SPATIO-TEMPORAL ASSESSMENT OF URBAN GROWTH IMPACT IN PUNE CITY USING REMOTELY SENSED DATA

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1 SPATIO-TEMPORAL ASSESSMENT OF URBAN GROWTH IMPACT IN PUNE CITY USING REMOTELY SENSED DATA Piyush Yadav, Shailesh Deshpande TATA Research Development and Design Centre, 54-B, Hadapsar Industrial Area, Pune , Maharashtra, India {piyush.yadav1, KEY WORDS: Land Use Land Cover (LULC), Support Vector Machine, VIS Classification, Urban Growth ABSTRACT: Land Use Land Cover (LULC) changes over the years have been one of the standard methods for determining the impact of anthropogenic activities in a given region. Comparative assessment of LULC changes is performed predominantly using a remotely sensed data and socio economic data from other sources. This paper presents the land use land cover changes taken place in Pune city in past one decade. Specific goals are to identify impervious surface increment, green cover loss, and natural drainage loss. We have used LANDSAT 7 imagery from 2001 to After correcting images for atmospheric effects and line striping problem, we used support vector machine (SVM) to classify each image into different classes. Various LULC classes namely forest, agriculture, residential, industrial, water bodies, open areas etc. were further grouped into broad level classes such as vegetation, impervious surface, and soil (VIS). We performed growth prediction of urban sprawl using Land Change Modeler in Terrset module. The lost drainage cover assessment was done using DEM image obtained from CARTOSAT-1. Various LULC assessment result shows that there was a significant increase of 13.74% in impervious surface, 14.27% combine loss of soil and vegetation and 5.30% loss of natural drainage network. 1. INTRODUCTION India is one of the most rapidly growing economies in the world (IMF, 2015). With the advent in the socio economic growth, its social fabric is getting affected with some major problems like population explosion, migration etc. which has led to an unsustainable growth of its various major cities. This leads to an adverse impact on our fragile ecosystem which is becoming increasingly degraded with time. Thus, there is a pressing need to restore the ecological balance to facilitate the sustainable development and conserve these natural resources. Pune is one of the Tier 1 cities of India (Wiki, 2015) and has undergone rapid development in last decade. The presence of various industries has led to increased migration of people to Pune from different parts of India for better employment opportunities. This has led to a rapid urbanization of the city within a short span of time. The sudden increase in the urban population and inadequate planning and development has created an immense pressure on cities natural and economic resources. Various townships and building have been raised over low lying slopes, on small water channels and agriculture lands. After the concretization of main city, urban sprawl is now increasing in the outskirt area by clearing forests, flattening down of small hills and building over flat agricultural lands. The loss of natural drainage, green cover and rise in impervious surface has increased the vulnerability of the city to flash floods during rainy season. This paper presents the LULC changes taken place in Pune city and gives detail account of impervious surface increment, green cover loss, and natural drainage loss. We have used LANDSAT 7 imagery from 2001 to 2014 (USGS, 2015), digital elevation image from CARTOSAT-1 (BHUVAN, 2015) and road data from the open street maps (OPENSTREETMAP, 2015). The paper is divided into four sections: 1) Materials and Methods: explains about study area, data collection and its preprocessing and classification methodology 2) Experimental Results: gives statistics about assessment results of various classes like VIS and river channel network analysis, 3) Conclusion and 4) References.

