Abstract
Remotely-sensed Leaf Area Index (LAI) is a useful metric for assessing changes in vegetation cover and greeness over time and space. Satellite-derived LAI measurements can be used to assess these intra- and inter-annual vegetation dynamics and how they correlate with changing regional and local climate conditions. The detection of such changes at local and regional levels is challenged by the underlying continuity and extensive missing values of high-resolution spatio-temporal vegetation data. Here, the feasibility of functional data analysis methods was evaluated to improve the exploration of such data. In this paper, an investigation of multidecadal variation in LAI is conducted in the Columbia River Watershed, as detected by NOAA Advanced Very High-Resolution Radiometer (AVHRR) satellite imaging. The inter- and intra-annual correlation of LAI with temperature and precipitation were then investigated using data from the European Centre for Medium-Range Weather Forecasts global atmospheric re-analysis (ERA-Interim) in the period 1996–2017. A functional cluster analysis model was implemented to identify regions in the Columbia River Watershed that exhibit similar long-term greening trends. Across this region, a multidecadal trend toward earlier and higher annual LAI peaks was detected, and strong correlations were found between earlier and higher LAI peaks and warmer temperatures in late winter and early spring. Although strongly correlated to LAI, maximum temperature and precipitation do not demonstrate a similar strong multidecadal trend over the studied time period. The modeling approach is proficient for analyzing tens or hundreds of thousands of sampled sites without parallel processing or high-performance computing (HPC).
Highlights
Satellites allow for near-continuous observations of earth and monitoring of vegetation dynamics, or changes in vegetation cover and greeness over time and space, as well as climate conditions at scales ranging from local to global [1]
Annual vegetation dynamics are largely caused by vegetative phenological phenomena that are sensitive to climate conditions
In this work, we argue that the grouping of remotely-sensed sites into increasingly homogenous groups improves the interpretability of any study of regional change in vegetation dynamics
Summary
The study of plant responses to climate change has greatly expanded in the recent past with the development and use of remote sensing and the compilation of multidecadal satellite data sets. Satellites allow for near-continuous observations of earth and monitoring of vegetation dynamics, or changes in vegetation cover and greeness over time and space, as well as climate conditions at scales ranging from local to global [1]. Annual vegetation dynamics are largely caused by vegetative phenological phenomena that are sensitive to climate conditions. Changes in phenology, such as the timing, rate, duration, and magnitude of annual vegetative growth, can signal important effects of climate change on plants [2]. LAI provides a key measure of plant cover in a given area and is defined as an essential climate variable (ECV)
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