INTERNATIONAL JOURNAL OF GEOMATICS AND GEOSCIENCES Volume 4, No 1, 2013 Copyright 2010 All rights reserved Integrated Publishing services Research article ISSN 0976 4380 Spatio-temporal analysis of land use - land cover changes in Delhi using remote sensing and GIS techniques Anirban Muhopadhyay 1, Sandip Muherjee 2, Garg RD 3, Tuhin Ghosh 1 1- School of Oceanographic Studies, Jadavpur University, Kolata-700032 2- Department of Natural Resources, TERI University, New Delhi 3- Department of Civil Engineering, Indian Institute of Technology (IIT), Rooree anirban_iirs@yahoocom ABSTRACT Temporal land cover changes have a strong effect on the urban environment and its surroundings In the present study, Delhi metropolitan area has been considered for the detection of land cover changes using Landsat images of 1989 and 2011 Emphasis has been given to identify the extent of urban expansion duly responsible for land cover changes Remote sensing and GIS techniques have that potential to analyze this change for the time span of 22 years Imageries have been classified digitally using Maximum Lielihood Classification (MLC) algorithm and have been validated through the process of accuracy assessment Here the overall accuracy achieved for classified images is more that 80% Areal change matrix and transitional probability matrix have also applied to identify the changes in land cover It is found that urban area, fallow land and have changed drastically in Delhi Keywords: Land cover changes; Remote sensing and GIS; Maximum Lielihood Classification 1 Introduction Land cover of any metropolitan city is changing rapidly due to rapid population growth and urbanization Peripheral or suburban area of metropolitan city is growing at a faster rate Sparse, wetland, forest and water body are being changed into built-up area, to satisfy the demand of infrastructure and industry for the highly growing population Remote sensing is an important technique for studying land cover changes Satellite imageries provide the opportunity of rapid acquisition and time series data of land cover, useful to identify the spatiotemporal land cover changes and understanding its exorbitant impact on the environment Continuous changes in urban processes, especially the worldwide urban expansion have important impact on natural and human systems at all geographic scales (Miller and Small, 2003) Adverse impact of crowding, insufficient infrastructure and housing together with urban climate and ecology related problems in urban set up requires constant monitoring Availability of high resolution satellite data provides opportunity for acquiring detailed spatial information for identifying and monitoring a number of environmental issues of urban regions at desirable spatio-temporal scales (Miller and Small, 2003) Urban environment represents one of the most important areas for remote sensing analysis due to the high spatial and spectral diversity of surface features (Matav et al, 2005) Analysis of spatio-temporal characteristics of land cover changes is also necessary to understand the pattern of urbanization Any variation with respect to urban expansion or and agricultural land may have profound impact on the people of the region Furthermore, such analysis can provide s sufficient information for decision-maing Submitted on August 2013 published on October 2013 213
Many researchers have studied land cover changes particularly in urban areas He et al (2000) and Zhang et al (2002) have used multi-temporal remote sensing images for analysing land cover in the metropolitan region of Beijing Liu and Zhou (2005) studied spatial pattern of urbanization and predicted the future urban expansion Coppin et al (2004) and Lu et al (2004) studied the geo-spatial information technology by investigating issues of such land cover change detection This study is based on the detection of change in the urban land cover around Delhi using temporal data of Landsat TM 5 (Thematic Mapper 5) Images of 1989 and 2011 have been considered to analyse the change in that area Detection of change by using images of two decades is very much effective as it is projecting the variation in the landscape Evaluation of the landscape change from 1989 to 2011 can provide important information on decision maing processes as it can indicate the increase in urban area or reduction in vegetated area, with significant impact on the environment Change in a particular land cover has been critically analysed and mapped in this study 2 Study area The study area of Delhi is extended from 28 40 N to 28 67 N latitude and from 77 14 E to 77 22 E longitude (Figure 1) The city has an areal extent of approximately 1483 Sq m and total population is around 1378 million according to 2001 census of India Delhi is located on the ban of river Yamuna and is bound by Haryana on the northern, western and southern side and Uttar Pradesh on the eastern side The climate is continental with temperature ranging from 45 C in summer to about 4 C in winter and has annual average rainfall of 750 to 1500 mm occurring over the years 3 Data used Figure 1: Study area In the present study two satellite imageries have been downloaded from the USGS website (http://glovisusgsgov) over the 22 years of time period (1989-2011) The details of the satellite imageries, acquisition date and resolution has shown in Table-1 Both the data sets are projected in UTM projection with zone number 43 and WGS 84 datum Satellite image of 1989 has been considered as the base data and image of 2011 is co-registered using first order polynomial model with that base data with 05 pixel (RMSE) accuracy 214
