Mapping and Attributing Normalized Difference Vegetation Index Trends for Nepal Remote Sensing

Global change affects vegetation cover and processes through multiple pathways. Long time series of surface land surface properties derived from satellite remote sensing give unique abilities to observe these changes, particularly in areas with complex topography and limited research infrastructure. Here, we focus on Nepal, a biodiversity hotspot where vegetation productivity is limited by moisture availability (dominated by a summer monsoon) at lower elevations and by temperature at high elevations. We analyze the normalized difference vegetation index (NDVI) from 1981 to 2015 semimonthly, at an 8 km spatial resolution. We use a random forest (RF) of regression trees to generate a statistical model of the NDVI as a function of elevation, land use, CO 2 level, temperature, and precipitation. We find that the NDVI increased over the studied period, particularly at low and middle elevations and during the fall (post-monsoon). We infer from the fitted RF model that the NDVI linear trend is primarily due to CO 2 level (or another environmental parameter that is changing quasi-linearly), and not primarily due to temperature or precipitation trends. On the other hand, interannual fluctuation in the NDVI is more correlated with temperature and precipitation. The RF accurately fits the available data and shows promise for estimating trends and testing hypotheses about their causes. 2017 Grant no. NA11SEC4810004 Grant no. NA15OAR4310080 OAR (Oceanic and Atmospheric Research) CPO (Climate Program Office) Education and outreach CREST (Center for Earth System Sciences and Remote Sensing Technologies and Tech Center at City College of CUNY)/ CESSRST (Cooperative Science Center for Earth System Sciences and Remote Sensing Technologies) PMC https://doi.org/10.3390/rs9100986 CC BY 2286