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Use of Remote Sensing and GIS Technology in agricultural surveys
*Randhir Singh, Prachi Misra Sahoo, Anil Rai
Indian Agricultural Statistics Research Institute (ICAR)
Library Avenue, New Delhi-110012
Fax: 011-2578 0564
The crop production of principal agricultural crops in the country is usually estimated as a product of area under the crop and the average yield per unit area of the crop. The estimates of the crop acreage at a district level are obtained through complete enumeration, whereas, the average yield is estimated through General Crop Estimation Surveys (GCES), on the basis of crop cutting experiments conducted on a number of randomly selected fields in sampled villages of the district. However, the traditional system of estimation of crop production is facing several problems, viz. lack of timely information, reliability of records maintained by the patwaries due to heavy burden of their work etc.
Advent of remote sensing technology and its great potential in the field of agriculture have opened newer possibilities of improving agricultural statistic system as it offers accelerated, repetitive and spatial – temporal synoptic view in different windows of the electromagnetic spectrum from its vantage point in space. In the last few years, remote sensing technology has been increasingly considered for evolving an objective, standardized and possibly cheaper and faster methodology for crop production estimation (Bauman, 1992). The acreage estimation procedure using remote sensing technique broadly consists of identifying representative sites of various crops (called training sites) on the image based on the ground truth collected, generation of signatures for different training sites and classifying the image using these training statistics. Depending upon the study area, broadly two procedures namely, (i) sample segment approach and (ii) administrative-boundary-overlaying approach have been studied
Further, as remote sensing methods regularize continuous landscapes into a grid of equal sized and regularly spaced data in the form of pixels (Fisher, 1997), it is anticipated that there will be some degree of dependency between pixels, most likely in the form of positive spatial autocorrelation. Such dependence has potentially a dual impact on the analysis of image data. On the one hand it is a source of nuisance and error, when traditional statistical techniques involving assumption of independence of sampling units are applied, while on the other hand it represents a valuable information, which may be exploited as an image characteristic. Hence, use of satellite data for estimation of crop acreage, considering spatial variability of crop area distribution needs to be considered. GIS is a potential tool for handling voluminous remotely sensed data and has capability to support spatial statistical analysis. Thus there is a great scope to improve the accuracy of crop area estimates by incorporating the effect of spatial dependency through integrated application of remote sensing technology and GIS.
Keeping in view the above facts, in the present study, an attempt has been made to estimate area under wheat crop for the year 1995-96 in Rohtak district of Haryana by following three approaches namely (i) through simple random sampling of villages (ii) using remote sensing technique of boundary overlaying approach and (iii) through spatial sampling approach namely Stratified CUBSS and Stratified DUBSS (Prachi et al 2002).
2 Study Area
This study has been carried out for Rohtak district of Haryana State for wheat acreage estimation, during Rabi season of the year 1995-96. During the year 1995-96, Rohtak district was reconstituted with four tehsils namely Rohtak, Jajjar, Bahadurgarh and Maham, altogether consisting of 402 villages. The digitized map of Rohtak district having 402 villages is shown in the Fig. 5.1. (Gohana tehsil containing 90 villages, which was earlier a part of Rohtak district during the year 1991-92, has been included in Sonepat district of Haryana. Hence, from this map, which is based on digital data of 1996, Gohana tehsil has been excluded.). Rohtak district is covered in twelve Survey of India Toposheets of scale 1: 50,000.
3 Data Used in the Study
Three types of data have been used in this study, satellite data obtained through remote sensing of Rohtak district of IRS 1B- LISS II for February 17, 1996 , spatial data in the form of digitized maps of all the 402 villages in the district obtained through GIS and cop acreage data of selected villages from the girdawari records maintained by the village patwaries.
4. Estimation Procedures
The area under wheat for the district was estimated by using three methods namely (i) simple random sampling technique, (ii) usual remote sensing technique and (iii) the proposed spatial sampling technique using remote sensing and GIS.
4.1 Estimation by Simple Random Sampling
A sample of 100 villages was selected from the entire population of 402 villages by the method of simple random sampling. The data for area under wheat crop, of these selected villages were obtained from patwari records. The usual estimator of simple random sampling was applied to obtain the estimate for area under wheat crop and its standard error.
4.2 Estimation by Usual Remote Sensing Technique using Administrative-Boundary-Overlay Approach
In this approach, the district administrative boundary of Rohtak district was overlaid over the remote sensing image to extract the image of Rohtak district in all four bands. Then wheat crop area was identified and estimated by following supervised maximum likelihood classification.
4.3 Spatial Sampling Technique Using Remote Sensing and GIS
In this approach, remote sensing digital data, in the form of NDVI has been used as an auxiliary character for the spatial sampling technique. Prachi (2002) proposed improved spatial sampling schemes and suitable unbiased estimators, which take into account the order of the draw. On the basis of the method of sample selection and estimation four spatial sampling methods have been suggested. These are (i) Contiguous Unit Based Spatial Sampling (CUBSS) Technique (ii) Stratified Contiguous Unit Based Spatial Sampling Technique (iii) Modified Contiguous Unit Based Spatial Sampling Technique (iv) Stratified Modified Contiguous Unit Based Spatial Sampling Technique.