Introduction

Land use land cover (LULC) information of a region depicts the status of a landscape for environmental progression and sustainable development. Land cover configuration is stated as a unified reflection of the existing natural resources, dynamic natural processes whereas land use refers to the human induced changes in the land cover. LULC changes alter the homogeneous landscape into heterogeneous patches by natural processes or anthropogenic processes (Mertens et al. 2000). Landscape fragmentation is an anthropogenic process involving breaking up a continuous habitat, land use type, biota or ecosystem (Carvalho et al. 2009), which is likely to have adverse effects. Land-use/cover changes also determine the vulnerability of places and people to climatic, economic, or sociopolitical perturbations. When aggregated globally, LULC changes significantly affect central aspects of earth system functioning (Lambin et al. 2003). These changes manifest in forest fragmentation resulting in habitat loss (Mertens et al. 2000; Nagendra et al. 2004; Mingshi et al. 2011).  Human induced land use changes for agricultural expansions, etc. have caused large scale deforestation leading to soil erosion, watershed degradation, reduced biodiversity, and agrochemical pollution (World Bank 2008), an increase in impervious surface area and landscape fragmentations.  In forest dominated landscapes fragmentation issues of prominence seem to relate typically to deforestation and loss of forest cover over a period of time. It has become an essential to integrate the patterns of land cover change with the processes of land use change by identifying various drivers of the changes. LULC change detection relies on an accurate interpretation of baseline conditions and changes in structure properties. This necessitates LULC analysis integrating landscape ecology theory and practice with other landscape science techniques. Availability of multi-resolution temporal remote sensing (RS) data has aided in monitoring larger areas at various spatial, spectral and spectral and temporal resolutions. Remote sensing data along with GIS (Geographical Information Systems), GPS (Global positioning system) and other collateral data (spatial as well as statistical) help in effective LULC analysis (Ramachandra, Kumar 2004). Inventorying, mapping, quantifying, and monitoring the physical characteristics of LULC has been widely recognized as a key element in the study of regional and global changes. The strong linkages between spatial pattern and ecological process have been established (Gustafson1998). Currently the approaches to explore spatio temporal process of LULC have been vastly improved by incorporating drivers of change (agent based modelling) and numerous methods with the help of remote sensing (Liu et al. 2010). These techniques emphasise the integrated analyses of remote sensing with socio political economic parameters for better insights to the human dimensions of LULC. Yang et al. (2011) derives the relation of population to environmental changes; by incorporating population distribution data for deriving human pressure on the surrounding environment. Social data, time series remote sensing data, and thematic coverages maintained within a GIS are integrated essentially to provide historically transformation of land conversions associated with the cultivation and other development activities in a certain landscape. Generally, gradients are widely used in landscape science and ecology to describe spatial land use patterns (Hahs, McDonnell 2006) and ecosystem structure, functions in rural–urban regions (McDonald 2009). The gradient based studies efficiently capture the large-scale changes of spatiotemporal characteristics and interactions of a landscape (Li et al. 2010).

Forest physiology is dependent on the photosynthetic activity that plays a major role in the assessment of forest physiology necessitate the understanding seasonal variations in the vegetation dominated landscapes. Satellite remote sensing in visible and near-infrared wavelengths is sensitive to changes in photosynthetic biomass and provides a means for regional mapping and monitoring of seasonal phenology (i.e. canopy growth and senescence) and growing season length for deciduous vegetation (Zhou et al. 2001). During the high water availability period, high photosynthetic activity values are detected due to the relatively low temperature (in comparison with summer temperatures), which helps in better charecterisation of forests; and conversely, during the summer, photosynthetic activity decreases as a result of high temperature and absence of water availability. All these variables affect the state of the forest reflectivity and are reflected in the varying intensity throughout the year (Volcania et al. 2005). The remote sensing data of varying seasons sheds new insight for better understanding of the seasonal dynamics of leaf and canopy (Zhang et al. 2006;Zang, Huang 2006). Sensitivity analyses improve accuracy through better estimates of seasonal changes in canopy photosynthetic capacity by incorporating seasonal remote sensing data (Waring et al. 2010).

Landscape metrics analyses through the quantification of landscape fragmentation provide better understanding of the geometric properties of a landscape. These metrics are also known as spatial metrics describe the composition and arrangement of the various patches of land cover types. These are considered for dynamic landscape monitoring, including ongoing changes (Peng et al. 2010), assessing the impacts of management decisions and human activities (Geri et al. 2010), supporting decisions on landscape and conservation planning (Garcia et al. 2011) and to analyze landscape and habitats fragmentation (Zeng, Wu 2005).  Furthermore temporal variations in the spatial metrics would reflect the aggregate or cumulative effects of different dynamic processes (Herold et al. 2005). Unplanned development leads to rapid land cover changes in the region. Road density, population, land use, and topography affect forest regrowth, which results in deforestation and forest fragmentation (Freitas et al. 2010). Rail/road connectivity increases the accessibility of remote areas, allowing logging, hunting, and deforestation for new agricultural and pasture fields (Nagendra et al. 2003). Global, regional, and national demand for agricultural products creates new land use demands that influence rates of deforestation (Onojeghuo et al. 2011). Landscape pattern and progress has been seen as indicators of the future scenario with the knowledge of temporal land use and land cover data. Combining empirical spatial analyses with scenario models will reveal important details in the process of LULC changes, for the future. Scenario analysis integrated with landscape analysis characterizes uncertainties, test possible impacts, support strategic planning for policy formulation, and uses current knowledge to assess possible future conditions. Thus, in this regard the spatial dynamic models have become an inseparable aspect of a planning system.