Mapping and Attributing Normalized Difference Vegetation Index Trends for Nepal

Remote Sensing

Nir Y. Krakauer¹  Tarendra Lakhankar¹  José D. Anadón² 

[1] Department of Civil Engineering and NOAA-CREST, City College of New York, New York, NY 10031, USA;The Graduate Center, City University of New York, New York, NY 10016, USA;

关键词: random forest;    regression tree;    carbon fertilization;    land cover change;    climate change;   

DOI  :  10.3390/rs9100986

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown