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
The data used in this research consist of remotely sensed data, topographic data, soil data, and climate data Table 3.1. The tools are softwares that required
for image processing, spatial preparation process, and spatial analysis. Table 3.1. Data Requirement
Data Description
Topographic: Research Area
Administration Hydrology
Contour Land use
Soil Imagery
Climate:
Temperature Rainfall
Bantul Sub-district
River and seasonal river Relief
Vector and raster data Soil type in research area
Landsat TM Temperature in research area
Data and distribution of rainfall
The toolsoftware that required consists of: - ER Mapper 6.3 for image process
- Autodesk Map 5 for spatial preparation process - ArcGIS 9 for spatial analysis
19 The hardware requirement for processing data at least has to fulfill the
specification: PC Pentium III, 256 MB RAM and 40 MB Hard disk.
3.3. Research Methodology
The procedures of this research consist of data compilation, data preparation, spatial analysis, modeling approach, and data validation. The flowchart of
research procedure is represented in Figure 3.2.
3.3.1. Data Collection
The data input is collected from various sources, e.g.: -
Topographic data which is obtained from National Coordinating Agency for Surveys and Mapping BAKOSURTANAL,
- Information of soil type is derived from regional soil maps produced
by Center for Soil and Agro Climate Research PUSLITANAK, -
Climate data were obtained from Bureau of Meteorology and Geophysics BMG and Puslittanak Bogor,
- and Imagery data.
3.3.2. Data Preparation
1. Image Processing
The first step of data preparation is to process the satellite image of research area, while the activities comprise of image processing and vector data processing
and analysis. In image processing the activities consist of identifying the data source coordinate system, format conversions, radiometric correction, geometric
correction, cropping image in research area, and image enhancement.
Figure 3.2. Scheme flowchart of the research Respondent
Data
Revenue Cost Analysis
Land Overlay Tentative Map
Land Suitability Map
Weighting
Area Selection with more
than one suitability criteria
Existing Condition
Spatial Analysis Land
Suitability Rainfall
Map Climate
Data
Image Processing
Land use Map
Landsat TM
Soil Map
Digitizing Generate
Rainfall Map Derive
Temperature Map
Soil Map Generate
Slope Map
Slope Map
Temperature Map
Topographic Map
Data Collection
Agricultural Potential Map
20
Figure 3.3. Description of Image Processing The image was then classified by using Supervised Classification technique
into several types of land uses. The classification processed was completed by landuse data, which obtained from the Bantul local government.
One of main steps in image classification is the ‘partitioning’ of the feature
space. In supervised classification the process is realized by defining the spectral
characteristic of the classes by identifying sample areas training areas. A sample of a specific land use class like rice field, comprising of a number training pixels,
form a cluster in feature space. Classification Result Data
Image Enhancement Cropping Image
Landsat Imagery
21
After that, all vector data required were extracted using spatial processing
software. The landuse data result will be used for the next spatial processing and analysis steps.
2. Generated Slope Map
The topographic data provide varying altitude of the research area. A slope map was derived from the contour of topographic map and was classified into
several classes. The topographic data that used in this research were already in digital format, so for generating slope map only took the contour data and
processing by 3D analysis tool in ArcGIS 9 application.
Figure 3.4. Generating of Slope Process Contour Data
Slope Map TIN Slope
22
The slopes were classed or grouped depending on the rank that each crop requires this was done based on available literature. The detail slope class of
each crop can be seen in appendix.
3. Generated Temperature Map
Temperature data were required to determine the distribution of temperature area. The temperature data was estimated using a formula with the
input of altitude polygon derived from altitude of topographic data. Same with soil and altitude, plant need certain temperature condition to grow optimally. The
formula that is used to estimate the temperature data is the Braak formula, and
the equation is given below: T = 26.3 °C – 0.01 altitude in meters 0.6 °C
Contour Data Temperature Map
Braak Formula
Figure 3.5. Generating Temperature Map In this case, temperatures were divided into 3 classes, based on the
limitation of the temperature that can influence to the growth of plants.
4. Soil Map Digitizing Process
Soil type data that was obtained from Puslittanak was a paper map. For further process is needed to change the format of soil type data from hardcopy
23
data to digital. This process can be done by digitizing the paper map with Autodesk Map 5 application, and then the digital data result will used for
analyzing process by using ArcGIS 9.
Figure 3.6. Digitizing Soil Map Digitized
5. Generated Rainfall Map
Rainfall map of investigated area was generated from digitized process of rainfall map which was obtained from Puslittanak Bogor. Digitalizing process
was carried out by using Autodesk Map 5 and the result was used for next spatial analyzing process.
Figure 3.7. Digitalizing Rainfall Map Digitalized
24
25
3.3.3. Spatial Processing and Analysis
1. Modeling Approach
There were several criteria involved to determine the growth factor. Multiple criteria typically, have varying importance; each criterion can be
assigned to a specific weight that reflects how big each criterion influence to the plant growth relative to other criteria. The principle of weighted method is to give
value to each factor, which influence to the land suitability for crops growth. The value of factor can be divided into two kinds of value, they are environmental
factor value and human value. The environment factors consist of soil type, water availability, slope, and temperature.