4 Methodology Table 1: Description of data Sensor type Acquisition date Spatial resolution Landsat TM 5 18 May 1989 30 m Landsat TM 5 5 March 2011 30 m 41 Land cover classification Although image analysis techniques are fast evolving, it requires discrete thematic land surface information from satellite imagery using classification based techniques (Prenzel and Treitz, 2005) In the present study, 30 m resolution TM data is used for the estimation of land cover in the urban area But high resolution data could be more useful to identify the complex land cover of Delhi However, Landsat imagery is useful for first level of classification of such urban land cover Major land cover types are considered and following classes have been chosen - 1 Built-up 2 Water body 3 Dense 4 Sparse and cultivated land 5 Fallow land Identification of the pattern of land cover changes in urban areas is extremely complex Satellite data with good spectral and radiometric resolution is very much essential for accurate land use and land cover classification Digital classification has received particular attention in the last few decades due to excessive growth in computing power Jain and Dubes (1988) illustrated a number of clustering techniques for digital classification Automated classification methods are mainly based on multi-spectral classification techniques (per-pixel classifiers) These processes assign a pixel to a class after determination of its statistical similarities, with respect to a set of classes in terms of reflectance (Gong et al, 1992) Maximum lielihood method of classification has been used in this study for classification MLC algorithm is one of the common parametric classifiers used for supervised classification The algorithm is used for computing the weighted distance or lielihood (D) of unnown measurement vector (X) belonging to one of the nown classes (Mc) which is based on the Bayesian equation [ 05 ln( cov )] [ 05( X M ) T (cov 1)( X M )] D = ln( a ) (1) c c The class is assigned with the unnown measurement vector in which it has the highest probability of belonging The advantage of the MLC is that it considers the variance covariance matrix within the class distributions In case of normally distributed data, MLC performs better than the other nown parametric classifiers, though the results may be unsatisfactory for the data not having normal distribution Maximum lielihood classification (Scott and Symons, 1971) is widely used in remote sensing where a pixel with the maximum lielihood is classified into the corresponding class For c c c 215
example, if there is m number of predefined classes, the class of a posteriori probability is described as ( x) P = m P i= 1 ( ) P( x) P( i) P ( i) where P() is described as the prior probability of class and P(x ) is conditional probability of observing x from class (probability density function) For normal distributions, the lielihood function, P(x ), can be expressed as (2) 1 1 T 1 L ( x) = exp ( x µ ) ( 1 x µ n 2 2 (2π ) 2 (3) where x = (x1, x2 xn) T is the vector of a pixel with n number of bands; L(x) is defined as the lielihood membership function of x belonging to class ; and µ = (µ1 µ2 µn) T is the mean of the th class; th class; = n 11 21 1 12 22 n2 1n 2n is the variance-covariance matrix of the class (4) nn 42 Accuracy assessment There are different types of approaches for assessing the accuracy level on classified thematic map (Congalton, 1991; Smits et al, 1999) Error matrix is considered as one of the common techniques for measuring the accuracy of thematic map (Story and Congalton, 1986; Smits et al, 1999; Foody, 2002) It measures a sample from a particular category of the classified map and the actual category is verified from the ground or reference data (Congalton, 1991) Overall accuracy can be calculated by the accuracy of individual classes with an expression as producer s accuracy and user s accuracy Producer s accuracy is acquired by dividing the number of correct sampling points in one class divided by the total number of points as derived from reference data, while user s accuracy can be obtained by dividing the correct classified units in a class by the total number of units that are classified in that particular class Kappa coefficient is a discrete multivariate technique which has been used since 1983 for evaluating the accuracy of remote sensing derived maps and error matrices These techniques are more suitable than continuous data The K-hat statistic is given below r r N X ij ( X j + * X + i ) Khat = 2 r r N ( X i+ * X + i ) (5) where r represents the number of rows in the matrix Xij that is the number of observations in X row i and column j, and i+ X and + i are the marginal totals for row and column the i and j respectively and N signifies the total number of observation (Lillesand et al, 2008) 216
