Modelling Land-Use Changes in Godavari River Basin : A

International Environmental Modelling and Software Society (iEMSs)7th Intl. Congress on Env. Modelling and Software, San Diego, California, USA

Daniel P. Ames, Nigel W. T. Quinn, Andrea E. Rizzoli (Eds.)http://www.iemss.org/society/index.php/iemss-2014-proceedings

Modelling Land-Use Changes in Godavari RiverBasin : A Comparison of Two Districts in Andhra

Pradesh

Mohit Kumar a, KS Rajan b

aLab for Spatial Informatics, IIIT Hyderabad, India ([email protected])bLab for Spatial Informatics, IIIT Hyderabad, India ([email protected])

Abstract Land use modelling requires analyzing the biophysical and socio-economic drivers for landuse change and then incorporating these to study the impact of human dimensions on LULC dynam-ics. The models help us understand the LULC changes for different socio-economic scenarios withinits defined biophysical context. This work explores the use of an agent based model (ABM) for such astudy. In this work, land–cover change agents are land owners, farmers, migrants and/or policy makingbodies, all of whom make decisions or take actions that affect land-use patterns and processes. Bysimulating the individual actions of many diverse actors, and measuring the resulting system behaviorand outcomes over time (e.g., the changes in patterns of land cover), ABMs can be useful tools forstudying the effects on land-use land cover processes.In this paper, the AGENT-LUC model has been used to model regions of Godavari river basin to un-derstand the change driver variability and its outcomes to understand the model performance, the twodistinct regions of Adilabad and East Godavari in the State of Andhra Pradesh, India are selected.While Adilabad is largely a forested landscape with pressures of urbanisation and industrial develop-ment, East Godavari is a prime agricultural landscape with expanding urban area. In addition, the twodistricts have very different socio-economic conditions like per-capita income and educational levels.The model parameters are based on an analysis of the period of 1985–95 and it is used to forecastthe changes till 2005 on an yealy/annual basis. The outcomes have been validated with the satellitederived land-use data of 2005. The model is able to achieve an overall accuracy of 92% across all theclasses and to around 97%for the major classes – Agriculture, forest and urban.

Keywords: Land-use modelling; Agent Based Modelling; Water basin; Land use land cover

1 INTRODUCTION

Land is used to meet a multiplicity and variety of human needs and to serve numerous, diverse pur-poses. When the users of land decide to employ its resources towards different purposes, land usechange occurs producing both desirable and undesirable impacts. The analysis of land use changeis essentially the analysis of the relationship between people and land. Why, when, how, and wheredoes land use change happen?Metropolitan areas in the world are growing at very rapid rates, creating extensive urban and builtup land. Many of the farmlands, wetlands, forests, and deserts have been transformed during thepast century into human settlements. Almost everyone has witnessed these changes in their localenvironment but a clear understanding of their impacts is missing. Land use and land cover changesimpact eciology, biodiversity, water, and climate at all scales, Riebsame and Parton [1994]. At localand regional scales, land cover change from new land use practices that may negatively affect waterquality and sedimentation. Urban growth and the concentration of people in urban areas can increase

Mohit Kumar et al. / Modelling Land use changes in Godavari River basin a comparison of two districts in Andhra Pradesh

environmental problems related to land use, soil degradation, can also have important implicationson water quality, Wear and Bolstad [1998] and health of watershed ecosystems. These changes inclimate, water sources, vegetation, forests etc. again causes the current land use to change and so allthe interactions between all environmental forces are interdependent and LULC change is the overalloutcome of all these forces and interactions.It is not until we study these landscapes from a spatial perspective and the time scale of decades thatwe can begin to measure the changes that have occurred and predict the impact of changes to come.In order to study and predict these closely interrelated phenomena, theories have been advanced andmodels have been built. The data from remote sensing helps us to monitor the current state andchanges to the land cover but prediction of the changes in future are hard to tell. Here modelling thesechanges over a time series data of land use would help to have some reliable prediction and under-standing of changes that occur in the land use land cover patterns.There have been different kinds of Land use models, based on the spatial scale of the analysis, periodof change, spatial extent of analysis, understainding past behaviour and extrapolating them, change-process based models. Here we consider the two broad categories of LU models to simulate the future- agent-based that tries to mimic the process and statistical models that learn from past changes.

