Abstract: Background, aim, and scope Remote sensing is an effective approach of vegetation monitoring, and remote sensing-based vegetation indices can well capture vegetation growth curve. However, the longest remote sensing observations are only available for about 40 years, which limits their use in research on long-term vegetation dynamics. Tree ring data usually have longer records, which overcomes the limitations of remote sensing observations, but there is no direct link between tree ring and vegetation properties (e.g., gross primary productivity, biomass). Previous studies combine tree ring width and satellite-based vegetation index to reconstruct vegetation dynamics for a long period. However, the selection of indicators depends much on correlation, and thus different indicators might be chosen for different regions, which is not suitable for comparison spatially. Here, we took Yimeng Mountainous area as the study area, reconstructed long-term accumulative NDVI values of the growing season, an effective indicator of annual gross primary productivity, from the earlier 20th century, and characterized their temporal changes. Materials and methods (1) 225 tree ring cores were collected from three sites within our study area: Meng Mountain, Ta Mountain and Yi Mountain. The ring widths of all the cores were measured using MeasureJ2X professional measurement software under the LINTAB tree ring width meter (measuring accuracy 0.01 mm), and then COFECHA program was used to test the cross-dating quality. Finally, the standardized chronology (STD), difference chronology (RES) and autoregressive standardized chronology (ARS) were established by the ARSTAN program. (2) To derive the time series of accumulative NDVI of the growing season, we extracted NDVI time series at 15-day interval for each sampling site, used the TIMESAT software to determine the start, end, and length of the growing season, and finally get the sum of NDVI values within the range of the growing season. (3) The Bootstrap method was used to establish the empirical relationships between tree ring width and accumulative NDVI, and reconstruct the time series of accumulative NDVI from early 20th century. (4) Wavelet analysis was utilized to identify the underlying periods within the long-term time series of climate variables and accumulative NDVI. Results The mean accumulative NDVI of growing season for Yi Mountain area is 7.36, which was lower than that in Meng and Ta Mountain areas. Vegetation productivity in Meng Mountain area showed an increasing trend, and the magnificence of increase was particularly large after 1980s, but there was no significant trend in Ta and Yi Mountain areas. There were 2 years, 4 years and 8 years of period underlying the time series of accumulative NDVI for three sites. Variations in mean wavelet power between 2 to 8 years for accumulative NDVI was more consistent with those for PDSI than temperature. Discussion Our research found that tree ring width significantly correlated with accumulative NDVI of growing season, an effective indicator of vegetation productivity. Meanwhile, our used nonparametric method, Bootstrap regression, was more robust than traditional statistical method, which could address the situations when the sample size was small or the distribution of samples was not normal. Therefore, our research provides a framework which accurately reconstructs vegetation dynamics for a long time period. We also found that vegetation dynamics within our study area were determined by combined water and temperature, as indicated by the highly consistence between variations in accumulative NDVI and PDSI. Conclusions We concluded that integrating remote sensing and tree ring techniques could effective reconstruct long-term vegetation dynamics, and accumulative NDVI of growing season was useful indicator to be chosen for reconstruction given its high correlation with tree ring width and its close link with vegetation productivity. Vegetation dynamics in the Yimeng Mountainous areas were determined by water and temperature factors. Recommendations and perspectives We develop a framework to accurately reconstruct vegetation dynamics by combined remotely sensed data and tree ring materials, which could be extended to other research areas. Our findings that both water and temperatures are important to determine vegetation productivity are useful for explaining and predicting vegetation dynamics under climate change especially in the warm temperate Yimeng Mountainous regions.
Keywords: tree ring; remote sensing; NDVI; Yimeng Mountainous area; Forest vegetation dynamics