Seasonal-based analysis of vegetation response to environmental variables in the mountainous forests of western himalaya using landsat 8 data

Abstract

The health (or greenness) of the mountainous vegetation varies with seasons depending on its type and local topographic and climatic conditions. The forests in the Western Himalayas are influenced by variables such as precipitation and temperatures through seasons with considerable inter-annual variability. This study presents the phenological behaviour of the moist deciduous forests (MDFs) in Doon Valley of Uttarakhand, India, during 2013–2015 using medium spatial resolution data set. We proposed a new index called the temporal normalized phenology index (TNPI) to quantify the change in trajectories of Landsat 8 OLI-derived normalized difference vegetation index (NDVI) during two time steps of the vegetation growth cycle. To establish the associations amongst a set of environmental factors and vegetation greenness during different seasons, multiple regression analysis was carried out with sample-based TNPI values as response variable and elevation, slope, aspect, and Landsat 8-derived land surface temperature (LST) as explanatory variables. Our results indicated that major changes in NDVI values occur between April (transitional month of leafing phenophase and starting of leaf flush activity) and September (end of leaf flush activity). Furthermore, interactions amongst environmental variables (eleva-tion, LST, and precipitation) are strongly correlated with changes in vegetation greenness between April and September, whereas they show lesser correlations as stand-alone factors. The pronounced effect of the change in LST (ΔLST) was observed in lower elevation areas (400–600 m), which resulted in the change in vegetation greenness between leaf fall and leaf flush activity. In conclusion, cross-validated statistics confirmed that TNPI may be used as a better alternative for the analysis of temporal phenology cycle between two time steps of maximum and minimum vegetation growth periods. This may reduce the requirement of large time-series remote-sensing data sets for long-term vegetation phenology analysis. © 2017 Informa UK Limited, trading as Taylor & Francis Group.