The structure of the landscape is undergoing transformation either naturally or due to anthropogenic activities. Land cover (LC) changes aggravated with the enhanced antropogenic activities to meet the growing demand of burgeoning population has led to alterations in the regional climate. LC changes refer to human modification of the Earth's terrestrial surface for food and other essentials. Terrestrial ecosystems are permanently changing at a variety of spatial and temporal scales due to natural and/or anthropogenic causes. Changes in LC induced by any of these agents (either human and/or natural processes) play a major role in global as well as regional scale patterns, which in turn influence weather and climate. The key links between LC with weather and climate include the exchange of greenhouse gases (water vapour, carbon dioxide, methane and nitrous oxide) and sensible heat between the land surface and the atmosphere, the radiation (both solar and long wave) balance of the land surface, and the roughness of the land surface and its uptake of momentum from the atmosphere (Loveland et al., 2003). The current rate of LC changes has increased drastically with wider extent and intensity, driving unprecedented changes in ecosystems and environmental processes at regional and global scales. Extensive clearing of forests for unplanned developmental activities and management practices have encouraged the concentration of human populations, accompanied by the intensification of agriculture in the biologically productive lands and the abandonment of unproductive lands, threatening the sustainability of land resources. Mismanagement of land resources is linked to potentially negative consequences. LC changes encompass the greatest environmental concern today, including climate change, biodiversity loss, global warming, ground water depletion, and the pollution of water, soil and air (Ellis and Pontius, 2010). Moreover, local alterations in LC can have global consequences, requiring local and regional solutions with cooperation of the stakeholders in land management at all scales. Monitoring the locations and distributions of LC change and curtailing its negative consequences while sustaining the production of essential resources and resilience of degraded ecosystem is a major challenge for management, policy decision makers and economic planners and is important for establishing links between policy decisions, regulatory actions and subsequent land use activities (Lunetta et al., 2006).
Ideally, frequently updated data of environmental status, trends, are essential for understanding ecosystem processes and modeling. However, currently available LC datasets for large geographic regions are produced on an intermittent basis and are often outdated with the current pace of change. A scientific investigation to understand the cause and consequences of LC change across a range of spatial and temporal scales is now possible with the availability of temporal remote sensing (RS) data with advancements in geo-informatics and modeling techniques, together with the field based scientific methods. The knowledge of spatial and temporal distributions of vegetation are fundamental to many aspects of environmental science, time-series global change detection for evolving appropriate strategies for sustainable management of natural resources. Acquisitions of data remotely through space borne sensors have offered means of measuring vegetation properties at regional to global scales over the last two decades. Phenological changes during the growing season can be studied by examining the spectral signature changes in the RS data. Time-series remotely sensed data acquired in different spectral bands aid in LC change detection analysis. They provide a powerful tool to learn from past events, monitor current conditions (Orr et al., 2004), and prepare for future change. However, spectral-based change detection techniques (classification) have limited performance in biologically complex ecosystems due to phenology-induced errors (Lunetta et al., 2002a, b). Other factors that limit the application of post-classification change detection techniques include cost, consistency, and error propagation (Singh, 1989).
