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Conformity Analysis of Cotton Crop using Remote Sensing and GIS
2.4(i) Ranking Soil Parameters and categories:
The soil suitability map was derived from the base soil map of NBSS & LUP at 1:250000 scale. Based on the guidelines provided by the agronomic experts, the soil parameters and the categories within them were arranged in rank order; that is every parameter / category under consideration is ranked in the order of the decision maker’s preference. Straight ranking was used i.e., the most important = 1, second important = 2 etc.
2.4. (ii) Deriving weightages:
Once the raking is established, numerical weights are generated from the rank-order information using the Rank Sum method. Rank Sum weights are calculated according to the following formula:
Wj = ( n - rj) / S (n – rk+1)
Where wj is the normalized weight for the j
th criterion, n is the number of criteria under consideration, (k = 1,2….n), and rj is the rank position of the criterion. Each criterion is weighted (n-rj+1) and then normalized by the sum of all weights, that is, S (n-rk+1).
2.5 Integration of soil parameters:
The various soil parameters with their weightages need to be integrated to derive an overall assessment of the alternatives. Additive decision rules are the best known and most widely used methods in GIS based Multi criteria decision-making. Simple Additive Weighting
method is used for the integration of soil parameters.
This method integrates the data and information on alternatives and decision maker’s preferences into an overall assessment of alternatives. This technique also referred to as Weighted Linear Combination (WLC) method which is the most often used technique for tackling multi- attribute decision-making problems. This method is based on weighted average of the weights assigned by the decision-maker to each attribute. A total score is obtained for each alternative by multiplying the importance weight assigned for each attribute by the scaled value given to the alternative on that attribute, and summing the products over all attributes. When the overall scores are calculated for all the alternatives, the alternative with the highest overall score is chosen. Each alternative (Ai) is evaluated as follows:
Ai = S (wjxij)
Where xij is the score of the ith alternative with respect to the jth attribute, and the weight wj is normalized weight, so that S (wj) = 1. The weights represent the relative importance of the attributes. The most preferred alternative is selected by identifying the maximum value of Ai (I = 1,2,…m).
2.6 Reclassification of composite layer into suitable zones:
The composite layer generated above contains the overall scores for all the alternatives. Alternatives with high overall scores are more suitable. The entire range of values in the composite layer is divided into 4 classes using the Equal Interval Classification method. The four classes are Most suitable, Highly suitable, Moderately suitable and Least suitable.
4.3 Estimation of area under different suitability classes:
The soil suitability map derived as above was then subjected to the ‘dissolve’ operation based on the suitability class attribute, so that adjacent polygons having the same suitability class are merged. By using the statistics operation with ‘sum area’ area corresponding to each suitability class is obtained.
4.4 Analysis of satellite data:
The satellite data of IRS 1D LISS – III of November, 2000 was classified by total enumeration approach employing the supervised maximum likelihood classifier algorithm (Krishna Rao, et al., 2000), using ERDAS IMAGINE-8.5 software. The satellite data was registered with the soil map to carry out the overlay analysis. The classified raster data of cotton was converted to vector format, which contains information about the spatial distribution of the crops within the district.
4.5 Estimation of area of cotton under different suitability regimes: The cotton mask coverage containing the areas where cotton is grown is overlayed with the soil suitability map using the union operation. The union operation retains the shapes and attributes of both the input layers. Therefore, the resultant layer provides the information about how the cotton crop is distributed across the various soil suitability zones. With the help of the statistics operation on the cotton-soil overlaid coverage, the amount of cotton growing area in each suitable zone was derived.