43 Change detection Land cover change is a continuous phenomenon in any expanding metropolitan city Change detection can be done in different ways, lie image differencing, thematic land cover change and contextual change analysis In the present study thematic land cover change is carried out considering three decades and radiometric normalization is ignored Change matrix is produced to show the changes that have occurred due to urban expansion and subsequent growth Transitional Probability Matrix has been used to indicate the conversion or transfer of land cover from one category to another A flow diagram for the methodology is given in Figure 2 with the sequential explanation of the entire processing to identify land use land cover changes Figure 2: Methodology flow diagram showing the sequential processes to identify land use land covers changes 5 Results and discussion 51 Evaluation of land use - land cover map The satellite image of 1989 and 2011 are classified into five classes eg, built-up area, water body, dense, sparse and cultivated land and fallow land, and for each 217
class there are minimum 200 training samples In order to evaluate the land cover map of 1989 and 2011, error matrix has been prepared for a total of 120 sample points The error matrix is given in Table 2 Total classified samples are 2, 66, 21, 30 and 1 for water body, built-up, dense, sparse and wasteland respectively in 2011, selected by stratified random sampling technique Producer s and user s accuracy of 2011 are 100%, 8814%, 6071%, 80%, 100% and 100%, 7879%, 8095%, 80%, 100% for water, built-up, dense, sparse and wasteland respectively after calculation In 1989, classified points are 2, 39, 31, 45 and 3 for water body, built-up, dense, sparse and fallow land classes respectively and verified on the basis of classified samples and reference samples of which reference samples are 2, 38, 32, 45 and 3 respectively Producer s and user s accuracy for 1989 stands with 100%, 8421%, 7813%, 8222%, 100% and 100%, 8205%, 8205%, 8222%, 100% respectively Class name Table 2: Error matrix for the 1989 and 2011 images Reference totals Classified totals 2011 Number of correct points Producer s accuracy (%) User s accuracy (%) Water body 2 2 2 100 100 Built-up 59 66 52 8814 7879 Dense 28 21 17 6071 8095 Sparse 30 30 24 80 80 Waste land 1 1 1 100 100 Total 120 120 96 - - 1989 Water body 2 2 2 100 100 Built-up 38 39 32 8421 8205 Dense 32 31 25 7813 8205 Sparse 45 45 37 8222 8222 Waste land 3 3 3 100 100 Total 120 120 99 - - Table 3: Kappa statistics for individual class for the 1989 and 2011 images Class name Kappa statistics (2011) Kappa statistics (1989) Water body 1 1 Built-up 058 074 Dense 075 074 Sparse 073 072 Waste land 1 1 Kappa statistics in 2011 for water body, built-up, dense, sparse and wasteland are 1, 058, 075, 073 and 1 respectively while overall accuracy is 80% and in 1989, Kappa statistics are 1, 074, 074, 072 and 1 respectively and overall accuracy for 1989 is 8250% (Table 3 and Table 4) 218
Table 4: Kappa statistics and overall accuracy for the 1989 and 2011 images Category 2011 1989 Over all Kappa statistics 068 074 Over all accuracy (%) 80 8250 52 Land use land cover change analysis Land cover map of 1989 and 2011 is shown in the Figure 3 Classified map of 1989 is showing settlement in the eastern part of the Delhi city concentrated around the water channel of the region It is expanding in all directions to the north, south and west Eastern part of the built up area has already been occupied by the settlement except for a very small portion and is bloced by the boundary of Delhi Areas in the north and west have cover of maximum dense while sparse is found in the southern part mainly and mixed with dense and settlement in other regions 1989 2011 N Kilometers 0 5 10 20 30 LEGEND Water Built-up area Dense Sparse Fallow land Figure 3: Land use - land cover maps of 1989 and 2011 It is evident from the classified map of 2011 that the built up areas have increased to a greater extent than 1989 as most of the Delhi metropolitan area is presently occupied by settlements Vegetation has decreased to a large extent to provide more area for urban expansion The major loss due to urbanization is seen in the western, northern and southern boundary of Delhi during that particular time span Major portion of Delhi is dominated by the land use class built up area as found from the 2011 image analysis, and also gradual decrease in 219
