is necessary to consider the different spatial scales of 
these changes and also their drivers. Most of the 
changes are highly dependent on the biophysical 
constraints of the land units and the human 
understandings of these. The model should be able to 
simulate land use/cover changes in response to both the 
biophysical constraints - existing and the changes 
within, and the socio-economic conditions prevailing at 
a given point of time. The socio-economic factors like 
the population, economic conditions, etc are the human 
drivers that have to be considered in such a model. 
It is recognized that changes in the scale of analysis, 
changes the results. As such, it is necessary to consider 
the feedback effects in such models, as these feedbacks 
also act as causes or drivers at different scales of 
analysis. Thus, in building the model the aggregations 
at the different scales for effective analysis and 
interpretaion should be taken into account. 
Here, in this paper we describe one such approach. 
First, we describe the general concept and principles of 
the modeling approach, followed by the description of 
the sub-models that constitute such a model and we try 
to demonstrate its applicability by operationalizing it 
for Thailand. 
2. MODELING CONCEPTS 
Land evaluation and suitability has long used the 
biophysical factors like climate and soil as its 
determining factors (FAO, 1978), but the influence of 
human factors are not so well studied and documented. 
In addition, there has been considerable gaps in 
estimating the actual yields from such potential 
suitability calculations. Recent advances in modeling 
crop-yields based on their phenology has yielded better 
results, though the majority of them are point/location- 
specific. 
In order to model land use/cover changes under the 
assumption that they are influenced by the prevailing 
economic conditions at a given time, it is necessary to 
evaluate or estimate the human responses to putting 
the land to appropriate use. The human ability to 
comprehend and anticipate (with a limited risk) the 
need of the hour has to be considered in deriving land 
use/cover patterns that are seen from time to time. 
These behavioural patterns influence the decision 
making process. 
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 - 
biophysical and socio-economic. 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 
472 
existing social apparatus in a given year, for arriving at 
the choice of the annual land use. (see Fig 2.) 
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 its assimilation. 
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 of the socio-economic factors into the 
decision-making process at the grid level, thereby 
creating an action of response to the natural and 
economic stimuli. 
3. LAND USE DRIVERS 
3.1 Biophysical Drivers 
1. The biophysical characteristics like temperature, 
precipitation and soil have been considered in the corp 
yield models. 
2. Crop specific parameters are used in the yield 
models. 
3. Land use history is also an important factor. Land 
degradation may be caused because of mismanagement 
or prolonged use of the land. This can be quantified by 
soil erosion and resulting loss in soil fertility. Also, 
better facilities like irrigation, etc. may aid the yield 
Thus history will act as a feedback 
mechanism in the simulations. 
4. Pests, weeds and diseases lead to decrease in yields. 
This may have local or regional impacts on the overall 
estimations. 
productivity of the area. 
3.2 Human Drivers 
Population 
1. Population determines the domestic demand for 
foods. These are translated into the seven major crops 
that are consisdered here. No animal products are 
considered at this stage. 
2. The age distribution of the poplulation is considered 
to simulate migration tendencies. 
3. Urban population growth rates are considered. 
4. Sector-wise labour availability is also taken into 
account. 
Technology 
5. Technological inputs like irrigation and fertilizer 
applications may help to overcome the biophysical 
limitations. 
Level of Affluence 
6. The level of affluence determines the food basket and 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXII, Part 7, Budapest, 1998 
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