Estimation and Prediction of Grassland Cover in Western Mongolia Using MODIS-Derived Vegetation Indices

Abstract

Spectral indices derived from satellite observations, such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), are widely used for grassland monitoring and management around the globe. In this study we contrasted performance of NDVI and EVI metrics obtained from Aqua and Terra, the two satellite platforms carrying the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, for estimating grassland cover measured at ground level on ninety-two 1 × 1 km blocks distributed from semidesert to high montane grasslands in the Sailugem Range of western Mongolia, where overgrazing and overstocking of domestic livestock are concerns for pastureland management. We also explored utility of late spring (May) vegetation indices for forecasting vegetation cover at the peak of the growing season (July). Vegetation indices developed using MODIS 1-km monthly data (MOD13A3 and MYD13A3) were strongly related to on-the-ground field estimates of the percentage of vegetation cover in July (74−85% variation explained), with second-order polynomial regressions demonstrating better fit to the data than first-order regressions, Aqua vegetation indices (VIs) explaining slightly more variance than Terra’s VIs, and NDVI performing comparably to EVI for both Aqua and Terra. Both Aqua and Terra VIs for May were highly predictive of July vegetation cover (R2 = 0.80−0.84). We conclude that monthly MODIS NDVI and EVI datasets can be useful for rangeland managers in western Mongolia to monitor and predict summer pasture conditions at the regional level, where science-based guidance on grazing policy and practices is much needed.