Monitoring Vegetation Changes with NDVI Trends and Climatic Correlations

Abstract

Monitoring Vegetation Changes with NDVI Trends and Climatic Correlations

Sushil Pradhan
GIS Specialist
IKM - MENRIS / ICIMOD, India
suspradhan@icimod.org.np, sushil@icimod.org.np

Abstract :
Normalized Difference Vegetation Index (NDVI) maps allow comparisons of the spatial and temporal variability in the amount and condition of vegetation. The time series satellite derived NDVI was used to monitor and analyze changes in vegetation pattern of Nepal. The NDVI maps of Nepal for the period 1998 – 2003 (6 years) was computed using 10-day’s time series SPOT Vegetation satellite image. The trend showed that the months September and October are normally best growing period and April, May, and June are normally driest period. The NDVI comparison was done among the best growing season for 6 years’ period and it was found that significant differences in vegetation growth production was observed. The maximum, mean, and minimum of the 1998 – 2003 Annual Maximum NDVI was derived to evaluate and represent the vegetation condition through the country. The variability in annual maximum greenness from six years of NDVI data (1998 – 2003) was computed to determine the area of potentially higher and lower inter-annual variability in agricultural production, and was found to be very useful to estimate the frequency of meteorological and agricultural drought events. The agricultural vegetation anomalies for each year (1998 – 2003) were computed to determine the agricultural production anomalies in the southern districts of Nepal, the major agricultural production domain area. The result provided insight into the spatial and temporal agricultural production anomalies events, and was the basis for the detailed analysis to determine the dynamics of these events. For this, the relationship between low agricultural production zone and precipitation variable was analysed to determine the dynamics of agricultural production anomalies. The result shows that most of the agricultural production anomalies events are due to decrease in precipitation.