mohd.Mazhar Ali Khan)

Department Of Geography

Jamia Millia Islamia

ACKNOWLEDGEMENT

As a token of gratitude, I put on records, my thanks toProfessor Mohd.Mazhar Ali Khan, Department of Geography, JamiaMilliaIslamia, under whose supervision,guidance and encouragement, I could complete this work to my satisfaction.

Thanks are also due to other faculty members of the Department Of Geography(J.M.I) for their great cooperation. Sincere thanks are due to my friends, who provided a happy environment for the writing of this project work.

It would not be out of place to express our thanks to the non-teaching staff of the department who extended all possible help on time.

Last but not the least , I acknowledge my parents for their love and constant encouragement, without which this work could not be translated into reality.

Dated: 25/06/2015 (Sabbah Nawaz )

P.G.D.R.S (II-semeter)

Department of Geography

JamiaMilliaIslamia

LIST OF CONTENTS

LIST OF FIGURES

LIST OF TABLES

INTRODUCTION

Several techniques have been adapted for cropestimationusing aerial photographs and satellite images,including: pixel count [Bauer et al., 1978; Fang, 1998;Shao et al., 2001; Sridhar et al., 1994], supervisedclassification [Gallego and Rueda, 1993], Bayesian/fuzzyclassification and spectral un-mixing [Gorte and Stein,1998; Quarmby et al., 1992], and area frame sampling[Gallego, 1999; Pradhan, 2001]. Pixel count is dependenton classification accuracy, which is limited by the presenceof mixed pixels, colocation inaccuracy, and the spatialcorrelation between trainingsites and test sites. Furthermore,classification techniques and spectral un-mixing arehighly sensitive to the variance among categories andcorrelation betweenspectral bands. The presence of mixedpixels induces significant errors, particularly in developingcountries, where small subsistence farms adjoin uncultivatedareas [Ozdogan and Woodcock, 2006].The area frame sampling approach has been appliedin a number of ways in agricultural surveys. The generalprinciple involves a multistage sampling approach wherethe scale of segments, or sampling units,at each stage isdifferent from other stages. Statistical relationships betweenthe high and medium resolution data are then used toestimate the CA for the largest unit practical Crop acreagesare crucial for crop production forecasting or estimation besidescrop yield. Thus it isessential to know the crop acreage timelyRemote sensing based sampling methods have been showngood solutions for large area crop acreage

monitoring systems (Macdonald and Hall, 1980; Cecil andCharles, 1984; Taylor et al.,1997; Tsiligirides, 1998; Sushil, 2001; Wu and Li, 2004; Gallegoand Bamps, 2008; NASS of the USDA, 2009b).

Throughout the years there have been many approaches to crop area estimation. Agricultural statistics are collected both by censuses, which require enumeration of the total population of interest, and by samples requiring enumeration of only a small part of the population

Crop production forecasts/estimates are generally portrayed as the product of two components: area (to be) harvested and (expected) yield per unit area. The accurate forecasting/estimation of both harvested area and yield are equally important in ensuring the accurate determination of their product. Although the yield part of the equation gets most of the attention, there are many complexities to the estimation of area that might not be readily apparent

Since the 1970s and 1980s, application of remote sensing technique in crop acreage estimation has becoming increasingly dominant. Crop acreage estimationusing remote sensing provides timely and reliable information. Efficient images processing and classification provide the so called crop area estimates by remote sensing. Nowadays, methodologies combining ground and earth observation data are commonly adopted. Research has been conducted to use

the medium resolution satellite data to assess the cropacreage early in the growth season. These coarse resolution sensors have the advantage of low costs, high availability to avoid the cloud disturbance. It isexpected that this combined approach will retrieve the best area estimate.

India underwent a series of successful agricultural revolutions, starting with the green revolution in wheat and rice in the 1970’s, the white revolution in milk and, and in the 1980’s, the yellow revolution in oil seeds. Despite these major transformations, the agricultural sectors continues to be dominated by a large number of small landholders (70% of therural people and 8% of the urban household depend on agriculture). The country is also marked by large fluctuations in agricultural output, though to a declining extent with the development of irrigational facilities, adoption to new technologies and changes incropping pattern. The traditional approach of crop estimation in India involves complete enumeration for estimating crop acreage and sample survey based on cropcutting experiments for estimating crop yield. The cropacreage and corresponding yield estimate data are used to obtain production estimates.