On the other hand, ecosystem-specific regeneration rates are an important consideration for determining the required frequency of data collections to minimize errors. As part of the natural processes associated with vegetation dynamics, plants undergo intra-annual cycles (phenology). Earlier researches have focused on monitoring changes in vegetation growth due to its important role in regulating terrestrial carbon cycle and the climate system. During different stages of vegetation growth, plant structures and associated pigment assemblages can vary significantly. Changes in vegetation productivity are a primary regulator of the variation in terrestrial net carbon update (Zhao and Running, 2010). Further, changes in vegetation productivity alter biophysical land surface properties and the amount and nature of the energy transfer to the atmosphere, which ultimately changes the local or regional climate (Jackson et al., 2008). Hence, increased attention has been paid to the dynamic rules of vegetation growth and its response to climate change at regional, continental and global scales in the past several decades (Zhang et al., 2013). Ability to identify vegetation classes using remote sensor systems is due to wavelength-specific foliar reflectance (0.76–0.90 μm), pigment absorptions (0.45–0.69 μm), and foliar moisture content (1.55–1.75 μm). Vegetation type appear significantly different at various stages during intra-annual growth cycles (Lunetta et al., 2006) enabling the analysis of seasonal variations. Comparison of current vegetation data records with historic long-term averages have been used to support ecosystem monitoring (Orr et al., 2004). Long term analysis of the vegetation changes over wet, normal and dry years is a vital requirement to closely look into vegetation response to climatic changes. Based on these, numerous pre-classification change detection approaches have been developed and refined to provide optimal performance over the greatest possible range of ecosystem conditions. These semi-automated digital data processing approaches include image-based composite analysis (Weismiller et al., 1997) and principal components analysis (PCA) (Byrne et al., 1980; Lillesand and Keifer, 1972; Richards, 1984).
Vegetation indices are commonly applied data transformation technique (Crist, 1985; Jensen, 2005) where the vegetation signal is boosted and the information becomes more useful when two or more bands are combined into a vegetation index (VI). VI can then be used as surrogate measures of vegetation activity. A widely used VI to separate vegetation from non-vegetative classes is NDVI (Normalized Difference Vegetation Index). NDVI is dependent on the spectral relationships of vegetation in the red and near-infrared (NIR) part of the spectrum. Due to vegetation pigment absorption (chlorophyll, proto-chlorophyll), the reflected red energy decreases, while the reflected NIR energy increases as a result of the strong scattering processes of healthy leaves within the canopy. NDVI can provide a useful index of vegetation variability on seasonal and inter-annual time-scales, and that long-term monitoring of NDVI elucidates relationships between inter-annual fluctuations of vegetation and climate. NDVI theoretically takes values ranging from –1.0 to +1.0. Positive NDVI values (NIR>RED) indicate green, vegetated surfaces, and higher values indicate vegetation with good canopy density. The red portion of the spectrum decreases as solar radiation is absorbed, largely by chlorophyll, whereas reflectance of the NIR portion is caused by leaf mesophyll structure (Kremer and Running, 1993). Negative or zero NDVI values indicate non-vegetated surfaces such as water, ice, and snow.
NDVI have been directly used as a surrogate of plant photosynthetic activity and helps in detecting the biotic responses to climate change (Zhou et al., 2001). This has been effectively used in vegetation dynamics monitoring and to study the vegetation responses to climatic changes at different scales during the past few years and have been a useful tool to couple climate and vegetation distribution and performance at large spatial and temporal scales (Pettorelli et al., 2005). Satellite-derived seasonal greenness/NDVI data have the potential to provide temporal indicators of the onset, end, peak and duration of vegetation greenness as well as the rate of growth, senescence and periodicity of photosynthetic activity (Reed et al., 1994; Yang et al., 1998). Past studies have demonstrated the potential of using NDVI to study vegetation dynamics (Townshend and Justice, 1986; Verhoef et al., 1996), illustrating the value of using high temporal resolution imageries to monitor changes in wetland vegetation (Elvidge et al., 1998) and document the importance of image temporal frequency for accurately detecting forest changes in the southeastern United States (Lunetta et al., 2004). Lyon et al. (1998) reported that NDVI was the best performing VI (vegetation index) for detecting LC changes in the ecologically complex vegetation communities in Chiapas, Mexico. Consistent NDVI time-series are paramount in monitoring ecological resources that are being altered by climate and human impacts (Willem et al., 2006). However, Lunetta et al., (2002a, b) determined that image differencing methods such as two-date NDVI differencing and Multiband Image Differencing (MID) do not perform well in a biologically complex vegetation community in North Carolina. There is also an expanding need for continuous data streams to support the development of spatially distributed landscape process models that would incorporate higher frequency simulations (time steps).

