A New Concept in Modelling Land Use land Cover
Modelling Concepts and Principles
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 described. Also, there exists considerable gap between the potential suitability of a given area to its actual productivity. Recent advances in modeling crop-yields based on their phenology has yielded better results, through the majority of them are point/location-specific.
In order to model land use/cover changes under the assumption that its function is influenced by the prevailing economic conditions at a given place and time, it is necessary to evaluate or estimate the scenario that closely resembles reality. The human ability to comprehend and anticipate (with a limited risk) 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-marker. 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 in a given year, for arriving at the choice of the annual land use. (see Fig 2.)
Fig 2. Digital World of GIS: Agent-based Macro-Micro Integration
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 at the grid level, thereby creating an action in response to the natural and economic stimuli.
World of Micro and Macro Information
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.
Additional Information Used
In addition to the above data, the experience of different researchers in arriving at qualitative conclusions on the land use practices in the different regions of the study area are also considered in charting out the behavioural patterns of the agents.
Model Description
The overall framework of the model is given below, in Fig 3. The model consists of four sub-models the bio-physical crop yield sub-model, the agricultural income sub-model, the urban land use sub-model and the land use decision sub-model. All these fur sub-models interact and have feedback loops, to determine the new course of action by the agent at the next time step . The model structure is sequential. The model calculations were carried out on a land unit basis, consisting of 1km square grids.
Fig 3. Conceptual Framework of the Model
The bio-physical crop yield sub- model calculates the potential productivity of the land unit for the given conditions of soil topography, water availability and climatic parameters. The distribution of water availability takes into account the soil conditions, amount of rain-received, and the existence of irrigation facilities. The main assumption of this sub-model is that there is a strong linkage between the climate and crop distributions. (Leemans, et.al., 1993). The crop yield estimates are derived by modifying the approach described in the EPIC model (EPIC, 1990). The central concept of this approach is the growing period and the photosynthetic efficiency of the crops. The biomass and yield calculations are carried out on a day -to day basis and the final yield takes into effect the fluctuations in water and nutrient availability.
The agricultural income sub-model estimates the income per land unit from various sources including the yield-related revenue and the cost of production. The model also accounts for the initial cost
incurred in land conversion from other uses to agricultural lands. The other incomes considered are the non-yield-on-farm income and the off-farm income. These factors influence the decision making process, in case of fluctuating crop-field incomes from a given area.
Urban land use is the other major land use that is primarily influenced by the activities of the human beings. Here, we estimate the urban land requirements as it competes with the agricultural area due to increasing population pressures and the rise in the economic levels of the region. The model takes into account the locational value of the land-unit in assessing the new areas that will be urbanized. The model assumes that all the extra land needed for the urban areas in a given year is fulfilled in the next year. The model provides information on the urban land demand and supply, on a spatial basis.
The final step in the simulation is the land use decision mode, which uses the estimated income, urban land needs & the existing landuse in the land unit under consideration as its input to predict the land use. The "agent" is the decision maker in this model, where in the agent arrives at a decision taking into account the prevailing conditions in the respective grids. In additions to the economic factor, the demographic condition (age distribution and education levels) and the land use history are considered to help in arriving at a reasonable estimate for the change in the land use patterns. The decision includes the consideration of risk when arriving at the crop combinations in the respective grid. Also, the model accounts for the external influences to shifts in the agricultural patterns, by considering the export quantities of specific crops, like cassava in Thailand. As of now these external influence are exogenous variables and are not calculated within the model.
Study Area
The study area choosen to check the application of this model is the Royal Kingdom of Thailand, because land use/cover in Thailand has undergone dramatic modifications in the last century. Cultivated land area has shown an astounding increase by nearly ten-times, a net increase in area by about 616.4 million hectares, during the period 1880-1980 and has since risen by 10% till 1990 (on a year -on-year basis), because of two reasons-one that it being a recent period in history helps us to get a substantial amount of information on some of the causes for changes including quite detailed data at the sub-provincial level. The second reason is that this has been a period of rapid changes in the country's economic structure and the model-run would help us to understand the pitfalls and the better point in our assumptions.
Our aim in developing the "agent -based" model was to mimic the change process by including all the major forces that drive land use changes as well as the basic bio-physical characteristics at the lowest level of interaction (the land-lot size), to help in constructing the possible land use change scenarios, Also , it would help to evaluate our understanding of the land use change mechanisms. The results from within the acceptable range of estimation and the approach has a high potential in estimating yields.
The income sub-model depends heavily on the initial data and the tuning of the model according to it. Historical data can be used to develop scenarios of land use changes and the model can also be validated with such data. In addition to it, the use of remote sensing images can be made to compare the estimated land cover from the model, with the measured values. In this case, care must be taken to maintain the spatial resolution at acceptable levels of comparison.
The entire modeling approach is based on the GIS platform. The use of GIS platform and it tools has helped in analyzing the micro-information (spatial) within the boundaries of the available macro-level (non-spatial) data. The results of the model application to simulating land use changes within the national boundaries of the Royal Kingdom of Thailand, the case study region, will be presented at the conference.
Reference
- Alcamo, J., Kreileman, G.J.J., Krol, M. and Zuidema, G., 1994. Modelling the global society-biosphere-climate system. 1. Model description and testing. Water Air Soil Pollution, 76, pp. 1-35.
- Bilsborow, R.W. and Okoth-Ogendo, H.W.O., 1992. Population-driven changes in land use in developing countries. Ambio, 21, pp.37-45.
- FAO., 1978. Report on the Agro-Ecological Zones Project, Food and Agricultural Organisation of the United Nations, World soil Resources Report 48, Rome.
- Leemans R. and Solomon A.M., 1993. Modelling the Potential Change in Yield and Distribution of the Earth's Crops under a warmed Climate, Climate Research Vol. 3.
- Rajan, K.S. and Shibasaki, R., 1997 a. Estimation of Agricultural Productivity and Its Application to Modelling the Expansion of Agricultural Land in Thailand. Journal of Agricultural Meteorology, 52(5)pp. 815-818.
- Rajan, K.S. and Shibasaki, r., 1997b, National Scale Land Use Change Modelling-Issues and Applications. In: Proceedings of the 18th Asian Conference on Remote Sensing, Kuala Lumpur, Malaysia, pp. H-2.
- Robinson, J. 1994, Land-use and land -cover projections. Report of working group C in Changes in Land-use and Land-Cover: A Global Perspective' (Editors: Meyer and Turner) Cambridge Univ. Press.
- Sharpley, A.N. , and Williams, J.R. (eds.), 1990. EPIC -Erosion/Productivity Impact Calculator:1. Model Documentation, USDA Technical Bulletin No. 1768.
- Turner II, B.I., moss R.H. and Skole, D.L. (Editors), 1993. Relating land use and global land-cover change: a proposal for an IGBP-HDP core Project. IGBP report No. 5, 65pp.
- Veldkamp, A. and Fresco, L.O., 1996. CLUE: a conceptual of land use and its effects. Ecol. Model., 85: 253-270.
- Wegener, M., 1994. Urban/regional models and planning cultures: from cross-national modeling projects, Environment and Planning B: Planning and Design 21:629-64a.
