Mapping and Attributing Normalized Difference Vegetation Index Trends for Nepal

Global change affects vegetation cover and processes through multiple pathways. Long 1 time series of surface land surface properties derived from satellite remote sensing give unique 2 abilities to observe these changes, particularly in areas with complex topography and limited research 3 infrastructure. Here, we focus on Nepal, a biodiversity hotspot where vegetation productivity is 4 limited by moisture availability (dominated by a summer monsoon) at lower elevations and by 5 temperature at high elevations. We analyze normalized difference vegetation index (NDVI) from 6 1981 to 2015 semimonthly, at 8 km spatial resolution. We use a random forest (RF) of regression trees 7 to generate a statistical model of NDVI as a function of elevation, land use, CO2 level, temperature, 8 and precipitation. We find that NDVI has increased over the studied period, particularly at low 9 and middle elevations and during fall (post-monsoon). We infer from the fitted RF model that 10 the NDVI linear trend is primarily due to CO2 level (or another environmental parameter that is 11 changing quasi-linearly), and not primarily to temperature or precipitation trends. On the other hand, 12 interannual fluctuation in NDVI is more correlated with temperature and precipitation. RF accurately 13 fits the available data and shows promise for estimating trends and testing hypotheses about their 14 causes. 15