1. Agent based models Multi agent models are based on the simulation of the behavior of individ-uals and the up scaling of this behavior, in order to relate it to changes in the land use pattern.Multi-agent models simulate decision-making by individual agents of land use change explicitlyaddressing interactions among individuals. The explicit attention for interactions between agentsmakes it possible for this type of models to simulate emergent properties of systems.If the de-cision rules of the agents are set such that they sufficiently look like human decision-makingthey can simulate behavior at the meso-level of social organisation, i.e. the behaviour of in-homogeneous groups of actors.An agent is a real or abstract entity that is able to act on itselfand on its environment; which can, in a multi-agent universe, communicate with other agents;and whose behaviour is the result of its observations, its knowledge and its interactions with otheragents Sanders et al. [1997]. AGENT-LUC, Rajan and Shibasaki [2001].The validity of these models will depend on the strength of the model of human decision makingand interaction. The challenge in this area is to obtain sufficient data at the individual/householdlevel to develop a well-parameterised and validated model of decision-making actor. Observedland-use or land-cover change outcomes are not sufficient to validate such a model.

2. Statistical models: Statistical models are based on an analysis of the spatial structure of landuse; therefore, they are not bound to the behavior of individuals or sectors of the economy. Thesemodels are simply based on an extrapolation of the trend in land use change through the use ofa regression on this change, Mertens and Lambin [2000]; Pijanowski et al. [2000]; Schneider andPontius [2001]; Serneels and Lambin [2001]; Geoghegan et al. [2001]. This type of models aretherefore not suitable for scenario analysis, as they are only valid within the range of the land usechanges on which they are based. The validity of the relations is also violated upon a change incompetitive conditions between the land use types, e.g. caused by a change in demand.Eg.IIASA-LUC, Fischer and Sun [2001] : The model is designed to establish an integrated as-sessment of the spatial and inter-temporal interactions among various socio-economic and bio-physical forces that drive land use and land cover change. ‘’Input-output” accounting tables areused by the model as the initial representation of the economy and a dynamic welfare optimiza-tion is applied on the model. Mathematically, the welfare optimum levels of resource uses andtransformations are a function of the initial state of the economy and resources, of the param-eterization of consumer preferences and production relations, and of specified dynamics andconstraints such as population growth and climate changes. The model has a low spatial reso-lution and is very data-demanding due to the multiple sectors of the economy that are taken intoaccount.CLUE and CLUE-S, Veldkamp and Fresco [1996].

There are few other models as well such as cellular automata, Integrated/Hybrid models but they arebased upon the interaction and extrapolation of input data to predict the output, rather than the processbased interactions in case of Agent based models. The agent based models can be used in differentscenarios as they are more process oriented than the input data.


In this paper we would be using an agent based model, Agent-LUC for modelling the land-use land-cover changes in two districts of Godavari basin for the time period 1985-2005. The initial decade isused for understanding the phenomenon and making the model functions. While the changes in thesecond decade is simulated by the model and and validated against satellite derived land–use data of2005.

2 CASE STUDY REGION AND DATA

The Godavari basin extends over states of Maharashtra, Andhra Pradesh, Chhattisgarh and Odishain addition to smaller parts in Madhya Pradesh, Karnataka and Union territory of Puducherry having atotal area of 3,12,812 Sq.Kms. It lies between 73◦24’E to 83◦4’E longitudes and 16◦19’N to 22◦34’Nnorth latitudes and accounts for nearly 9.5% of the total geographical area of the country. The basin isbounded by Satmala hills, the Ajanta range and the Mahadeo hills on the north, by the Eastern Ghatson the south and the east and by the Western Ghats on the west. It covers a total of 44 districts in thebasin across 6 states.The major classes in the basin are croplands(57.73%), forest area (35%), Water (3%) and urbanarea(0.6 %).

Figure 1. 1985 Land use map of Godavari basin showing Adilabad(grey line) and East Godavari(blackline) districts.

The area of study for this project are two districts of Adilabad and East Godavari in the state of AndhraPradesh, India. The two districts have very different socio-economic conditions like per-capita incomeand educational levels in addition to varied land cover pattern. Adilabad is spread over 16128 Sq.kms.which accounts for 5.90% of the total area of the State, with forest (45% of the total area) as the majorland use class, followed by agriculture 35%) within the district.East Godavari on the other hand is spread over 10,807 Sq.kms. The District is located betweenNorthern latitudes of 16◦30’ and 18◦20’ and between the Eastern longitudes of 81◦30’ and 82◦30’,occupying a major portion of the delta area with Agriculture as main land cover over 40% of the district’stotal area.

