Home Articles A new concept in modelling land use land cover

A new concept in modelling land use land cover

ACRS 1998

Land Use

A New Concept in Modelling Land Use land Cover

KS Rajan* and Ryosuke Shibasaki**

**Center for Spatial Information Science, University of Tokyo,
7-22-1, Roppongi, Minato-Ku, Tokyo 106-8558. (Japan)
Tel: (81)3-3402-6231 Fax: (81)-3-3479-2762
E-mail: [email protected]


A model to simulate the changes of land use, at the national-level is presented here. The land use model developed here takes into account two major land uses that are influenced by humankind-agricultural and urban land use. The model simulates the changes in the land use in space and time, as a result of the interaction between the biophysical conditions and the socio-economic factors prevalent. The model proposed here deals with the development and application of a new concept 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 micro and macro-information. The bio-physical characteristics of the land is considered along with it economic condition, given the social apparatus at a given point in time, in arriving at the choice of the land use. The entire process is carried out on a grid -by -grid basis, and is aggregated at the different scales to analyze and the results compared with the prevailing macro-condition. This kind of bottom-up approach with inter-scale aggregations help to develop a more realistic scenario of the land use changes. The use of GIS platform and its tools has helped in analyzing the micro-information (spatial) within the boundaries of the available macro-level (non -spatial)data.


The surface of the earth has always been influenced and transformed under the effect of natural forces, through the material flows and energy transfer that take place at different scales-global, regional and local scales. In all these changes, one area stands out very markedly-that being the changes in the land use and land cover. Most of the global physical and biological models that have been developed for the understanding of the various parts of the global earth system, are dependent on the land use and land cover information, as these with their control on the albedo and water & nutrient cycling establish the boundary conditions to these models (Robinson, 1994). Land use/cover is c continuously changing, both under the influence of humans and nature, resulting in various kinds of impacts on the ecosystem (Rajan, et. Al., 1997a). These impacts at local, regional and global levels have the potential to major human life supporting systems. The most important factor in the modification of the land cover and its conversion is the human use component rather than the natural changes (Turner, et.al., 1993). Changes in the land cover cannot be understood without a better knowledge of the land use changes that drive them and their links to human causes. The linkages between the human and biophysical causes or drivers to land management and land cover are not sufficiently understood (Rajan et. al., 1997b). This arises from the complexity in dealing with the considerable variations in the land use drivers, land use and land cover at the various levels -local, national and regional. At present, the global models and studies of land use changes capture the broad sectoral trends based on the changes in some of the macro variables, like population, quality of life and technology level. The statistical data shows a strong support in concluding that these variables may be the underlying drivers of environmental changes (Bilsborrow et.al., 1992). On the other hand, such statistical relationships do not hold good for long-term analysis if the trends are dominated more by policy options, like export oriented agricultural production of cassava, and not by the inherent needs of the population, as seen in Fig. 1, for Thailand. Around 10% of the Cassava produced is consumed domestically, while the rest is mainly for export markets.

Fig 1. Cassava Production and Population distribution in Thailand for the period 1961 to 1991

Land use can be looked upon as a multi-dimensioanl (= 4D) process which consequently poses many difficulties for proper description and classification. In the context of global change, the formal characteristics of land use, i.e., its effect on cover structure, phenology and composition, is more relevant than the purpose or function of landuse (Veldkamp et al., 1996). But, unless the function is properly understood, it is difficult to amalgamate the land use conditions into the processes that drive the changes in land use and land cover. 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, educational levels 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 interpretation should be taken into account.

Here, in this paper we describe a new concept that can be used to effectively model the micro-characteristic that describe the landuse along with the known macro-variables that also influence such changes. First, we describe the general concept and principles of the existing modeling approaches, and followed by the principles and issue of the new concept developed us also, its applicability will be discussed within the framework of developing a land use/cover change model at the national level.

General Concepts of Land Use and Land Cover Models

Modelling approaches vary according to the objective of the studies and the type of data available. Models may be deterministic or stochastic in nature; macroscopic or microscopic in extent; evaluation models or optimization models. The modeling approaches also depend upon the type of study carried out in understanding the different aspects of the changes that are taking place. These may be a purely descriptive study leading to a cause-effect type of explanation of the changes; or a statistical study with the objective for a probabilistic model at a macro-scale; or just monitoring of changes 9with the use of remote sensing technologies ) at a spatial and temporal scale. Models that can accommodate more than one standard approaches, as listed above, have not been developed though it has been felt that some of these approaches, on their own, are less applicable in real practice. This lack of development comes both from the fact that there is not only a need of extensive information and data but also a common basis on which to compare these issues – the development of a common currency for the model. Also, such efforts will have rather large computing needs, both in terms of speed and data storage. With advances in computing speed and the evolution of better GIS systems, the possibility of combining some of the approaches on a single platform is becoming possible.

Need for Scale Analysis

The modeling at a national level is more complex that to generate a global picture, while local case studies pose a separate set of problems. They not only do not link well with either the regional or global scale models, but also identify and entirely different set of driving variable than the global models. The transformations in the land cover, occurring on the large scale will lead to large -scale change. In the “global environment”. These changes are complex and require different scales of analysis (Alcamo eta al., 1994, Robinson, 1994). The changes from forest to agricultural land uses have had tremendous impact from local to global scale, such as soil erosion, deterioration of water quality and carbon emission. Thus, to model Land use/cover changes realistically, it 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 the factors influencing them.

Existing Models

At present, there is only IMAGE2 model (Alcamo et al., 1994), a global model integrating agricultural systems, food supply and other environmental issues. It has a comprehensive model for land cover
Estimations that is linked to the demands of the agricultural sector (Zuidema et al., 1994). The simulations consider human driving forces as population, GNP and technological developments based on scenarios for 13 world regions on a grid basis of 0.5 deg. In size. Most of the models are successful within their limited domains of validity and scales. No inter-scale dynamics are considered in most models, and as long as this is not done it, is difficult to get realistic simulations.

Recently, a new model CLUE (Veldkamp and Fresco, 1996) was developed as a dynamic model to simulate conversion of land use and its effects. It brings about changes in land cover, at the sub-national level based on the interaction between the biophysical drivers and human drivers. The model is based on rules derived from qualitative information, and includes feedback loops of effect to land national land use/cover changes in Costa Rica, readily available statistical data at the national scale was used. The authors observe that even though there are not methodological constraints to scale down and/or up and to link up with other models, there exists data limitations that prevent such an exercise.

At the local scale, the initiative in modeling land use can be traced to the 1960s, when there was an emergence of large-scale urban/regional models linked to the regional planning paradigm dominant in have been developed in the various countries. Also, these models have evolved in time from just being urban/regional planning process. The focus of these models have been mostly in the domain of urban and sub-urban systems. Also, these models are based on a variety of factors in the socio-economic sphere, like transportation network, ownership, existence of vegetation (parks, etc) and population density to influence the land use decisions.

The shift towards understanding the impact of human activities on environmental and resources sustainability, with an aim to better manage the land use and land cover system at the landscape level, is driven from the recognition of the fact that regional and global-level changes are influenced by the local changes. But, a lack of modeling initiatives in land uses other than urban areas, is a major cause in limiting the knowledge acquired at these levels to be amalgamated into the higher levels of analysis.

ACRS 1998

Land Use

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.


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