The information on crop area statistics is backbone of Agricultural statisticalsystem. Reliable and timely information on crop area isof great importance toplanners and policy makers for efficient and timely agricultural development andmaking important decisions with respect to procurement,storage, public

distribution, export, import and other related issues. India possesses anexcellent administrative setup, which has long standingtradition of generatingquality information. Most parts of the country are having detailed cadastral surveymaps, frequently updated land records and institutions like permanent villagereporting agency for providing reliable and continuous data on crop area.However with more emphasis on local area planning, there is further need forCrop area with respect to different varieties grown in the area, irrigationavailability, the soil type etc. which can go a long way in rapid development of theregion.

With the advent of Remote Sensing Technology during 1970’s, its great potential in the field of agriculturehave opened new vistas of improving agricultural statistics system all over the world. Space borne remotely sensed spectral satellite data has been widelyused in the field of agriculture for estimation of areaunder different major crops like wheat, paddy, groundnut and sugarcane. It is accepted that the satellite remote sensing technology is used world wide for speedy resource assessment and monitoring, predominantly in the developing world. NASA of USA has made most considerable contribution wit satellite based remote sensing. Remote sensing technology and its application has undergone a remarkable change in terms of sensor development since 1972, when Landsat-1 was launched.

Aerial flights with enhanced sensors, satellite design development and operations including data reception, processing, interpretation and utilization of satelliteimages is overly use with its advancement. Such development have widened the usage of remotely sensed data in various areas viz. forest cover, vegetation type mapping and their changes on a regional scale. This sophisticated advancement have indicated that if satellite data is judiciously used alongside sufficientground data, it is feasible to carry out meticulous forest inventories and monitoring of land use and vegetation cover at diverse scale.

LITERATURE REVIEW

1.W. Bingfang, et.al(2003) studied “CROP AREA ESTIMATION USING REMOTE SENSING ON TWO-STAGE STRATIFIEDSAMPLING” In their paper ,they analysed problems that remote sensing technique met in China and provided a new methodology on stratified sampling technique. According to them during the stratification procedure, physical factors, such as temperature, precipitation, soil type, sun eradiation were taken into consideration. And then, the methods used by them to estimate crop proportion were, cluster sampling, transect sampling and GVG assisted by remotely sensedimages. At last, they calculated the crop area on 1:100,000 scale.

2.G. J. Husak et.al(2008) studied “Crop area estimationusing high and medium resolution satellite imagery inareas with complex topography” according to themsatellite interpretation of crop area is an effectivealternative to extensive and costly field surveys,which fail to represent the spatial heterogeneity atthe country-level. In their paper,they demonstrated ahybrid high-medium resolution technique for CentralEthiopia that combines spatially limited unbiasedestimates from IKONOS images, with spatially extensiveLandsat ETM+ interpretations, land-cover, and SRTM-based topography. They used logistic regression toderive the probability of a location being crop. Theirdistrict-level analysis of Landsat based estimatesshowed crop area totals. They suggested segmentation

algorithms will be valuated, in order to automateclassification of medium resolution imagery for routinecrop area estimation in the future.

3. C. M Beltran et.al (2001) studied “Irrigated crop area estimation using Landsat imagery in La Mancha, Spain” they presentedmethodology for supervised classification to identify irrigated crops with LandsatTM imagery in a semiarid zone (La Mancha, Spain]in which the discrimination procedure was based on the diffrent crop spectral responses . Their multi temporalsupervised classification included maximum likelihood algorithms, decision-tree criteria, and context classifiers. They estimated the areas occupied by each crop class by means of intersecting the TM-derived land-use raster map and the digital rural cadastre vector map in a geographic information system. They assessed the accuracy ofthe crop area estimation by comparing these areas with those calculated from the digital rural cadastre. At last they applied median filter to the final classification that improved the estimated crop areas with the cadastre data

4. G. Alonso et.al(1997)studied “Remote Sensing and Agricultural Statistics: crop area estimation in north eastern spain through diachronic Landsat TM and ground sample data”. In their study they used Landsat ThematicMapper(TM) imagery to estimate crop area through the regression method. According to them if the rate of land cover change is not great, the use of an image of the year previous to the ground survey is a way

reducing the cost of the image acquisition without any loss in the accuracy of area estimation of crops.

