Modelling and Prediction of Land Use Changes in Jodhpur City using Multi- Layer Perceptron Markov Techniques

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1 Modelling and Prediction of Land Use Changes in Jodhpur City using Multi- Layer Perceptron Markov Techniques S. L. Borana 1 and S. K. Yadav 2 1,2 (RSG, DL, Jodhpur , Rajasthan, India) Abstract: Land use change models techniques are effectively used in prediction of land use dynamics of the urban development. Landsat satellite data of 1990, 2000 and 2010 were used to analyse the current and predict future land use growth of Jodhpur city. The supervised classified images were used for future prediction of land use change and generation of projected map (year 2020 and 2030). Multi Layer Perceptron Markov (MLP_Markov) Model and IDRISI, Land Change Modeler (LCM) were used to analyze the land use and land cover changes between various classes during the period Using multi-layer perception, the five classes identified as built -up area, vegetation, mining area, water body and other area. The prediction of land use change was prepared on neural network built-in module and its validation. Keywords: Remote Sensing, Landsat Data, Land change modeler, Predicted Map. I. INTRODUCTION Unplanned fast development of cities causes troubles to urban surroundings and natural environment. A scientific awareness of urban expansion trends and possible urban development management should be achieved by remote sensing temporal data. Satellites images are the resources for exposure, quantification, and land use mapping (Abel El-kawy et al., 2011).The base data generated from remote sensing is used for spatial modeling of urban growth and its future prediction. Modelling studies have started using cellular automata and Markov chain (CA_Markov) together to give spatial measurement to Markov chain model which is fragile in spatial face. The CA_Markov has an capability to predict any transition with any number of categories. Thapa and Murayama (2011) use LCM for Nepal urban development modelling. They model years of 2020, 2030, 2040, and 2050 by observing and analyzing satellite images of 1991, 2001, and 2010 and by using historical, environmental, conservational scenarios. Sefyaniyan (2009) evaluates Isfahan land use changes ( ) and finds significant changes in agricultural lands. Recently, combination of Multi-layer perceptron (MLP) Neural Network and Markov chain modeling technique with Land Change Modeler (LCM), is used in future prediction of urban growth by many researchers for various cities. Study Area Jodhpur also known as sun city, is centrally situated in western region of the Rajasthan state with location at 26ºN 18' latitude and 73º E 04' and at an average altitude of 224m above mean sea leve (Fig.-1). l. In general, the contours are falling from North to South and from North to Southeast with maximum level of 370m and minimum of 210m. The present population is about 1.05 million and admeasures 230sq.km. Alongside, Jodhpur has been functioning as one of the engines powering the Rajasthan state economy. Page 14

Figure1: Study Area II. DATA USED AND METHODOLOGY Remote Sensing (RS) data scene (1990, 2000 and 2010 FCC & TCC) were used for analysis and interpretation of land use types in the study area.

2 Figure1: Study Area II. DATA USED AND METHODOLOGY Remote Sensing (RS) data scene (1990, 2000 and 2010 FCC & TCC) were used for analysis and interpretation of land use types in the study area. The Coordinate System use for projection of satellite images are WGS-1984 and UTM Zone-43N. with 30m spatial resolution were used in this study. The SoI maps, ground truth data were used for land use classification and accuracy analysis. Ancillary data, such as a digital elevation model (DEM), major road networks were also included into the analysis. ArcGIS and ERDAS Imagine software were used to achieve land use classification mapping in a multi-temporal approach. Image classification is carried out by using the supervised classification maximum likelihood method. Five land use types i.e. built -up area, vegetation, mining area, waterbody and other area have been identified in this study. For future prediction of land use change, Multi Layer Perceptron Markov (MLP_Markov) Model has been used for modelling of land dynamics. IDRISI Selva software with Land Change Modeler used in this paper for analysis of land use changes. Methodology adopted for this research is shown in the Figure 2. Page 15

Figure2: Methodology Flow Chart III. RESULT AND DISCUSSION Future Prediction and Analysis: MLP_Markov model has been selected for simulating land cover map of study area for the year 2020 & 2030.

