Use of Remote Sensing and GIS Technology in agricultural surveys

Use of Remote Sensing and GIS Technology in agricultural surveys

The spectral response of vegetation in the red band is strongly correlated with chlorophyll concentration while the spectral response in the near infrared band is controlled by the leaf area index and green vegetation density (Major et. al., (1990). The differential vegetation responses at these two spectral regions have been used to develop Normalized Difference Vegetation Index (NDVI), which is defined as

where, B4 is mean reflectance (digital number) in near infrared band and B3 is mean reflectance (digital number) in the red band.

NDVI image was prepared from unstretched corrected B4 and B3 bands of IRS 1B, LISS -II sensor to represent quantitatively the vegetation coverage over the area. The value of the prepared NDVI image was ranging from -0.31 to 0.62. Higher the positive value of NDVI, higher is the vegetation cover and its vigour. This NDVI image was further linearly stretched to have values between 0-255 digital numbers for better display and interpretation. In case of district Rohtak, the wheat is one of the major crops during Rabi season. Hence, major part of cultivated area was under wheat crop. Due to this fact the correlation between mean NDVI and area under wheat in a village is expected to be high. Hence It is expected that greater the value of mean NDVI of the village, larger is the area under cultivation

Two spatial sampling techniques - Stratified CUBSS, for the regular area units using contiguity based and stratified DUBSS, a modified technique for irregular area units using distance based neighbour approaches respectively, have been used here for selecting a representative sample of villages. The mean NDVI of each village was used as the auxiliary character for estimating spatial correlation using both the approaches. The spatial correlation was further used for obtaining weights for selecting the villages in the sample for better representation. The calculated values of spatial correlation for stratified CUBSS and stratified DUBSS were 0.48 and 0.57 respectively. Based on these values, weights were obtained for the sample selection.

A sample of 100 villages was selected for estimating area under wheat crop for Rabi season of 1995-96 for district Rohtak by two sampling techniques namely (i) Stratified CUBSS and (ii) Stratified DUBBS. The first unit was selected by probability proportional to size sampling. The rest of the units were selected according to the weights assigned to each unit according to stratified CUBBS and stratified DUBSS technique.

5. Results
To examine the performance of different estimators, percentage relative bias for mean (RB) for different estimators were computed using the following formula., Where, is the estimated and is true value.

The results of the study are given in the table 1. The table shows the estimates of the area under wheat crop in the district by the four methods viz. simple random sampling (SRS), Remote sensing technique, Stratified CUBSS and Stratified DUBSS. The true value of the total area under wheat in the district based on patwari records is also obtained and is given in the table. This was used for calculating relative bias for each estimator. Since, exact

Table 1 Comparison of different estimators of area under wheat for district Rohtak during Rabi 1995-96 ('000 ha)

Estimator	Area under wheat	Relative Bias (%)
SRS	132.57	7.52
Remote Sensing Technique	137.01	2.97
Stratified CUBBS	135.73	3.87
Stratified DUBBS	139.76	1.02
* True value is 141.20 (‘000) ha.

On the basis of this investigation, it has been observed that stratified DUBBS is superior in terms of relative bias followed by remote sensing estimate and stratified CUBSS. Thus it has been observed that the traditional sampling techniques can be improved upon using GIS assisted spatial sampling technique. Further, when remote sensing parameter, NDVI was used as an auxiliary character for the GIS assisted spatial sampling technique its performance was enhanced. The study demonstrated that the two technologies are mutually complementary and should be used simultaneously to achieve the best results.

Reference

Arbia, G. (1988). The damping effect on higher order spatial autocorrelation in irregular lattices. Metron., 67: 459-840.
Arbia, G. (1993). The use of GIS in spatial statistical surveys. Int. Stat. Rev., 61(2): 339-359.
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Misra P. (2000) Application of Spatial Statistics in Agricultural Surveys. IASRI, Unpublished Thesis.
Raj, D. (1956a). Some estimators in sampling with varying probabilities without replacement. J. Amer. Stat. Assoc., 51: 269-284.
Raj, D. (1956b). A note on the determination of optimum probabilities in sampling without replacement. Sank hya,17: 197-200

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