5.L .A Sawasawa (2003) studied “Crop Yield Estimation: Integretating RS, GIS, Management and Land Factors” According to him early prediction of crop yield is important for planning. His study applied space borne satellite based NDVI to predict crop yield. The purposeof this study was to investigate the relation between space borne satellite based NDVI and rice yield in irrigated fields and combining NDVI with management andland factor for yield prediction. Data was collected through interviewing the farmers. Step wise linear regression was used to relate yield to management land factors and the NDVI to derive a yield model. The results showed a significant correlation between RemoteSensing NDVI and field level rice yield.

6. A. Gusso et.al (2012) studied “SOYBEAN CROP AREA ESTIMATION AND MAPPING IN MATO GROSSO STATE, BRAZIL” inthis paper they evaluated the MODIS Crop Detection Algorithm (MCDA) procedure for estimating soybean crop areas. MCDA was based on temporal profiles of EVI (Enhanced Vegetation Index) derived from satellite dataof the MODIS (Moderate Resolution Imaging Spectroradiometer) imager. On average, the MCDA resultsexplained some of the variation of the data estimated by the IBGE Brazilian Institute of Geography and Statistics. MCDA calibration was able to provide annualthematic soybean maps, forecasting the planted area in the State.

7.B.Wu et.al (2012)studied “Crop planting and type proportion method for crop acreage estimation of complex agricultural landscapes” Their study presents acrop planting and type proportion method for crop acreage estimation of complex and diverse agricultural landscapes. Validation was been done with in situ data for different agricultural landscapes over China. CropPlanting Proportion estimation with remote sensing dataand Crop Type Proportion were estimated through ground survey resulting in high accuracy. According to them The Crop Planting and Type Proportion method thus has ahigh accuracy, yields timely information at low costs, and provides objective results. Their study concludes that the Crop Proportion Type Proportion method can be used for large area crop acreage estimation of complex agriculture landscapes.

8.H. MAHYOU, et.al (2014) studied “Crop Area Estimation” according to them, cereals (bread wheat, durum wheat and barley) constitute the basis for food security. Area of the three main cereals in Morocco were estimated based on two methods: Area frame sampling and remote sensing methods. Supervised classification was undertaken based on inexpensive h resolution images of 30 meters spatial resolution (Landsat TM5 and ETM+7) and using an available detailedcroplands mask provided by The Direction of Strategy and Statistics (DSS) of the Ministry of Agriculture. Inorder to improve the accuracy of cereals, they introduced the decision process tree classifier that resulted from supervised classification and the land cover classes developed in the stratification method.

Results show that the methodology based on low resolution images and a detailed crop mask was able to provide accurate estimates of cereals area.

9. S.Goswami et.al (2012) in their article “Remote Sensing and GIS based wheat crop acreage estimation of Indore district, M.P” .Their paper highlights on application of Remote Sensing (RS) and Geographical Information System (GIS) technologies for the wheat acreage estimation. Single date, cloud free Resourcesat -1 LISS- III digital data coinciding with flowering stage of wheat crop was used for acreage estimation. ERDAS IMAGINE 9.1 image analysis software and Arc GIS 9.2 software were used for data processing and analysis. Remote sensing &GIS technologies based district level wheat acreage estimation has been done and found that deviated from the land record.

10.G. Mythili (2008) in her article “Acreage and Yield Response for Major Crops in the Pre- and Post- Reform Periods in India: A Dynamic Panel Data Approach” .Estimates supply response for major crops during the pre- and post-reform periodsusing the Nerlovian adjustment cum adaptive expectationmodel. Her estimation was based on dynamic panel data technique using pooled cross-sectional time-series dataacross the states of India. According to her study foodgrains reveal less response than nonfood grains. Yield response was higher than acreage response for the main cereals, rice and wheat. Treating yield variable as a proxy for nonacreage inputs, the results confirmed that farmers respond to price incentives increasingly

by adjusting non-acreage inputs than acreage for main cereal crops.

11. James.W et.al (1981) studied “Crop Area Estimation using Ground gathered and sampled landsat data”, that described a comparative study in which a procedure using ground gathered data and classifiesd LANDSAT datafor estimating crop area was compared to ground gathered data and sampled Landsat data. They used unitemporal data and multitemporal data for the study. Their estimated results indicated that some sampling schemes can be used without any significant difference in the crop area estimates.

