Mapping and Attributing Normalized Difference Vegetation Index Trends for Nepal

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....

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2017-10, Vol.9 (10), p.986
Main Authors: Krakauer, Nir, Lakhankar, Tarendra, Anadón, José
Format: Article
Language:English
Subjects:
Biodiversity
Biodiversity hot spots
carbon fertilization
climate change
land cover change
Land use
Mathematical models
Moisture availability
Monsoons
Normalized difference vegetative index
Precipitation
random forest
Regression analysis
regression tree
Remote observing
Remote sensing
Spatial discrimination
Spatial resolution
Statistical analysis
Statistical models
Surface properties
Temperature effects
Trends
Variation
Vegetation
Vegetation cover
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs9100986