Introduction

Urbanisation is a dynamic process refers to the growth of urban population resulting in land use land cover (LULC) changes, being experienced by most of the developing nations. Recent projections indicate that the world population living in urban areas will reach 60 percentages by 2030 [1]. Urbanisation process involves changes in LULC, socioeconomic aspects including population density. Urban land use entails interactions of urban economic activities with environment, which further leads to expansion. The rapid and uncontrolled growth of the urbanising cities brings numerous changes in the structure and hence the functioning of landscape [2]. Urban form reveals the relationship between a city with its surroundings as well as the impact of human actions on the local environment within and around a city [3]. This necessitates planning at various stages to manage the urban growth while addressing economic development with the environment goals. Multi Resolution remote sensing data acquired through sensors mounted on Earth Observation Satellites (EOS) provides a synoptic and repetitive coverage of large areas through time. It is now possible to monitor and analyze urban expansion and land use changes in a timely and cost-effective way due to improvements in spatial, spectral, temporal and radiometric resolutions with analytical techniques [4]. However, there are technical challenges in retrieving accurate information of urban expansions with rapid land use changes. A major challenge in urban remote sensing data analysis is caused by the high heterogeneity and complexity of the urban environment in terms of its spatial and spectral characteristics. A successful implementation of remote sensing technique requires adequate consideration and understanding of these specific urban landscape characteristics in order to explore the capabilities and limitation of remote sensing data and to develop appropriate image analysis techniques [5]. Recently there has been an increased interest in the application of spatial metrics techniques in urban environment because of their capability in revealing the spatial component in landscape structure with the dynamics of ecology and growth process [6-9]. The analysis of temporal landscape structure would aid in accounting spatial implications of ecological processes [10]. Many spatial landscape properties can be quantified by using a set of metrics [5], [11-14]. In this context, spatial metrics are a very valuable tool for planners in understanding and accurately characterising urban processes and their consequences[5],[10],[15]. Spatial metrics have aided in landscape monitoring, including landscape changes [16-18], assessing impacts of management decisions and human activities [19-21]. A variety of landscape metrics have been proposed to characterize the spatial configuration of individual landscape class or the whole landscape base [22-25]. Compared to the other change detection techniques, the landscape metrics techniques are advantageous in capturing inherent spatial structure of landscape pattern and biophysical characteristics of these spatial dynamic [26].  Furthermore, spatial metrics have the potential for detailed analyses of thespatio-temporal patterns of urban change, and the interpretation and assessment of urbanisation process.

Land use dynamics detection using remote sensing data
Remote sensing data aids in detecting and analysing temporal changes occurring in the landscape. Availability of digital data offers cost effective solutions to map and monitor large areas. Remote sensing methods have been widely applied in mapping land surface features in urban areas [27]. Satellite based remote sensing offers a tremendous advantage over historical maps or air photos, as it provides consistent observations over a large geographical area, revealing explicit patterns of land cover and land use. It presents a synoptic view of the landscape at low cost [28]. Remote sensing also provides high-resolution datasets that are used to assess spatial structure and pattern through spatial metrics.

Landscape metrics analysis for landscape change detection
Landscape metrics or spatial metrics is based on the geometric properties of the landscape elements, are indicators widely used to measure several aspects of the landscape structure and spatial pattern, and their variation in space and time [12]. A variety of landscape metrics have been proposed to characterize the spatial configuration for the individual landscape class or the whole landscape. Scaling functions of the images describes the variations of different landscape pattern metrics with spatial resolutions [29-31]. Patch size and patch shape metrics have been widely used to assess patch fragmentation both at small and large scales [26]. Patch shape index acts as an indicator, which correlates with the basicparameter of individual patch, such as the area, perimeter, or perimeter–area ratio. However, these indices fail in reflecting the spatial location of patches within the landscape [25]. Heterogeneity based indices proposed subsequently aid in quantifying the spatial structures and organization within the landscape which was not quantified by patch shape index. Similarly, the proximity indices quantify the spatial context of patches in relation to their neighbors [32]. For example, the nearest-neighbor distance index distinguishes isolated distributions of small patches from the complex cluster configuration of larger patches [33]. Thus patch-based and heterogeneity-based indices highlight two aspects of spatial patterns, which are complement to each other. As landscape patterns possess both homogeneous and heterogeneous attributes, it is necessary to adopt both groups of indices for analysing spatial patterns of heterogeneous landscapes [34]. This illustrates that multi-resolution remote sensing data with spatial metrics provide more spatially consistent and detailed information about urban structures with the temporal changes, while allowing the improved representations for better understanding of heterogeneous characteristics of urban areas. This helps in assessing the impacts of unplanned developmental activities on the surrounding ecosystems.