Variables, Transition Potential Modeling, Markov Chain Analysis and Analysis of the Predicted Map.

3 Figure2: Methodology Flow Chart III. RESULT AND DISCUSSION Future Prediction and Analysis: MLP_Markov model has been selected for simulating land cover map of study area for the year 2020 & Various steps involved in prediction procedure are creating Boolean and Distance Images, generation of Land Cover Transition Image, Selecting Driving Variables, and Testing Potential of the Driving Variables, Transition Potential Modeling, Markov Chain Analysis and Analysis of the Predicted Map. Creating Boolean and Distance Images: Boolean images for land cover type of Built-up, Mining area, Vegetation & other are and driver have been prepared using linear type fuzzy membership function available in IDRISI; a value 255 indicates the highest suitability and a value 0 indicates the lowest suitability of that particular category. The values 1 represent the areas of interest and the values 0 represent the areas of no interest (Fig.3). Figure3: Boolean Images of each Land Cover Type Page 16

Distance images for each of these Boolean land cover images and drivers have been generated. These distance images are important to measure the suitability values for the pixels of land cover classes.

The lowest and highest values obtained from the distance images have been used as the input for fuzzy set membership analysis (Fig.4).

Transition from all land cover types to built up has been produced, considering the transitions from all other land cover types to only built up area(fig.5). Other changes have been ignored. Fig.6.

4 Distance images for each of these Boolean land cover images and drivers have been generated. These distance images are important to measure the suitability values for the pixels of land cover classes. The distance images are produced using simple Euclidean distance function which measures the distance between each cell from the featured image. The lowest and highest values obtained from the distance images have been used as the input for fuzzy set membership analysis (Fig.4). Figure4: Distance Images of each Land Cover Type (2010) Land Cover Transition Image: The basic concept of modelling with MLP neural network is to consider the change in built-up area over the years. Transition from all land cover types to built up has been produced, considering the transitions from all other land cover types to only built up area(fig.5). Other changes have been ignored. Fig.6. shows the land cover type is contributing more to net change in built-up area. It is found that other area is contributing most converting towards built-up area followed by vegetation. Figure5: Transition from All to Built-up Area ( ) Figure6: Contributors to Net Change by Built-up Area (Unit: per cent of Area) Page 17

Selection of Driving Variables: The issue of which variable affects the change to built-up area (2000-2010) has been considered at this stage.

Another important aspect is to find out the empirical likelihood of all changes for transforming into built-up areas based on the base image of the year 2000 (Fig.8). The highest value 0.

5 Selection of Driving Variables: The issue of which variable affects the change to built-up area ( ) has been considered at this stage. Therefore, distance from all to built-up area has been chosen (Fig.7). Another important aspect is to find out the empirical likelihood of all changes for transforming into built-up areas based on the base image of the year 2000 (Fig.8). The highest value 0.80 is showing a high probability of converting other land cover types to built-up area. Therefore, 5 driver variables have been selected for the model. These five driver variables are distance from all to built-up area, distance from water body, distance from vegetation, distance from other area and empirical likelihood image. Figure7: Distance Image of Transition from All to Builtup Area. ( ) Figure8: Empirical Likelihood Image ( ) Testing Potential of the Driving Variables The quantitative measures of the variables have been tested through Cramer s V. It is suggested that driving variables having Cramer s V of about 0.15 or higher are useful. Table1 shows that the potential explanatory values of the driving variables are useful (Cramer s V > 0.15). Table1: Cramer s V of the Driving Factors Driving Variable Class Overall V Built-up Other area Vegetation Water Body Distance from All to Built-up Distance from Other Area Distance from Vegetation Distance from Water Body Empirical Likelihood Image Page 18

Transition Potential Modeling The MLP running statistics gives a very high accuracy rate of 99.29% (Table-2).

The RMS error curve has also been found smooth and descent after running MLP neural network (Fig.9). It means the training result is satisfactory.

