INTRODUCTION

Human induced land use and land cover (LULC) changes  have  been  the major drivers for the changes in local and  global  environments. Land cover  dynamics  involving conversion of natural resources (vegetation, water bodies, green spaces)  into urban space have affected various natural and ecological  process  .  Urbanisation is a dynamic complex phenomenon involving large scale changes in the land uses at local levels. Analyses of changes in land uses in urban environments provide a historical perspective of land use and give an opportunity to assess the spatial patterns, correlation, trends, rate and impacts of the change, which would help in better regional planning and good governance of the region (Ramachandra, et al., 2012). Urban  growth  is  a  spatial  and  demographic  process,  involving concentration of  human population to the land area which has high economy (Bhatta, et al.,  2010a;  Luck  &  Wu,   2002; Ramachandra et al., 2012). 

Urban  growth  pattern, have  a  direct  influence  on  urban  development process,  which extends its influence on the neighborhood  (Bhatta,  2009; Nelson,  2010), leading to Urban  sprawl,  which is often referred as peri-urban growth. Urban sprawl refers to a  small clusters of medium to low-density urban  growth in the outskirts without proper basic amenities (Bhatta et al., 2010b;  Ramachandra et al., 2012;  Petrov et al., 2009; Sudhira et al., 2004).  This  form of  peri urban  low density growth apart from lacking basic amenities also have a number  of  social,  economic  and  environmental disadvantages  (Bhatta  et  al.,  2010b;  Ramachandra et al., 2012).

A quantitative and qualitative analysis of the landscape structure is essential to analyse of the patterns of landuse change. Thematic land-use and land-cover maps generated allow us to quantify characteristics such landscape heterogeneity (Baldwin etal., 2004) and landscape fragmentation (Benedek et al., 2011; Gao& Li, 2011; Sudhira et al., 2004). Spatio-temporal data (Remote  Sensing  (RS)  data) with Geographic  Information  System  (GIS)  are helpful in  data acquisition  and  analysis  of  LULC  changes and for qualitative and quantitative results to understand the changes  (Ramachandra et al., 2012; Sudhira et al., 2003).  Temporal RS  data has been used to analyze  and understand the changes and  impacts  of  human activities on the natural ecosystem  (Yang et al., 2003; Herold et al., 2005;  Cowen and Jensen, 1998; Xu  et  al.,  2005;  Berberoglu  and  Akin,  2009). Urban  growth  is captured based on spatial  configuration and  its  dynamics  (Muller et al.,  2010;  Seto&Fragkias, 2005;  Xian  &  Crane,  2005; Sudhira et al., 2003). Spatial metrics have been used for describing landscape structure (McGarigal, 2002; McGarigal et al., 2002; Sudhira et al., 2004; Ramachandra et al., 2012) and for a wide range of applications, including the  assessments of land-use change (Iverson, 1988 ; Turner &Ruscher, 1988; Ramchandra et al., 2012 ), required for landscape planning and management (BotequilhaLeitão& Ahern, 2002), detection of changes in vegetation patterns (Fernandez, Aguiar& Ferreira, 2011; Kelly et al., 2011), changes in landscape structure (Pocas et al., 2011; Ramachandra et al., 2012, Bharath et al., 2012 ), for assessing the impacts of urbanization on the landscape ( Gao& Li, 2011; Li et al., 2010; Ramachandra et al., 2012; Sudhira, 2004, Bharath et al., 2012). Common  spatial  metrics have been computed for  describing  the structural  characteristics  and  growth  patterns  of  the built-up  area.  Herold et al.,  (2003),   for  instance,  used  spatial  metrics  to  characterize urban  growth  patterns  in  four  administrative  regions  of  Santa  Barbara.  Calculation  of  the  metrics  for  each  region  was   based  on  a visually  interpreted  land-use  map   representing  the  landscape  as patches  of  a  built  and  non-built  class. Ramachandra et al.(2012) and Bharath et al., (2012) have  examined  land-use  changes encompassing the urban area and peri urban area  using spatial metrics at  the class level. This work adopted gradient and direction analysis to locate and understand the local dynamics of changes in urban pattern. Further using Concentric  buffer  zones  (Seto&Fragkias,  2005, Settur et al., 2012) , transects  or rectangular  sample  plots (Weng,  2007, Luck  &  Wu,   2002) were also used for the sprawl analysis.  Spatial metrics  have  been proved as a valuable tool in comparing  urban  form  and  land-use dynamics (Huang et al.,  2007;  Schwarz,  2010). The review illustrates thatsignificant   research  contributions  ranging  from  gradient  analyses  to  geospatial  tool  applications  have  been  made  to understandthe  urban  growth  pattern,  quantification of  complex  patterns  or  processes  of  urban  growth  (Dietzel et al.,  2005;  Herold et al., 2003;  Peng  et  al.,  2010; Ramachandra et al., 2012).