2 2. MATERIALS AND METHODS LULC analysis is an important study for the future planning of a particular place or city. This is a study which includes both spatio-temporal data and thus can reveal some of the interesting and complex geographical processes and phenomenon (Yadav, Deshpande, & Sengupta, 2015) of that place. Thus it is of wide importance to have robust data in order to analyze these changes. This section presents the area of study and various steps involved for the classification and analysis of data to assess LULC changes. 2.1 Study Area Pune, a Tier 1 city is situated in the state of Maharashtra, India. It is located 560m above sea level and is the part of Deccan plateau region of India (Wiki-Pune, 2015). The city lies in the lap of Sahyadri mountain ranges of Western Ghats. Pune is famous for its IT and Automobile industry and is the hub of various research institutes of India. The study area is core urban Pune city which consists of Pune Chinchwad Municipal Corporation (PCMC). We have considered 45x45 square km area bounding box with top leftmost corner as (Lat: , Long: ) and bottom rightmost corner as (Lat: , Long: ) covering all the important physical features and landmark of the city (Figure 1). Figure 1 Left to Right: Pune Location on India Map and Study Area for Pune City (Red Rectangle) 2.2 Data Collection And Preprocessing The assessment of LULC changes have been performed by integrating various raster and vector data acquired from different data sources. LULC change is a complex phenomenon which is noticeable at times only in a long span of time. So, we have collected the images of past 14 years ranging from years 2001 to 2014 to understand the complex changes occurred due to various socio-economic and other factors. Since, the data collected is of different format and resolution, so for better assessment, it is vital to convert them to same spatial resolution and format. Thus, preprocessing of data becomes an important step prior to LULC classification. Figure 4 depicts various preprocessing steps to get the final data for classification LANDSAT 7 Images Past 14 year s images of targeted area have been collected from month of April and May. The significance of these months is that because it is an onset of summer season in Pune. So there are less chances of cloud cover and thus, clear visibility leads to good quality of images. The probability of getting open area also increases as lot of small grasses on land surface dry up exposing bare open grounds. The images collected, have been acquired from LANDSAT 7 satellite with Enhanced Thematic Mapper (ETM) sensors (USGS, 2015). This is a 1500X1500 pixels seven band multispectral image with a 30 m spatial resolution. Various preprocessing techniques have been applied for the correction of Images.

3 SLC Correction: In 2003, Scan Line Corrector (SLC) of ETM sensor got failed leading to black scan lines on the images (WIKI-LANDSAT7, 2015). We have used the sliding window approach to remove these lines as shown in Figure 2. Suppose we have an image array in which Class 5 represents SLC pixel. In order to remove this pixel a sliding window of 9X9 pixels have been used which will traverse on the whole image array. The central pixel of moving window (5 th pixel), is replaced with the class having highest mode i.e. the highest number of count. The size of window may vary depending on the width of SLC lines Classs Mode Count Figure 2 Sliding Window Approach to remove the Scan Lines due to SLC failure Atmospheric Correction: Electromagnetic radiation captured by the satellite sensors is skewed because of the atmospheric interference due to phenomenon such as scattering, dispersion, etc. Atmospheric correction of LANDSAT images was implemented by subtracting digital number (DN) value of image from the DN value of water pixel of band 4 of LANDSAT 7. This is done because band 4 is a near-infrared band which has very low water leaving radiance (Cracknell, 2007). Then the DN values are converted to spectral radiance using Eq. (1) (Kaufman, 1989). L = L min + ( L max 254 L min ) x DN (1) 255 In Eq. (1) L is the spectral radiance, L min is the minimum spectral radiance, L max is the maximum spectral radiance, and DN is the recorded digital number at the sensor. For clear LANDSAT images, solar correction of the images was done by converting spectral radiance to exoatmospheric reflectance using the following Eq. (2) (Kaufman, 1989) π L λ d 2 ρ p = (2) ESUN λ cosθ s where ρ p is the unit less planetary reflectance, L λ is the spectral radiance at the sensor s aperture,d is earth to Sun distance in astronomical units, ESUN λ is the mean solar exoatmospheric irradiance, and θ s is the solar zenith angle in degrees Digital Elevation Image The Digital Elevation Model (DEM) Images have been collected from CARTOSAT-1 satellite of Indian Space Research Organization (ISRO). The satellite uses PAN (2.5m) stereo data sensor with spatial resolution of 1 arc sec (BHUVAN, 2015). Since slopes play an important role in urban development as it is easy to build on low lying slope as compared to steep ones. The DEM image acquired is converted to slope using QGIS DEM Terrain models (GDALDEM, 2015) (Figure 3). Thus the slope image is used in urban growth prediction and in river channel network assessment.

