14 probability distribution of models for the classes of interest to those in which the
multi spectral space in partitioned into class-specific using optimally located surfaces Richards, 1993.
2.6. Role of GIS and Remote Sensing
GIS is a tool for input, storage and retrieval, manipulation and analysis, and output of spatial data Marble et al. 1984. GIS functionality can play a major role
in spatial analysis. Considerable effort is involved in information collection for the suitability analysis for crop production. GIS has the ability to perform
numerous tasks utilizing both spatial and attribute data stored in it. It has the ability to integrate variety of geographic technologies like GPS, Remote Sensing
etc. The ultimate aim of GIS is to provide support for spatial decisions making process Foote and Lynch 1996. In multi-criteria evaluation many data layers are
to be handled in order to arrive at the suitability, which can be achieved conveniently using GIS.
Remote sensing provides information about the various spatial criteriafactors under consideration. Remote sensing can provide us the
information like land usecover, drainage density, topography etc. Many of the non-spatial parameters can also be inferred by looking at the various spatial
parameters. Remote sensing in combination with GIS will be a powerful tool to integrate and interpret real word situation in most realistic and transparent way.
Research by Leingsakul et al. 1993 showed that integrated GIS and remote sensing technologies apart from saving time and yielding good data quality have
the ability to locate potential new cropland sites.
15
2.7. Weighted Method Analysis
The basis of this research is a classification problem in which class definition is done through training samples for a particular class of interest. For
labeling samples, it is necessary to define all of the class’s existent in a given data by collecting ground truth or existing data.
Typically multiple criteria have varying importance. To illustrate this, each criterion can be assigned to a specific weight that reflects its importance
relative to other criteria under consideration. The weight value is not only dependent the importance of any criterion, it is also dependent on the possible
range of the criterion values. A criterion with variability will contribute more to the outcome of the alternative and should consequently be regarded as more
important than other criteria with no or little changes in their range. Weights are usually normalized to sum up to 1, so that in a set of weights w
1
, w
2
, w
3
, … w
n
, ∑ w
i
= 1. There are several methods for deriving weights, among them Malczewski, 1999: ranking, rating, pair wise comparison and trade-off.
The simplest way is the straight ranking in order of preference: 1 = most important, 2 = second most important, etc. Then, the ranking is converted into
numerical weights on a scale from 0 to 1, so that they sum up to 1 http:journalofvision.org216.
2.7.1. Simple Additive Weighting
Simple Additive Weighting SAW or Weighted Linear Combination WLC is the most often used technique in multi-criteria decision making Fisher,
1994. Criteria here may include weighted factors and constraints. Calculating
16 the product of weight and factor multiplied with all constraints at any location,
and then summing up all products yields a total overall score. The score for each alternative A is:
A = SUM w
i
x
i
or A = SUM w
i
x
i
SUM c
j
if a constraint is part of the decision x
i
= criterion score of factor i, w
i
= weight of factor i, c
j
= criterion score of constraint j
3. III. RESEARCH METHODOLOGY
3.1. Description of Research Area
The research area is located in Bantul Regency, Yogyakarta, Indonesia. Geographically the area is located between 110° 12 34 - 110° 31 08 East, and
07° 44 04 - 08° 00 27 South. The breadth of Bantul Regency has an area of about 50,685 Ha or 506.85 square km and consist of 17 seventeen districts.
Figure 3.1 shows a map of Bantul Regency.
Figure 3.1. Map of Bantul Regency Topographically, most of Bantul areas are flat land and some parts are
infertile hilly areas. In western part, stretching from north to south is low and some hilly lands of about 89.86 square km. The middle part is flat and low land,
but is fertile, covering about 210.942 square km. The eastern part varies from low, undulated to steep areas covering about 206.05 square km. The southern
17
18 part, which is actually part of middle area, is sandy and lagoon area, from
Srandakan, Sanden, and Kretek Districts. The area of Bantul is classified into wet tropical area. The wet season
occurs between November – April and the dry season between May – October. In 2004, it was recorded that the number of rainy days of 30 days happened in
January. But normally the highest average monthly rainfall occurs in December of about 316 mm and the highest rainy days of 14 days.
3.2. Research Materials and Tools