The AGENT-LUC model requires the extensive input data about the land use, biophysical and socioeconomic drivers of the area to be simulated. The raster(Spatial) data used in this study include


District Built-up Crop Fallow Plantation needle mixed shrub Waste WaterAdilabad 0.55 38.88 7.25 0.13 34.05 8.26 5.19 0.17 5.22E.Godavari 1.20 40.36 2.17 9.53 24.21 13.82 0.89 0.17 7.35

Table 1. Percentage of total land use in Adilabad(top) and east godavari(bottom).

Figure 2. 1985 Land use map of adilabad and east godavari.

Land Use, Elevation, Slope, Road, Population, Crop, Soil and Administrative boundaries. The socioeconomic data used includes Regional Occupation, Population Growth, Farm gate prices, Road length,and Establishments data.

3 AGENT-LUC MODEL

3.1 Overview of the model

The AGENT-LUC model simulates the land-use landcover changes, as a result of the decision madeby the ”agent” of the land unit. The decision-making process of the agent is autonomous in decidingthe next course of action based on both micro and macro information available to him at a particularpoint of time and space.The prevailing bio-physical characterstics of hte land, the economic conditionand land use history along with demographic pattern in a given year are taken into consederation fordeciding the final land use. A large amount of datasets is needed to be managed and processed toget correct results out of the model. Various submodels of the model

1. Agent decision model. This is the main decision making module, that takes into account theeconomic returns along with the risk associated landuse assessment with the landuse changefor effectively allowing a landuse changeto occur. It also moderates abrupt and flip-flop changesin order to mimic human behaviour.

2. Landuse Assessment model. This consists of bio-physical crop model, urban landuse model,agricultural LU model, Rural Income model and demand assessment for land intensification orextensification. This model evaluates the current and immidiate past landuse so as to help de-velop trajectories of change.

3. Demographic Changes One of the fundamental principles in this model is that landuse changeis always accompanied by population shifts, either in terms of population growth and readjust-ment or migratory responses to local and regional factors. This model predicts the migrationwithin rural and between rural and urban areas. This model is described in detail in the next subsection.


3.2 Decision Making Framework

The framework allows the agent to make two major decisions - change in the land use(whethertocontinue the current land use or undergo a change) and change in the population(relocate some of thepopulation and drive land use changes in subsequent time frames). The various factors that drive thedecisions are economic, demographic and the land-use history.

1. Land Use Change ”Profit maximization” and ”Risk Minimization” are the basic principles whichhelp in deciding land use for a given land unit within its neighbourhood context. The age distri-bution and educational levels of the population are used to derive behavourial patterns that arelaible to influence decision making process. Also, model takes into account external influencesthat are likely to effect shifts in agricultural patterns. The model outputs a landuse map at theend of each year, to keep record of land use history and provide time series data of land usechanges.

2. Migration Model This submodel gives out a migration map at the end of each year, and is usedto calculate population density distribution over the area of interest.While as part of Urban migration, Rural to urban migration and natural population growth rateof Urban area are considered, the rural migration is an outcome of household movements. thelater leads to the development of new or existing agricultural areas in hitherto to unexplored orless-pressured land units. As can be seen in figure 3, landuse extensification can lead to a rangeof possible outcomes. In this model other physical and accessibility constraints are consideredto guide it.

Figure 3. Sample illustration showing possible outcomes of a land use pattern change.

4 RESULTS

The model run is over the two districts at a spatial grid size of 1km with the initial decade 1985-1995used to tune the model and changes in the land use of second decade 1995-2005 were simulated toget the output LU map of 2005.

4.1 Output of the model

As can be seen from the graphs in figure 6 the built up area in Adilabad increased form 150 sq. kms. in1995 to 210 sq kms in 2005 where as it remained the same 160sq kms in case of East Godavari. Thecroplands increased in Adilabad to 6740 sq kms from 6651 in 1995, and remained the same in EastGodavari 3683kms. The decidous and mixed forest area is decreased in Adilabad and the same inEast Godavari. The shrub land in Adilabad showed a decline of 210 sq. kms. The rest of the land-useclass change patterns are displayed in the figure.