4 2.2.3 Road Network Image Figure 3 Left to Right: Pune Hill Shade Image and Primary Road Network Roads act as arteries for economic development of any area. It is of highly plausible that any infrastructure development starts in the region which is having good connectivity of road network. In the present work, the road network is obtained using Open Street Maps (OPENSTREETMAP, 2015). We have considered only primary and trunk roads for our assessment because of their significant impact on the economy (Figure 3). The road network is used as one of the input in urban growth prediction. The road network is converted to raster distances using QGIS proximity raster distance (GDAL_PROXIMITY, 2015). 2.3 Classification Methodology Figure 4 Data Preprocessing Steps to get Corrected Images After preprocessing, the final corrected LANDSAT Image is classified into 7 class s viz. 1) forest canopy 2) agriculture 3) residential areas 4) common open area 5) bright soil 6) water and 7) industrial areas. The supervised classification technique is applied to classify these images. In order to enhance the accuracy measure Texture is added as an extra feature in the image which is determined by using Local boundary (LBP) pattern algorithm (Scikit-LBP, 2015). Initially, various training data sets were extracted for different classes. These training data were then trained over classifiers to predict the classified image. Three different classifiers viz. SVM, Gaussian Naïve Bayes (NB) and Nearest Neighbors were used to predict the images (Cortes, Corinna, & Vapnik, 1995), (NB, 2015), (Neighbor, 2015). The confusion matrix of each classifier was created separately to find their classification accuracy. As shown in Table 1, SVM gives the best classification result with an accuracy ranges from 84%-91%.

5 Table 1 Overall Accuracy of Three Classifiers in different years (in %) Year SVM Gaussian NB Nearest Neighbors During classification there has been some confusion between impervious surface and bright soil because of resemblance of their spectral signatures. Thus, gross misclassification (less than 2%) has been corrected manually with the help of local knowledge and field surveys as required. Finally the multiple classes were merged into broad level VIS classes. The future prediction of impervious surface has been done by using Land Change Modeler (LCM) of TERRSET module (TERRSET-LCM, 2015). LCM is based on MARKOV chain model where it computes the temporal probability of classes between two years and then try to predict it for the future.it also takes DEM and road data as an extra input to calculate the urban change. The natural drainage network analysis has been done using Digital Elevation Model (DEM). The DEM image is converted to slope using QGIS DEM Terrain models (GDALDEM, 2015). The network of streams is identified using watershed model of GRASS module which takes DEM as an input to extract the river streams and its basin area (Watershed, 2015). Some of the drainage streams have been corrected manually by overlapping it on google earth images. Then the corrected stream networks have been laid over the various years of google images to find out the channel loss. 3. EXPERIMENTAL RESULTS 3.1 VIS Assessment The result shows that in past one decade ( ) Pune has gone under a rapid urbanization. The major development has happened in the outskirts of the city because of land availability. It can be easily evident from Figure 7 that most of the buildings have been constructed on the flat agricultural land. Figure 5 and Table 2 (a) shows the VIS changes happened in the past 14 years (2004 is not considered because of bad image quality). It can easily be seen that there is a consistent rise in the impervious surface. In 2001 the impervious surface was only 11.67% of the total study area. In 2009 it rose to 18.88% while in 2014 the built up area exceeds to 25.4%. Thus, there was increase of 13.74%, which means that the built up area has been increased more than twice from 2001 levels. Annual variation in soil and vegetation classes is because of the seasonal land cover changes of a given area. Corresponding increase in soil class is because of late summer images. Figure 5 shows that there is quite variation in soil (S) and vegetation (V) classes. This is due to the inter convergence between soil and vegetation classes. Since Pune lies in the rain shadow area of Western Ghats hence consists of Tropical Deciduous forests and small grasses. Sometime these small grasses remain green due to water abundance giving vegetation signature but sometime they become dry thus exposing bare soil.

