Food security is emerging as a concern in the Himalayas, with changes in weather patterns, the water cycle, and water availability, due to climate change. Remote sensing provides an effective way of monitoring agricultural fields and providing a synoptic view of the result of field practices, which can then be processed to help agricultural scientists make appropriate decisions.
Monitoring agriculture from space means looking at the green-ness to estimate how much photosynthesis is occurring on the ground. By comparing the recent vegetation conditions with the long term average, anomalies are determined to predict the increase or shortfall in the production. In this application, the phenological patterns in agricultural areas are assessed across Nepal by analysing time series anomalies in the vegetation index for the period 2001 to 2010.
The selected vegetation index was the Normalized Difference Vegetation Index (NDVI) acquired by the moderate resolution sensor MODIS satellite at 16-day time intervals and 250 m spatial resolution, a standard product coded as MOD13Q1. A total of 230 NDVI 16-day composite images were used in the analysis and statistics were generated based on agricultural masks for each district of Nepal.
This study used the Earth Trends Modeler (ETM), which provides an integrated platform for the exploration and analysis of remote sensing time series. While the smoothing and fitting of NDVI and other series data is supported, ETM implements a Seasonal Trend Analysis (STA) method based on harmonic analysis of time. The user can view the status of latest year compared with long term averages and anomalies. NDVI composites for selected period can be viewed using the time-slider tool.