The dominance of water body and fallow land to built-up area type is clear here (Fig. 10).

area, 3- vegetation, 4-Water body) Markov Chain Analysis: Using this MLP neural network analysis, determine weights of the transitions is included

6 Transition Potential Modeling The MLP running statistics gives a very high accuracy rate of 99.29% (Table-2). The minimum number of cells that transitioned from 1990 to 2000 is The RMS error curve has also been found smooth and descent after running MLP neural network (Fig.9). It means the training result is satisfactory. Based on these running statistics four transition potential maps have been produced. The dominance of water body and fallow land to built-up area type is clear here (Fig. 10). Table2: Running Statistics of MLP Neural Network Figure9: RMS Error Monitoring Curve Figure10: Transition Potential Maps (1- Built up area, 2- Other area, 3- vegetation, 4-Water body) Markov Chain Analysis: Using this MLP neural network analysis, determine weights of the transitions is included in the matrix of probabilities of Markov Chain for future prediction. The transition probabilities are shown in Table-(3 &4).The final predicted map of 2020 and 2030 have been simulated through Markov chain analysis based on all these pieces of information from MLP neural network (Fig.11 &12). Table3: Transition Probabilities Grid for Markov Chain (2020) Page 19

Table4: Transition Probabilities Grid for Markov Chain (2030) Table5: Land Cover Area of Base map and projected map LU/ LC Area (km 2 ) Projected Area (km 2 ) 2010 2010 2020 2030 Built up 114.42 119.

61 Total 594 594 594 594 Figure11: Change in Area (%) over the Years (1990-2020) Analysis for Prediction Map: The predicted map of 2020 and 2030 reveals that 28 % and 34.

Similarly Gains in built-up area land cover type are prominent while most of the areas will be persistent (Fig.12). Slight loss in water body and vegetation cover types will also be found.

7 Table4: Transition Probabilities Grid for Markov Chain (2030) Table5: Land Cover Area of Base map and projected map LU/ LC Area (km 2 ) Projected Area (km 2 ) Built up Other area Vegetation Mining area Water body Total Figure11: Change in Area (%) over the Years ( ) Analysis for Prediction Map: The predicted map of 2020 and 2030 reveals that 28 % and 34.8% of the total area will be occupied by built-up area cover type respectively. On the other hand, water body and other area (fallow land) types are going to decrease. Similarly Gains in built-up area land cover type are prominent while most of the areas will be persistent (Fig.12). Slight loss in water body and vegetation cover types will also be found. But other area will decrease in good amount in near future. Long term predictions are not possible by applying the MLP_Markov model. It is giving errors in predicting land cover maps for long terms as the urban growth rate is very high in case of Jodhpur city. It means that the study area will reach its threshold limit in built-up area type by 2020 and Therefore, CA_Markov model is better option for long term prediction for this research. Figure12: MLP_Markov Projected Land Cover Map (2020 and 2030) Page 20

8 IV. CONCLUSION This study demonstrates different models to simulate the land cover change map of Jodhpur city. The best-fitted model has been selected based on various Kappa statistics values and also by implementing other model validation techniques. The Multi Layer Perceptron Markov (MLP_Markov) Model has been qualified as the most suitable model for this research work. Using the MLP_Markov model, the land cover map of 2020 & 2030 has been predicted. Using MLP_Markov model, Prediction map shows that 28% and 34.8% of the total study area will be converted into built-up area cover type in 2020 and 2030 respectively. Acknowledgment The authors are thankful to the director dl, Jodhpur and Head, Department of Mining Engineering, Jai Narain Vyas University, Jodhpur for help and encouragement during the study. V. 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Accuracy Assessment of Land Cover Classification in Jodhpur City Using Remote Sensing and GIS

Accuracy Assessment of Land Cover Classification in Jodhpur City Using Remote Sensing and GIS S.L. Borana 1, S.K.Yadav 1 Scientist, RSG, DL, Jodhpur, Rajasthan, India 1 Abstract: A This study examines