6 Pixels Count Pixels Count Classes V I S W Figure 5 Vegetation, Impervious, Soil and Water Classification of Pune ( ) Years Figure 6 Analysis of Vegetation and Soil Clubbed Together ( ) Except the core forest area majority of land is exposed rocks, agricultural area, open grounds, and open areas with grass cover. Thus, for better understanding we have merged these two classes to understand their overall impact, as shown in Figure 6. It can be easily seen from the Table 2 (b) that in 2001 vegetation and soil consists of overall 87.54% of total area while it reduced to 79.78% in 2009 to 73.27% in Thus there was a total depletion of 14.27% of total non-impervious (soil and vegetation) resources. Percentage of area covered by water bodies remains unchanged throughout the period with very little fluctuations (Figure 5). Table 2: (a) Change in Impervious Surface in Pune (2001, 2009and 2014), (b) Change in Vegetation and Soil in Pune clubbed together (2001, 2009 and 2014) Impervious Pixels Vegetation and Soil Pixels Total Area Covered (Sq. Km) Percentage Area Covered (a) Total Area Covered (Sq. Km) Percentage Area Covered (b) Impervious Surface Soil Agriculture Lake/Reservoir Figure 7 Left to Right: Classified VIS images of Pune 2001, 2009 and 2014 respectively where 1) Pune City 2) Kothrud 3) Chakan and MIDC

7 We have predicted impervious surface for year The 2001 and 2014 classified image has been taken as the input with the road and slope data to calculate the transition probabilities in TERRSET module (TERRSET-LCM, 2015). In LCM various rules have been incorporated like water (reservoirs and lakes) is highly resistant to built up and impervious class is highly resistant to be converted to other class. Table 3 shows the transition probabilities between various VIS classes and their chances of conversion to other. Figure 8 shows the prediction of impervious surface for year 2020 along with chances of different areas to be converted to impervious surfaces. It can be seen that black area is having zero probability (Figure 8) i.e. it is completely resistant to change to impervious surface as it is already a built up area whereas orange to red area has high probability of changing to impervious surface. Orange area is normally an agricultural land and is under intense pressure to get converted to build up areas. 5.2 Channel Network Assessment Pune lies in the leeward side of Western Ghats of India. This Ghats section is drained with various small streams and river channels because of its uneven topography. Mula, Mutha and Indrayani are major rivers (Wiki-Pune, 2015) which flow from the main PCMC area. The channels of these rivers act as a lifeline for the city as it act as major feeder streams for various small reservoirs built across city for agricultural and drinking water purposes. With the rapid expansion of the urban conglomerate there has been rapid of extinction of these small channels. Various anthropogenic activities like encroachment, garbage dumping etc. on river embankments have abysmally reduced their carrying capacity. Figure 9 (a) and (b) shows various buildings, slums etc. have been raised over to these small streams reducing the effective drainage capacity of the natural drainage. This has made the city vulnerable to flash floods, water pollution etc. The average ground water is depleting with respect to previous years because of potential loss of aquifers (Groundwater Surveys and Development Agency, ). Increased impervious surface and increased runoff has reduced the total amount of water percolation leading to ground water depletion (Water Supply and Sanitation Department, 2011). Table 3 Transition Probability Matrix for various VISW classes Water Soil Impervious Vegetation Water Soil Impervious Vegetation Figure 8 Impervious Surface Prediction of Pune for year 2020

