Detection of Multidecadal Changes in Vegetation Dynamics and Association with Intra-annual Climate Variability in the Columbia River Basin

TL;DR

This study investigates multidecadal vegetation changes in the Columbia River Basin using satellite data and functional data analysis, revealing trends toward earlier and higher leaf area index values linked to climate variability.

Contribution

It applies functional data analysis to detect long-term vegetation dynamics and regional clustering, offering new insights into climate-vegetation interactions.

Findings

Long-term LAI shows a trend toward earlier, higher seasonal peaks.

Regional clusters exhibit similar greening trends.

Maximum temperature and precipitation are correlated with LAI but lack clear trends.

Abstract

Leaf Area index is widely used metric for the assessment of vegetation dynamics and can be used to assess the impact of regional/local climate conditions. The underlying continuity of high resolution spatio-temporal phenological processes in the presence of extensive missing values poses a number of challenges in the detection of changes at a local and regional level. The feasibility of functional data analysis methods were evaluated to improve the exploration of such data. In this paper, an investigation of multidecadal variation of leaf area index (LAI) is conducted in the Columbia Watershed, as detected by NOAA AVHRR satellite imaging, and its inter- and intra-annual correlation with maximum temperature and precipitation using the ERA-Interim Reanalysis from 1996 to 2017. A functional cluster analysis model was implemented to identify regions in the Columbia Watershed that exhibit similar long-term greening trends. Across these several regions, the primary source of annual LAI variation is a trend toward seasonally earlier and higher recordings of regional average maximum LAI. Further exploratory analysis reveals that although strongly correlated to LAI, maximum temperature and precipitation do not exhibit clear longitudinal trends.