13
2.3. Methodology
Changes in land use pattern are related to a large number of biophysical and socio-economic factors. The modeling of spatially explicit changes in land use
pattern requires, therefore, a large database of factors considered to be important in the case study. Therefore, the database is not similar for every application. To run
the model it is minimally needed to have spatially explicit data for at least 1 year. However, to allow calibration and validation model works, it is necessary to have
data of another different year. To meet this necessity, the research will employ data from 2 different years, with 6
years’ time difference, i.e. 1991 and 2009.
2.3.1. Data Preparation
2.3.1.1. Data Requirement for Land Use Change Model
For the simulation of dynamics of the spatial pattern of different land use types, data are needed for the land use distribution and a number of biophysical and
socio-economic parameters that are considered as important potential drivers of the land use pattern. These drivers are most commonly variables that describe the
demography, soil, geology, climate and infrastructural situation. This study only considers the biophysical aspects, while the socio-economic aspect is considered
constant Business as usual. The data required to analyze land use change process and build scenario
development were obtained from various sources. The data are derived from multispectral satellite data, extracted from digital topographic data, and from spatial
processing of statistical data. According to type, data are divided in three: remotely sensed data, digital topographic data and statistical data. How the data were
collected and used will be explained below.
Remotely Sensed Data
Remotely data used in this study comprises of Landsat images 1991 30 m, and ALOS-AVNIR image 2009 10 m. These data will be used for land use change
analysis and input for trend extrapolation to calculate land use requirements year 1991-2030.
14 Table 1.
Remotely Sensed Data Requirement for Land Use Model
Image Resolution
Date Acquisition Source of Data
Landsat TM 30 m
28 July 1991 GLOVIS
ALOS AVNIR 10 m
17 July 2009 BTIC
– BIOTROP
Topographic data
Topographic data sets are obtained from Agency of National Survey and Mapping Bakosurtanal
– Geospatial Information AgencyBIG, nowadays, Bogor. These data are digitized from topographic map scale 1:25.000 and are extracted for
selected layers, including road, facilities and public service distribution, and central of economic. All layer data have been digitalized, infrastructure and facilities, road,
river, public services and industries location. Data sets used in this study can be seen in the following table.
Table 2. Topographic Data Sets Used in Analysis Land Use Change and
Scenario Development
Data Set Year and
Scale Class
Source Collection
Method Data Use
Land use map
1991, and 2009; Scale
1:50.000 5 classes See
Table 4 for
detailed land use classification
Interpretation from Landsat TM
and ALOS- AVNIR images
Interpretation using
supervised classification
Land uses data are used for land use change analysis and
trends extrapolation and land demand input in CLUE-S
Road 2003; Scale
1:25.000 Classified as main
road, appropriate with study area
Rupa Bumi Indonesia RBI,
2003 Derived from
topographic map RBI
Road map used as driving factors influencing land use
change and pattern, it will be processed by using distance
analysis to obtain classes of distance from road
Elevation Data
2005, Scale 30m
Continuous USGS
Derived from SRTM-Level 2
Interpolation and rasterizing to obtain digital elevation
data and image classification to obtain elevation class
Facilities and public
service distribution
2003; Scale 1:25.000
All of facilities education, health
and public service facilities
Rupa Bumi Indonesia RBI,
2003 Derived from
topographic map RBI
Facilities and public service are processed by using
proximity analysis to obtain distance classes of facilities
Central of Economic
2003; Scale 1:25.000
Business center Rupa Bumi
Indonesia, 2003 Derived from
topographic map
COEs are processed by using proximity analysis
Forest Designation
Map 2009, Scale
1:250.000 Forest Area, Non
Forest Area Directorate
General of Forest Planology,
Ministry of Forestry
Re-delineate in GIS
environment Forest designation map will
be used as spatial policy in the model.
Socio-economic data
Statistical data in this study consist of population data in village level year 2007. Other data socio-economic are total education and health facility unit for each
village in the study area year 2007.