During the last few decades, many regions have experienced major LC transformations, often driven by human activities. Assessing and evaluating these changes require consistent data over time at appropriate scales as provided by remote sensing imagery. Given the availability of small and large-scale observation systems that provide the required long-term records, it is important to understand the specific associated characteristics.
This work constructed monthly NDVI sequences from MODIS data during 2003–2012 covering Western Ghats, India. The data were comprehensively used in the LC change analysis, and correlation analysis between NDVI and climatic parameters. The study confirmed the feasibility of long-term NDVI time-series climate research. NDVI trends were spatially heterogeneous, corresponding with regional climatic characteristics for  different seasons. Monthly NDVI trend reflected different spatial patterns for different regions and different  LC classes. NDVI  had  significant correlations  with  monthly  mean  temperature and monthly precipitation. The correlation between NDVI and temperature was  larger than that between NDVI and precipitation. Overall variation trend in NDVI–LST and NDVI-rainfall within the three regions were calculated to see if those could conceal significant changes in short periods. Monsoon and winter NDVI was more correlated with temperature than with rainfall. A decreasing trend in the rainfall pattern over forest and agricultural/grassland areas were forecasted between 2013 to 2020 in northern, central and southern Western Ghats.
Future study will focus on the type of forest/vegetation and their response to change in climatic parameters. Further investigations will address other land use change phenomena, such as combination of short-term disturbance and gradual land use conversion practices typical for areas in Western Ghats. If land use conversions are targeted, change detection based on multi-seasonal and multi-year image classification would complement trend based approaches. This study makes a strong case for the usage of long-term data repository supporting consistent monitoring of terrestrial ecosystem, irrespective of the strategy followed.