Each crop, which will be investigated in this research, has its own growth requirement. Optimum growth of crop could be reached if the requirements are
met. Based on crop tolerance to the environmental value, the degree of suitability can be divided into 4 classes: highly suitable, suitable, marginally suitable, and
not suitable. While the environment factor value depend on the condition of the
environment, which meet to the optimum growth of crops; the human factors, which contribute to the assessment of environment factors, are obtained from the
questioners that are distributed to experts. The expert in this case consists of policy makers, farmers, and researcher, which have experience or expertise on the
land suitability for each investigated crop. The human factor values are set from 0 up to 100 percent. The human factor values applied to each crop is described in
Table 3.2.
26 Table 3.2. The human factor value from expert for Environmental factors
Crops Slope
Water Availability
Soil Temperature
Total Rice 25 30
29 16 100
Corn 28 23 31
18 100 Soybean 28
22 33
17 100
Peanut 27 24
32 17
100 Mungbean 26
22 33
19 100
Source: Respondent data After getting the result of human factors values from respondents above, the
weighting method will process all data with the formula that have created. The formula describes the relationship between all factors i.e. environmental factor
and human factor in weighted method analysis. As mentioned before, there are two values for the overlay processed of
weighted method i.e. value for each environmental factor altitude, water availability, soil, and temperature, which were given by experts above, and value
for the class of each environmental factor that depend on literature. For instance, the values of overlay weighted method for corn are shown in Table 3.3.
Table 3.3. Factor and Class value of Overlay Weighted Method
Factor Weight value
Class of factor Class Value
Total Value
Slope 28
8 8 – 16
16 - 30 3
2 1
84
Water Availability
23 500 – 1,200 mm
1,200 – 1,600 mm 1,600 mm
3 2
1 46
Soil type 31 Very
suitable Suitable
Marginal Suitable 3
2 1
93
Temperature 18
20° - 26° C 26° - 30° C
16° - 20° C 3
2 1
36 Total 100
259
27 Note:
factor value : from expert Class of environment factor : from literature
class value : 1= marginally suitable, 2 = suitable, 3 = highly suitable
The land suitability value is summing up of all factor total values that were applied, and the total value itself is obtained from human factor value multiplied
by the environment class value. The minimum and maximum values of land suitability can be calculated as:
a The maximum value: if all factors have maximum class value. The maximum value: 100 3 = 300
b The minimum value: if all factors have minimum class value. The minimum value: 100 1 = 100
As mentioned before, the land suitability areas were divided into 3 classes that are very suitable, suitable, and marginal suitable. Therefore, the range value
between land suitability classes is the maximum value minus minimum value divided by number of classes. So, the range value is 300 - 100 3 = 66.67, or
rounded up to 67. The interval values for each class are: -
Marginally suitable area having value between 100 up to 167; -
Suitable area having value between 168 up to 235; and -
Highly suitable area having value between 236 up to 300. If one or more factors or classes have 0 zero value, the result becomes a
not suitable areas.
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2. Revenue Cost Analysis Approach
Revenue cost analysis is needed in order to get the biggest profit in the area that is suitable for several crops.
The procedure to get the potential area is done by overlaying all of suitable land area for each crop; from this activity the areas that have the suitable criteria
for more than one crop in the same suitable criteria level can be found. By inputting revenue cost analysis data for each crop, the potential crop, which could
give the maximum return, can be obtained. For the areas that have the ‘suitable’ criteria for more than one crop in the
different suitable criteria levels, for instance: the area is suitable for corn in level S3 and also suitable for rice field but in level S1; this area should be as a potential
area for the crop that has higher suitability level in this case is suitable for rice.
4. IV. RESULT AND DISCUSSION
4.1. Land Use Map
The existing condition of research area that was obtained from the classification process and completedvalidated by secondary and field data, shows
that land cover consists of settlement, agriculture area, dry land, bush, and sand. Depending on the source data, land use in research area can be divided into
several land uses see Figure 4.1.
Figure 4.1. Land Use Map of Bantul Regency. Based on the land utilization data from land use map above shows that
areas which could be processed refer to scope of research were rice field, dry land, grass, and rice dependent of rain field.
29
4.2. Soil Type Map
The Peta Tanah Semi Detail map from Puslittanak classified the soil types as Satuan Peta Tanah SPT or Land Map Unit. SPT is the smallest unit of soil
type, which had the same characteristics and distinguished element from other SPT.
From the available data used in this research, the research area consists of 78 SPT’s.
Figure 4.2. Land Map Unit of Bantul regency. Then the 78 SPT’s were analyzed one by one to get the level of suitability
of each SPT to the investigated crops. The results of analyzed process for each crop were grouping into highly suitable SPT group, suitable SPT group,
marginally suitable SPT group, and not suitable SPT group.
30
The suitability classification of SPT group for each investigated crops are shown in Figure 4.3.
Figure 4.3. Suitability map of Land Map Unit for each crop
4.3. Slope Map
