Improving signal strength of tree rings for paleoclimate reconstruction by micro-hyperspectral imaging

Abstract

In dendroclimatology, tree ring chronology is ordinarily established to reveal the fluctuation law of climate change on the interannual, interdecadal, and centennial scales. However, since traditional dendrochronology can only use one variable (tree ring width) to reflect environmentally related information, this causes the richer information recorded in the tree rings to be discarded. In this study, we examined the potential of hyperspectral chronological indices (shortened as “hyperspectral index/indices”) with samples collected in Shennongjia woodland in central China. The correlation analysis of the tree ring series on different samples indicated that hyperspectral indices outperform the traditional width index in chronology statistics including Signal-to-noise Ratio (SNR) and Expressed Population Signal (EPS). The reliability test shows that hyperspectral chronologies have more periods reaching the threshold of EPS or Subsample Signal Strength (SSS) > 0.85, which means that hyperspectral chronologies provide more reliable periods for accurate climate reconstruction. Based on this, chronologies built by the three dendroclimatic indices were used to reconstruct the average temperature changes in Shennongjia over the last 103 years. The reconstruction results indicate that in our study area, the traditional width index model failed the split-sample calibration test and exhibited a low reconstruction accuracy, while the hyperspectral index model has a higher explained variance of 46.4% (p < 0.01), compared to the width index (21.4%) and the grayscale index (38.3%). Our research results show that hyperspectral indices have greater potential for climate reconstruction in regions with lower susceptibility to climate stress. This is attributed to their ability to effectively extract subtle climate signals from the spectral variations on the surface of tree rings. Such ring spectral changes may be caused by complex and currently unknown responses of the trees to the climate.

Keywords

• Common signal strength
• hyperspectral indices
• microscopic hyperspectral imaging
• past climate reconstruction
• tree ring width