8 (a) (b) Figure 9 Left to Right: Google Earth image of various buildings raised on Small River streams (a) 2008 and 2015 (b) 2008 and 2015 Pune area has observed a total loss of 4.14% from the base year of (Table 4). It can be seen that there was loss 5.30% of channel network in areas having maximum urban penetration while loss of only 0.51% of streams with areas having low urban penetration. Black wide lines in Figure 10 shows the lost channel. Thus association between urbanization and loss natural drainage is evident. Table 4 Loss in the Riparian Channels Maximum Urban Least Urban Total Penetration Penetration Original River Channels(pixels) Channels Lost (pixels) Total Lost (%) Least Urban Penetration Maximum Urban Penetration Riparian Channels Impervious Surface Soil Agriculture Riparian Channels Channel Lost Reservoir/ Lake Figure 10 Left to Right: Original River Channel Network and Lost river Streams from 2002 to 2014

9 4. CONCLUSION The LANDSAT 7 image classification accuracy ranges from minimum 84.3% to maximum 91.04% with an average accuracy of % for overall 14 years. The classification result shows that there is prominent increase in the impervious surfaces: jump of 13.74% from 11.67% in 2001 to 25.41% in The increase in impervious surface has resulted in loss of urban vegetation (grass, tress cover etc.), agricultural land, and large open areas from 87.54% to 73.27% with a total loss of 14.27%. Our investigation indicates loss of 5.30% of natural drainage network over 12 years in urban areas and 0.51% loss in less urban areas because of various anthropogenic activities mainly development of new townships and utilities. In final the paper gives a detailed account of change in Land Use and Land Cover dynamics of the city. 5. REFERENCES BHUVAN. (2015, September 1). Bhuvan: Gateway of Indian Earth Observation. Retrieved September 1, 2015, from Bhuvan: Cortes, Corinna, & Vapnik, V. (1995). Support-vector networks. Machine learning 20(3) (pp ). Springer. Cracknell, A. (2007). Atmospheric Corrections to Passive Satellite Remote Sensing Data. In A. Cracknell, Introduction To Remote Sensing, Second Edition (p. 196). CRC Press. Retrieved September 1, 2015 Earth, G. (2015). Google Earth. Retrieved September 1, 2015, from Google Earth: GDAL_PROXIMITY. (2015). GDAL_PROXIMITY. Retrieved September 1, 2015, from GDAL: GDALDEM. (2015). GDALDEM. Retrieved September 1, 2015, from GDAL: Groundwater Surveys and Development Agency, P. ( ). Report On The Dynamic Ground Water Resources Of Maharashtra. Maharashtra: Ministry of Water Resources,Government of India. Retrieved September 1, 2015 IMF. (2015). IMF. Retrieved September 1, 2015, from International Monetary Funf: Kaufman, Y. J. (1989). The atmospheric effect on remote sensing and its correction. In Theory and applications of optical remote sensing (pp ). NB, S. G. (2015). Scikit- Guassian NB. Retrieved September 1, 2015, from Scikit: Neighbor, S. N. (2015). Scikit- Nearest Neighbor. Retrieved September 1, 2015, from Scikit: OPENSTREETMAP. (2015). OPENSTREETMAP. Retrieved September 1, 2015, from Scikit-LBP. (2015). Scikit-LBP. Retrieved September 1, 2015, from Scikit: TERRSET-LCM. (2015). TERRSET-LCM. Retrieved August 30, 2015, from Clark Labs: USGS. (2015). USGS. Retrieved September 1, 2015, from Earth Explorer: Water Supply and Sanitation Department, G. o. (2011). Districtwise list of villages included in Over Exploited /Critical mini watersheds after Reassement October 2011 (base year ). Pune: Groundwater Surveys and Development Agency. Retrieved September 2, 2015 Watershed, O.-G. (2015). GRASS Watershed. Retrieved August 26, 2015, from OSGEO: Wiki. (2015). Classification of Indian Cities. Retrieved September 8, 2015, from Wikipedia: WIKI-LANDSAT7. (2015). Landsat7. Retrieved September 1, 2015, from Wikipedia: Wiki-Pune. (2015). Wikipedia. Retrieved September 1, 2015, from Wikipedia: Yadav, P., Deshpande, S., & Sengupta, R. (2015). Animating Maps: Visual Analytics meets Geoweb 2.0. GeoComputation Dallas.