Human induced land use and land cover (LULC) changes  have  been  the major drivers for the changes in local and  global  environments. Land cover  dynamics  involving conversion of natural resources (vegetation, water bodies, green spaces)  into urban space have affected various natural and ecological  process  .  Urbanisation is a dynamic complex phenomenon involving large scale changes in the land uses at local levels. Analyses of changes in land uses in urban environments provide a historical perspective of land use and give an opportunity to assess the spatial patterns, correlation, trends, rate and impacts of the change, which would help in better regional planning and good governance of the region (Ramachandra, et al., 2012). Urban  growth  is  a  spatial  and  demographic  process,  involving concentration of  human population to the land area which has high economy (Bhatta, et al.,  2010a;  Luck  &  Wu,   2002; Ramachandra et al., 2012). 

Urban  growth  pattern, have  a  direct  influence  on  urban  development process,  which extends its influence on the neighborhood  (Bhatta,  2009; Nelson,  2010), leading to Urban  sprawl,  which is often referred as peri-urban growth. Urban sprawl refers to a  small clusters of medium to low-density urban  growth in the outskirts without proper basic amenities (Bhatta et al., 2010b;  Ramachandra et al., 2012;  Petrov et al., 2009; Sudhira et al., 2004).  This  form of  peri urban  low density growth apart from lacking basic amenities also have a number  of  social,  economic  and  environmental disadvantages  (Bhatta  et  al.,  2010b;  Ramachandra et al., 2012).

A quantitative and qualitative analysis of the landscape structure is essential to analyse of the patterns of landuse change. Thematic land-use and land-cover maps generated allow us to quantify characteristics such landscape heterogeneity (Baldwin etal., 2004) and landscape fragmentation (Benedek et al., 2011; Gao& Li, 2011; Sudhira et al., 2004). Spatio-temporal data (Remote  Sensing  (RS)  data) with Geographic  Information  System  (GIS)  are helpful in  data acquisition  and  analysis  of  LULC  changes and for qualitative and quantitative results to understand the changes  (Ramachandra et al., 2012; Sudhira et al., 2003).  Temporal RS  data has been used to analyze  and understand the changes and  impacts  of  human activities on the natural ecosystem  (Yang et al., 2003; Herold et al., 2005;  Cowen and Jensen, 1998; Xu  et  al.,  2005;  Berberoglu  and  Akin,  2009). Urban  growth  is captured based on spatial  configuration and  its  dynamics  (Muller et al.,  2010;  Seto&Fragkias, 2005;  Xian  &  Crane,  2005; Sudhira et al., 2003). Spatial metrics have been used for describing landscape structure (McGarigal, 2002; McGarigal et al., 2002; Sudhira et al., 2004; Ramachandra et al., 2012) and for a wide range of applications, including the  assessments of land-use change (Iverson, 1988 ; Turner &Ruscher, 1988; Ramchandra et al., 2012 ), required for landscape planning and management (BotequilhaLeitão& Ahern, 2002), detection of changes in vegetation patterns (Fernandez, Aguiar& Ferreira, 2011; Kelly et al., 2011), changes in landscape structure (Pocas et al., 2011; Ramachandra et al., 2012, Bharath et al., 2012 ), for assessing the impacts of urbanization on the landscape ( Gao& Li, 2011; Li et al., 2010; Ramachandra et al., 2012; Sudhira, 2004, Bharath et al., 2012). Common  spatial  metrics have been computed for  describing  the structural  characteristics  and  growth  patterns  of  the built-up  area.  Herold et al.,  (2003),   for  instance,  used  spatial  metrics  to  characterize urban  growth  patterns  in  four  administrative  regions  of  Santa  Barbara.  Calculation  of  the  metrics  for  each  region  was   based  on  a visually  interpreted  land-use  map   representing  the  landscape  as patches  of  a  built  and  non-built  class. Ramachandra et al.(2012) and Bharath et al., (2012) have examined land-use changes encompassing the urban area and peri urban area using spatial metrics at the class level. This work adopted gradient and direction analysis to locate and understand the local dynamics of changes in urban pattern. Further using Concentric  buffer  zones  (Seto&Fragkias,  2005, Settur et al., 2012) , transects  or rectangular  sample  plots (Weng,  2007, Luck  &  Wu,   2002) were also used for the sprawl analysis.  Spatial metrics have been proved as a valuable tool in comparing urban form and land-use dynamics (Huang et al., 2007; Schwarz, 2010). The review illustrates thatsignificant   research  contributions  ranging  from  gradient  analyses  to  geospatial  tool  applications  have  been  made  to understandthe  urban  growth  pattern,  quantification of  complex  patterns  or  processes  of  urban  growth  (Dietzel et al.,  2005;  Herold et al., 2003;  Peng  et  al.,  2010; Ramachandra et al., 2012).

This communication analysesthe growth pattern of a developing city in Karnataka State, India. The region has large neighborhood of various classes with diverse landscape patterns.   The  objectives  of the study are   (a)  to  understand the land cover and land use dynamics using temporal remote sensing data, b) quantify  urban  growth  ,  (b) to understand the urban growth patterns in different locations using gradients and (d) to assess the pattern of growth over past two decades using spatial metrics over gradient.