12. M.Premalatha et.al(1994) studied “Crop Area Estimation using ERS -1 SAR data” the purpose of their study was to estimate crop area of the kharifseason.Synthetic Aperture Radar data acquired from European Remote Sensing satellite were used to identify the major crops. Separability indices were calculated for major cover types after estimating the results they concluded single date SAR data cannot be used for accurate identification of various crops. According to them multitemporalcolour composite facilitated better identification of crop types.

STUDY AREA

Bulandshahris a city and a municipal board in bulandshahr district in the state of Uttar Pradesh, India. It is the administrative headquarters of Bulandshahr District located between Ganges and Yamuna rivers. According to government of India , the districtBulandshahr is one of the Minority Concentrated District in India on the basis of the 2001 census data on population, socio-economic indicators and basic amenities indicators. The official language is Hindi. The district has many important towns such as Sikandarabad, Dibai, Sikarpur, Siyana, Bugrasi, Anoopshahr, Jahangirabad, Khurja, and Bulandshahr. The Ganges river separates the district from JyotibaPhule Nagar District and Badaun District to the east. The district is bounded on the south by Aligarh district, on the west by GautamBudh Nagar, on the south west by Khair Tehsil and on the north by Ghaziabad district.

The geographical location of Bulandshahr district is 28.4degree North and 77.85 degree East. The total area of the district is 4,441sq km with an elevation of 237.44m above sea level.

The population of Bulandshahr district according to the2011 census is 222,826 and the population density is

788/sq km. The sex ratio is 892 females/1000male. As per provisional data of 2011 census,Bulandshahr urban agglomeration had a population of 235,310, out of whichmales were 125,549 and females were 111,761 this give it a ranking of 85th in India out of 640 districts. The literacy rate was 78.37%.

The district of Bulandshahr is an important grain producing agricultural district. Potato, wheat, maize and sugar are cultivated here in large quantity. Along with the green revolution the district is also a part of the white revolution in theform of milk production. Potteries of khurja a town in Bulandshahr have earned good name everywhere in the world.

AIMS AND OBJECTIVES

1. To find out the crop acreage of the Bulandshahr district using remote sensing data.

2. To evaluate the change in crop area.

DATABASE ANDMETHODOLOGYData Source

The satellite data used in the research includes a Landsat Thematic Mapper (TM image) image of 8th October 2000 and 21st September 2014. Radiometriccorrection was employed which is necessary for detecting changes of surface reflectance, using a relative radiometric correction method. The images of two dates were rectified by taking ground control points and projected to a common UTM coordinate system of WGS 84. Image enhancement techniques such as contrast stretching and ratio images were worked out toimprove the visual interpretability of the image. A ratioed image effectively compensates forthe brightness variation caused by varying topography and emphasized the color content of the data.

Method

Supervise Classification :Land use classes are mapped from remotely sensed data through digital image classification and interpretation. The image classification procedure is to automatically categorize all pixels in an image intodifferent classes of land use(Lillesandet al., 2004).The remote sensing data had varied spectral response due tothe effect of elevation, slope and its aspect. The spectral signature of same objects is often different or vice versa. In such conditions the combined use of supervised and unsupervised classification methods are of great help for proper classification. Therefore in my study of Crop Acreage Estimation I have used the

method of supervised classification for creation land use map of the study area(Bulandshahr). A supervised approach involves the selection of trainingareas (in pixels) on the image which statistically characterize land use classes, therefore I have createda level-1 land use map of Bulandshahr district of both the years 2000 and 2014. I have used the software ERDASImaging version 9.3 for supervise classification, this software provides user friendly interface which made the classification very easy and quick. The classes that I have created are Crop, Natural Vegetation, WaterBodies, Built Up area, and Open Land. Each class is created by taking training sets very minutely and then merging the training sets in the signature editor file.After making all these classes I have calculated the accuracy of these training sets from the contingency option. The accuracy table shows the different level ofaccuracy of all the training sets.