and fallow lands in the surrounding area of the city observed (Figure 3) The land cover statistics of year 1989 and 2011 is given in the Table-5 which shows the sequence of change of individual land cover type over the period of 22 years Land cover classes Table 5: Land use land cover statistics Year 1989 Year 2011 Area (m 2 ) Area (%) Area (m 2 ) Area (%) Built-up area 37322 2517 67005 4518 Sparse Vegetation 55460 3740 43551 2937 Dense Vegetation 47048 3173 33325 2247 Water Body 2997 202 1497 101 Fallow/Waste land 5473 369 2921 197 Total 1483 100 1483 100 Urban or built up area of 1989 was only 2517% which has increased to 4518% in 2011 showing rapid rate of urbanization during last two decades Dense has decreased from 3173% in 1989 to 2247% in 2011 and sparse has reduced from 3740% in 1989 to 2937% in 2011 Figure 4: Areal distribution of land use land cover classes This ind of urban expansion is obvious due to the continuous migration towards Delhi from the surrounding regions and other states Similarly, fallow/waste land has also diminished gradually from 369% in 1989 to 197% in 2011 and water body from 202% in 1989 to 101% in 2011, indicating relatively slower rate of conversion last two decades With the rapid expansion of greater Delhi in the surrounding rural set up, is expected to be more diminishing nature Distribution of different land cover area is given in Figure 4 220
Figure 5: Change in land use - land cover from 1989 to 2011 Figure 5 shows the changes in the land cover classes which indicates sudden rise in the built up area in 2011 to nearly 670 m 2 from 373 m 2 in 1989 Except the huge rise in built up area, the other land cover classes are showing decrease in aerial extent While the sparse and dense are depicting considerable reduction, fallow land area reduced in medium scale and water body in a minor scale The land use land cover change matrix is provided in Table 6, which explains total area of land transferred from one class to another within the period of twenty two years The matrix shows that maximum change occurs from sparse and dense to built-up area Some water body and fallow land has converted to built-up area Dense vegetated land has also converted to sparse, may be because of deforestation Some of the fallow land has also converted vegetated land This matrix indicates that rate of urbanization is very high in Delhi region Table 6: Land use land cover change matrix Year 2011 1989 Water Built-up area Dense Sparse Fallow land Total Water 1478 556 332 631 0 2997 Built-up area 0 37322 0 0 0 37322 Dense 0 6396 26566 14086 0 47048 Sparse 041 21506 5897 27951 092 55429 Fallow land 005 1225 53 883 2829 5429 Total 1497 67005 33325 43551 2921 1483 Transitional probability matrix is given in Table - 7 Each row of the change matrix is summed and the values in each matrix element or transition state are divided by the sum of the rows to compute the transition probability values In each row, the probability values should sum to 10 The diagonal of the transition probability represent the self-replacement probabilities whereas the off- diagonal values indicate the probability of change occurring from one state to another state or class The transitional probability matrix shows that built-up area is the highest consistent land cover because the probability value is 10 All other classes are having the 50% 221
of self-replacement probabilities which signifies that water body, dense, sparse and fallow land have the high probability to convert into built-up areas Table 7: Transitional probability matrix of land cover Year 2011 1989 Water Built-up area Dense Sparse Fallow land Water 049 018 011 021 000 Built-up area 000 100 000 000 000 Dense 000 013 056 029 000 Sparse 007 038 010 050 001 Fallow land 000 022 009 016 052 53 Conclusion Present study of Delhi land cover from 1989 to 2011 shows rapid change in the landscape as there is high growth in the built up area only within twenty two years Built-up areas have occupied the sparse and densely vegetated lands while fallow land has reduced marginally and water body is showing almost stagnant condition over time Urban built-up area has extended outwards from the central eastern part to the rest of the region and has grabbed most of the areas in northern, western and southern part Eastern part is rather saturated from the beginning as it mars the limit or boundary of the Delhi metropolitan area and if this trend of growth continues then most of the vegetated areas will be occupied by built up in near future which may create a threat to environment 6 References 1 Congalton, RG, (1991), A review of assessing the accuracy of classifications of remotely sensed data Remote sensing of environment, 37, pp 35 46 2 Coppin, P, Joncheere, I, Nacaerts, K, Muys, B and Lambin, E, (2004), Digital change detection methods in ecosystem monitoring: a review International journal of remote sensing, 25(9), pp 1565 1596 3 Foody, GM, (2002), Status of land cover classification accuracy assessment Remote sensing of environment, 80 (1), pp 185 201 4 Gong, P, Marceau, DJ and Howarth, PJ, (1992), A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data Remote sensing of environment, 40 (2), pp 137 151 5 He, CY, Shi, PJ, Chen, J and Zhou, YY, (2000), A study on landuse/cover change in Beijing area Geographical research, 20(6), pp 679 687 6 Jain, A and Dubes, R, (1988), Algorithms for Clustering Data Prentice-Hall, p 320 222
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