32.1. Introduction

Geographic Information System (GIS) is defined as an information system that is used to input, store, retrieve, manipulate, analyze and output geographically referenced data or geospatial data, in order to support decision making for planning and management of land use, natural resources, environment, transportation, urban facilities, and other administrative records. 

A GIS thus consists of

(a) An eaxtensive database of georraphic information involving both positional data about land features and descriptive/non-locational data about these features at different points of time and

(b) Sets of programmes of applications, which enable the dat to be input, assessed, manipulated, analysed and reported

32.2. Components of GIS

  • Hardware

  • Software

  • Data

  • People

  • Methods

(i) Hardware

Hardware is the computer on which a GIS operates.  Today, GIS software runs o a wide range of hardware types, from centralized computer servers to desktop computers used in stand-alone or networked configurations

(ii) Software

GIS software provides the functions and tools needed to store, analyze, and display geographic information.  Key software components are:

  • Tools for the input and manipulation of geographic information

  • A database management system

  • Tools that support geographic query, analysis and visualization

  • A graphical user interface (GUI) for easy access to tools

(iii) Data

Possibly the most important component of a GIS is the data.  Geographic data and related tabular data can be collected in-house or purchased from a commercial data provider.  A GIS will integrate spatial data with other data resources and can even use a DBMS, used by most organizations to organize and maintain their data, to manage spatial data.

(iv) People

GIS technology is of limited value without the people who manage the system and develop plans for applying it to real-world problems.  GIS users range from technical specialists who design and maintain the system to those who use it to help them perform

(v) Methods

A successful GIS operates according to a well-designed plan and business rules, which are the models and operating practices unique to each organization.

32.3. Advantages of GIS

  • Exploring both geographical and thematic components of data in a holistic way

  • Stresses geographical aspects of a research question

  • Large volumes of data

  • Integration of data from widely disparate sources

  • Allows a wide variety of forms of visualisation

32.4. Disadvantages of GIS

  • Data are expensive

  • Learning curve on GIS software can be long

  • Shows spatial relationships but does not provide absolute solutions

  • Origins in the Earth sciences and computer science. Solutions may not be appropriate for humanities research

32.5. Spatial variability of soil characteristics

Characterization of spatial variability of soil physical and chemical characteristics (e.g., soil texture, organic matter, salinity, water content, compaction, and nutrient content) is very important for managing agricultural practices. The precision of statements that can be made about soil properties at any location depends largely on the amount of variation within the area sampled.  As heterogeneity of soils increases, the precision of statements about their properties, behavior, and land use performance decreases.

Spatial variability of soil variables is commonly a result of complex processes working at the same time and over long periods of time, rather than an effect of a single realization of a single factor. To explain variation of soil variables has never been an easy task. Many soil variables vary not only horizontally but also with depth, not only continuously but also abruptly. Field observations are, on the other hand, usually very expensive and we are often forced to build 100% complete maps by using a sample of  less than or equal to 1%.

32.6. Objectives of soil spatial analysis

Spatial analysis of soils also known as neighbourhood analysis has the following objectives.

  • To find out the weighted average of a given soil property which varies from point to point over a given area of land for result interpretation and for carrying out simulation experiments in the field.

  • To work out interpolated values of a given soil property over time and space in unsampled or unvisited sites between sampled estimates for the purpose of depicting contour lines on the base maps.

  • To develop a rational sampling strategy for characterization of soil status to pave the way for successful implementation of field experiments.

The advent of GIS softwares has simplified the process of studying the variability with geostatistics being part of every GIS software.

32.7. Geostatistics

Geostatistics is a tool to help us to characterize spatial variability and uncertainty resulting from imperfect characterization of the variability.  Geostatistics involves the theory of regionalized variables, which dates back to the early fifties and includes concepts of random function and stationarity.  Geostatistical mapping can be defined as analytical production of maps by using field observations, explanatory information, and a computer program that calculates values at locations of interest.  There are a number of spatial prediction models depending on the amount of statistics involved in the analysis.

Most geostatistical studies in soil variability studies aim at estimating soil properties at unsampled places and mapping them.  Kriging is a generic name adopted by the geostatisticians for a family of generalized least-squares regression algorithms.   

32.8. Interpolation by Kriging

Kriging is a technique of making optimal, unbiased estimates of regionalized variables at unsampled locations using the structural properties of the semivariogram and the initial set of data values.  A useful feature of kriging is that an error term (estimation variance) is calculated for each estimated value providing a measure of the reliability of the interpolation.  The simplest forms of kriging involve estimation of point values (punctual kriging) or areas (block kriging) and assume that the sample data are normally distributed and stationary. Various other estimation procedures are available when sample data show departures form these assumptions.

Soil properties often exhibit lognormal or complex probability distributions, in which case lognormal or disjunctive kriging is more appropriate.  Directional differences in variation can also be taken into account during interpolation by using the anisotropic semivariogram model to obtain the weights in the kriging system

Last modified: Wednesday, 19 March 2014, 12:05 PM