LAND USE ACCURACY ASSESSMENT TABLE: 8october 2000Land UseClass

Crop

Water Bodies

Built Up

NaturalVegetation

Open Land

Row Total

User Accuracy

Crop

203 0 0 69 0 272 74.60%

Water Bodies

0 313 0 0 0 313 100%

Built Up

0 0 1352 0 25 1377 98.10%

Natural 1 1 0 2764 0 2766 99%

Vegetation Open Land

0 1 17 1 908 927 97.90%

Column Total

204 315 1369 2834 933 5655

ProducerAccuracy

99.50%

99.30% 98.70% 97.50% 97.30%

Overall Accuracy

97.90%

Table No-1

LAND USE ACCURACY ASSESSMENT TABLE: 21september 2014Land Use Class

Crops

Water Bodies

Built Up

Natural Vegetation

Open Land

Row Total

User Accuracy

Crops

861 0 0 1 0 862 99.8

Water Bodies

0 408 0 2 0 410 99.5

Built Up

0 0 1155 0 0 1155 100

Natural Vegetation

7 3 0 260 0 270 96.2

Open Land

0 0 0 0 88 88 100

Column Total

868 411 1155 263 88 2785

ProducerAccuracy

99.1 99.2 100 98.8 100

Overall Accuracy

99.5

Table No- 2

Accuracy Assesment :

The overall accuracy of the year 2000 image is 97.9% and of 2014 is 99.5% this means that the accuracy levelis very fine and reliable. As for the individual classes like Crop the producer and user accuracy are 99.5% and 74.6% respectively for the year 2000 and for

the year 2014 it is 99.1% and 99.8% respectively, for the class Water Bodies the producer and user accuracy of the year 2000 and 2014 are 99.30% and 100% and 99.2%and 99.5% respectively, for the class Built Up the accuracy level are 98.70% and 98.10%(2000) and 100% forboth producer and user accuracy in 2014, for Natural Vegetation the producer and user accuracy are 97.50% and 99% in 2000 and in 2014 it is 98.8% and 96.2% respectively, for Open Land it is 97.30% producer accuracy and 97.9% user accuracy in 2000 and in 2014 itis 100% for both the accuracy. In all the classes the level of accuracy is quite high which gives us an idea that the training sets selected are correct and hence the land use map can be made with these classes, there after I made the Land Use Land Cover map with these signature file. Now to get the area of all the classes we have to go to raster option of the supervised image in Erdas Imagine then open attribute and the go to editoption and then add area column, this gives the area ofthe land use classes, from this column extract the croparea which is our objective of this study. From the area column in the attribute table of the supervised image we get the remote sensing or estimated crop acreage.

Land Use Area Table:

Land Use/LandCover Classes

2014 2000

Area(hectare)

% Area(hectare)

%

Built Up 57390.3 16.90 36794.07 9.9

Open Land 48348.81 14.3 40076.01 10.7

NaturalVegetation

33420.15 9.88 91450.35 24.6

Crop 230076 68 201365.55 54.22

Water Bodies 2117.97 0.62 1667.25 0.44

Table No- 3

In table no-3 we can see the the area cover of all the classe and their percentage cover in both the years, With the following table we can estimate Crop Acreage, this is how we estimate crop acreage using remote sensing. The Crop Acreage Estimation procedure broadly consist of (i) Complete Enumeration and (ii) Sample Segment Approach. For this study the supervised classification is used to Crop Acreage Estimation.

The Relative Deviation method is applied to calculate the variations of actual area and estimated area of Crop. The equation is

Xi-X/Xi x 100

Where,

Xi is the actual area

X is the estimated area

Here actual area ‘Xi’ is 231141 hectares and the estimated area ‘X’ is 201365.55for the year 2000 and ‘Xi’ is 263736 hectares and ‘X’ is 230076 hectares in the year 2014, hence putting it into the formula we getthe relative deviation for the Crop in 2000 is 12.8 andin 2014 it is 12.7.

Normalised Differential Vegetation Index(NDVI)

For agricultural land use mapping in digital image processing the maximum crop growing period is taken to study. The identification of crops are done by the helpof vegetation index model. These model are acceptable when image are taken of maximum growth stage.

For this study images of two different dates that is 8th

October 2000 and 21st September 2014 is taken, the imaging months are September and October when Kharif crops are grown maximum. Following are the models and technique of agricultural land use mapping.

The normalized differential vegetation index model is applied in this study where two spectral bands are usedfrom Landsat TM data i.e Red band(band 3) and NIR band (band 4). The equation is as follows :

NIR – RED/ NIR + RED

After this the values are stretched by the following equation

DN(in) – DN(min)/ DN(max) –DN(min) X 255

The Density Slicing technique is used on the NDVI imagefor differencing the different land use classes on the basis of Digital Numbers(DN) or Brightness Value of theimage. The higher brightness (DNmax) values are taken as Crops due to the maximum growth period. Moderate aretaken as other Vehetation and lower is taken for Non- Vegetaion cover.

CONCLUSION