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Surface approximation of Point Data using different Interpolation Techniques A GIS approach Priyakant, Niva Kiran Verma, L.I.M. Rao and A.N. Singh Remote Sensing Applications Centre, Uttar Pradesh Kursi Road, Lucknow 226 021 Email : kantpriya@rediffmail.com
1.0 Project Background
Uttar Pradesh Sodic Land Reclamation Project (UPSLRP) is a major reclamation programme to improve agricultural productivity over nearly 2.4 lakhs Ha of USAR lands (Sodic Lands) in the state. RSAC-UP was assigned task of identification and mapping of B+ (Double Cropped Sodic area), B (Single Crop Sodic area) and C (Barren Sodic Land) categories of sodic land based on Remote Sensing data. The identified plots were transferred on Cadastral maps of the Villages for U.P. Bhumi Sudhar Nigam (UPBSN) to take up for reclamation. In addition to this RSAC-UP were also assigned to other functions like project Durability studies, Expansion/Reduction of Sodic land studies, Environmental monitoring to assess the impact of reclamation on Soils, Ground water, Surface Water and Biodiversity. This paper is based on part of environmental monitoring studies carried out from Ground Water datasets for six years i.e. from 1995-2000 in both pre and post monsoon seasons using Geographical Information System (GIS). 2.0 Introduction The advent of electronic and computing techniques coupled with GIS has increased the potential of creating and maintaining databases using geographic space as a key field. A GIS database has proved very useful especially for GW studies when vital decisions are required which are going to affect the entire ecology; economic and socio-economic setup of the area. Such application based on the holistic approach through the integration of various spatial and non-spatial elements and understanding of their dynamic inter-relationships. Quantitative assessment of ground water quality and depth conditions require a highly organized data collection programme that includes statistical evaluation of monitoring results (Nelson and Ward, 1981; Ward, 1989). However, with the use of GIS technique, the study got the potential to complete the monitoring programme in time, accurately and to evaluate the results. This paper describes how GIS have been used in GW monitoring programme to improve the overall effectiveness and minimize the complexity of the results. Many kinds of geographic data are often collected at irregular intervals. e.g. Ground Water data, elevation data etc. require a great deal of effort. At the same time, it is not possible to make such measurements at all the desired places and hence there is often a need to estimate values for locations where there are no measurements. These estimates are based on the data available and preferably understanding of the spatial variation of the phenomena. GIS is one such tool, which statistically estimates such survey to maximize the amount of information. This information is based on the fact that objects that are near are more important than those that are far away. With the limited numbers of sampled data, the values for places where there are no measurements, can be interpolated using suitable interpolation technique depending on the physiography of the area. In any interpolation, the basic decision is to choose a model for the statistical relationship between data inputs. The most common models for such relationship are weighted function and Spline (Nagy and Wagle, 1979). Both functions are analytical description of how to use known information to estimate, that which is unknown. Suppose we wish to estimate the Ground Water (GW) depth at a point C in-between the two measurement points A & B (Fig-1). If a line is drawn joining A and B then this line describes a locus of points that corresponds to an explicit interpolation model. This simple model is based on the rule that Change in GW depth between any two adjacent control points are linear with distance. Thus, it is simple matter to calculate the predicted depth at any point between the two control points.  Figure 1 Principal of Interpolation In GIS the estimated results are stored as raster data (grids) that represents geographical feature and variable by dividing the world into discrete squares called cells, for which a value is stored. These cells are often called grids. Each cell knows its location implicitly from the origin point and its location relative to origin. The exact location of each cell is not stored, just the origin, cell size and no. of cells from the origin are stored. Continuous variables are represented in GIS as surface, where the value of other locations within the same cell can be interpolated from the cell center and the centers of the neighboring cells. Since real surfaces vary continuously, it is impossible to record all the locations that define them. Therefore the surface models, which approximate them, take representative samples of the infinite number of locations possible, using a mathematical technique called interpolation to fill the gaps between the samples. How well these gaps are filled depends on how good the source data is and type of interpolation technique used. The resulting surface (grid) theme is the best estimate of the trend on the actual surface for each location. The surface interpolator makes certain assumptions about how to determine the best estimated values.
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