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Rajan, K S
provincial level and finally at the national level, to analyse and compare the results with the prevailing macro-condition.
These kind of inter-scale comparison helps to develop a more realistic scenario of the land use changes. The use of GIS
platform and its tools has helped in analysing the micro-information (spatial) within the boundaries of the available
macro-level (non-spatial) data. The model was developed and its application was tested to simulate the land use changes
for the period of 1980 to 1990, within the national boundaries of the Royal Kingdom of Thailand. As the model
considers the agent behaviour explicitly and at the same time considers the different drivers to landuse, the model can
also be used to understand the human responses to the changes in the environment
5
2 AGENT-LUC MODEL
In order to model land use/cover changes under the assumption that it is influenced by both the biophysical conditions
of the land unit and the prevailing economic conditions at a given location and time, it is necessary to estimate the
change mechanisms that may reflect some of the local process. The human ability to comprehend and anticipate, with a
limited risk assessment, needs to be considered in deriving land use/cover changes. The model proposed here deals with
the development and application of a new concept, proposed by the authors, in simulating the land use/cover changes —
the presence of an "agent" as the decision-maker. The agent decides on the next course of action based on the
information available to him from both the worlds of macro and micro information. The decision making process takes
into consideration the prevailing bio-physical characteristics of the land, the economic condition, and the land use
history along with the existing social apparatus (demographic pattern) in a given year, for arriving at the choice of the
annual land use. (see Figure 1.) As a large amount of datasets is needed to be managed and processed for such a model,
GIS was extensively used as the platform for managing and visualizing both the input and output data.
| Macroscopic Model (economic growth, change of life style, trade etc.) |
Macroscopic data (socio-economic/statistical data — 4g ——————jStatistical
such as population, GNP, Prices, etc.) Studies
per Y E m Behavioural
AGENTS : Arm] Descriptive
«4
- (Self-adaptive) Approaches
«^
competition for lan Demographic
SE
Major Outcomes :
¢ land use determination Enables modeling land uses B
population migration
GIS based approach and Analysis
supported by the land conditions
kai di
Spatial / Geographic data: (mainly physical land condition data)
- topography, land use, water availability, soil conditions etc.
A
Figure 1. “Digital World of GIS”: Agent-based Integration of Macro and Micro Information
2.1 Concept of an Agent.
Here, the term agent refers to an individual or a group of individuals who exist in a given area (referred to as grid) and
are capable of making decisions for themselves (or the given area). The agent also acts as an interface in helping to
assimilate the broader macro-information into the decision-making process at the grid level, thereby creating an action
in response to the natural and economic stimuli.
In this paper, the term 'micro' refers to the data used at the grid level in assessing the supportability of each grid. The
crop-specific productivity is calculated at the grid-level, considering the local bio-physical characteristics. The bio-
physical attributes considered here, are the climate (temperature, rain and radiation) and soil properties, along with
water and nutrient stresses to agricultural productivity. The 'world of macro' information refers to the data at the sub-
national (regional or provincial) or national level. This data is mainly statistical in nature. It is used to compare and
adjust the model simulations, to arrive at realistic cause-effect relationships within the model. The macro-data
considered are total agricultural demand and supply in a given year, the GNP per capita changes, the contribution of the
agricultural and non-agricultural sectors to GNP, and population distributions at the National and sub-national levels.
International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000. 1213