Earlier the time-series data analyses have largely focused on the use of coarse-resolution (≥ 1 km2) AVHRR (Advance Very High Resolution Radiometer) data to document LC and analyse vegetation phenology and dynamics (Justice et al., 1985; Townshend and Justice, 1986; Justice et al., 1991; Loveland et al., 1991). Availability of MODIS data having a 250 m spatial resolution in the red and NIR channels provides opportunity to map phenology at a much finer scale than AVHRR data. With the advent of MODIS NDVI 250 m data, time-series data analysis have been adapted for many applications even though their utility are occasionally limited by the availability of high-quality (e.g., cloud-free) data (Jin and Sader, 2005). Since 2000, NDVI data derived from the Terra/MODIS satellite sensors are being regularly used because they provide higher spatial resolution, enhanced atmospheric corrections and more precise geo-registration. Time-series NDVI are shown to capture essential features of seasonal and inter-annual vegetation variability and have been used to extract numerical observations related to vegetation dynamics (Pettorelli et al., 2005; Tucker and Sellers, 1986). Researchers have incorporated a number of processing techniques including weighted regression smoothing (Li and Kafatos, 2000), Fourier and wavelet transformation filtering (Sakamoto et al., 2005), weighted least squares (Reed, 2006) and wavelet feature extraction (Bruce et al., 2006) to deal with the data quality issues.
Spectral VI with its impact on local temperature and rainfall can be used to investigate and understand the interactions between vegetation dynamics and landscape ecosystems, monitor the effects of deforestation, investigate climate change and carbon sequestration, assess natural resources, agricultural production and food, aid in land management and sustainability to support ecosystem monitoring (Myneni et al.,1997; Nemani et al., 2003; Orr et al., 2004; Seelan et al., 2003; Yang et al., 1998). Specifically, vegetation changes and their relationships with temperature has been the subject of interest. The relationship between different LC types with LST (land surface temperature) revealed that NDVI and LST generally tend to show strong correlation (Ramachandra et al., 2008; Mao et al., 2012). In many cases, higher the NDVI, lower is the LST and vice versa. NDVI in conjunction with LST have been used for many different studies, such as, to derive the spatial extent of the LC change effect (Gunawardhana and Kazama, 2012), to estimate moisture content in forest fire (Chuvieco et al., 2004), to estimate land surface emissivities over agricultural areas (Jiménez-Muñoz et al., 2006), to estimate extent of vegetation types (Raynolds et al., 2008), to predict crop grain yield (Balaghi et al., 2008), to assess vegetation change and their response to climate change (Zhang et al., 2013), drought assessment (Karnieli et al., 2009), etc. 

Vegetation vigor and productivity are related to hydrological variables, hence rainfall data serves as a surrogate measure of these factors at the landscape scale (Groeneveld and  Baugh, 2007; Wang et al., 2003). NDVI is strongly coupled to rainfall fluctuations with index values generally increasing with rainfall (Tucker et al., 1991). This close coupling makes it possible to employ NDVI as a proxy for the land surface response to rainfall variation. The positive trend in NDVI is thereby taken as a response to an overall increase in precipitation (Hickler et al., 2005; Nicholson et al., 1990), although, there have been various theories for the rainfall variability such as influence by global sea surface temperature (Caminade and Terray, 2010), large scale changes in LC and land-atmosphere interaction (Hulme 2001; Nicholson, 2000). Whether the climate impact or human activities are dominating rainfall variability or not, the greening trend is a subject of debate and ongoing research (Huber et al., 2011). In one of the studies by Seaquist et al., (2008) NDVI was first regressed on satellite-measured precipitation data and then the NDVI residual time-series was searched for significant trends for the period 1982–2003. The trends in the residuals depict thereby that part of the measured NDVI was not explained by precipitation. Yet, Herrmann et al., (2005) used all the months of the year, including the long dry season in their study which introduced noise and skewness in the data distribution. NDVI residual time-series, originating from regressing NDVI on rainfall have also been used for identifying significant long-term trends in vegetation greenness induced by other factors than water availability (Huber et al., 2011).