Figure 4. Land Use of Adilabad in 2005 and predicted output of 2005

Figure 5. Land Use of East Godavari in 2005 and predicted output of 2005

4.2 Validation of the model

The output of the model was then compared with the satellite derived land-use map of 2005 to assessthe model performance. Figure 7 shows the ratio of predicted versus actual landuse for each of thecategories in the two districts. As can be seen, while there is a good agreement over the major classesof our interest.

5 DISCUSSIONS AND CONCLUSIONS

While the model performance in the district of adilabad is quite good, the same is true only for majorland uses in East Godavari. Population pressures driving the change can be seen in Adilabad whilethe near saturated landscape of East Godavari does not provide for significant changes in landuse.One of the limitations of this work is the lack of detailed, sub district level of socio economic data thatwould have helped capture the intra-district variability. The later would have given better insights intothe kind of agent interactions within the spatial land-use units.The model doesnot explictily account for changes in the forest classes and this needs to be incorpo-rated to understand the interactions at forest fringes.Though the model suitability is established by this work, there is a need to further refine the agent


Figure 6. Pattern of major land use changes in Adilabad(blue line) and Godavari(red line) districts.

Figure 7. Validating the output by comparing output and predicted class areas.

responses based on intra and inter land-use dynamics to achieve more robust understandings of thefactors at play.Further work will involve application of the model over the entire basin to assess the regional factorsthat might drive land-use changes on the Godavari basin. Policy related inputs will also be incorporatedto develop scenario based outcomes, so as to use such landuse models as effective decision supportsystems. Also the interaction of water and land use changes needs to be modeled to understand the


role of the resources within the context of this major river basin.

ACKNOWLEDGMENTS

We would like to thank Indian Institute of Remote Sensing, Dehradun and Indian Space ResearchOrganization, Bangalore India for their generous support in terms of appropriate data and projectfunding, without which this work would not have been possible.

REFERENCES

Fischer, G. and Sun, L.X. (2001). Model based analysis of future landuse development in China.Agriculture, Ecosystems and Environment, 85: 163–176.

Geoghegan J., Villar S.C., Klepeis P., Mendoza P.M., Ogneva-Himmelberger Y., Chowdhury R.R.,Turner II B.L. and Vance C. (2001). Modeling Tropical Deforestation in the Southern Yucatan Penin-sular Region: Comparing Survey and Satellite Data. Agriculture, Ecosystems and Environment, 85:25–46.

Mertens, B. and Lambin, E.F. (2000). Land-cover-change trajectories in southern cameroon. Annalsof the Association of American Geographers, 90: 467–494.

Pijanowski, B.C., Gage, S.H., Long, D.T. and Cooper W.C. (2000). A land transformation model for theSaginaw Bay Watershed. Landscape Ecology: A Top Down Approach, Lewis Publishing.

Postel, S. (1997). Dividing the Waters. In Technology Review.

Rajan KS and Shibasaki R. (2001). A GIS Based Integrated land Use/Cover Change Model to StudyAgricultural and Urban Land Use Changes. In 22nd Asian Conference on Remote Sensing.

Riebsame, W.E. and Parton, W.J.(1994). Integrated modeling of land use and cover change. Bio-science, 44:350–357.

Sanders L., Pumain D., Mathian H., Guerin-Pace F. and Bura S. (1997). SIMPOP: a multiagent systemfor the study of urbanism. Envrionment and Planning B, 24: 287–305.

Schneider, L.C. and Pontius, R.G. (2001). Modeling land-use change in the Ipswich Watershed, Mas-sachusetts, USA. Agriculture, Ecosystems and Environment, 85: 83–94.

Serneels, S. and Lambin, E.F. (2001). Proximate causes of land use change in Narok district Kenya: aspatial statistical model. Agriculture, Ecosystems and Environment, 85: 65–81.

Veldkamp A. and Fresco L.O. (1996). CLUE-CR: an integrated multiscale model to simulate land usechange scenarios in Costa Rica. Ecological Modelling, 91: 231–248.

Wear, D.N. and Bolstad, P.(1998). Land-use changes in Southern Appalachian Landscapes: spatialanalysis and forecast evaluation. Ecosystems, 1:575–594.

Documents

Modelling Land-Use Changes in Godavari River Basin : A