Spatiotemporal Variations of Forest Vegetation Phenology and Its Response to Climate Change in Northeast China

Abstract

Vegetation phenology is an important indicator of vegetation dynamics. The boreal forest ecosystem is the main part of terrestrial ecosystem in the Northern Hemisphere and plays an important role in global carbon balance. In this study, the dynamic threshold method combined with the ground-based phenology observation data was applied to extract the forest phenological parameters from MODIS NDVI time-series. Then, the spatiotemporal variation of forest phenology is discussed and the relationship between phenological change and climatic factors was concluded in the northeast China from 2011 to 2020. The results indicated that the distribution of the optimal extraction threshold has spatial heterogeneity, and the changing rate was 3% and 2% with 1° increase in latitude for SOS (the start of the growing season) and EOS (the end of the growing season). This research also notes that the SOS had an advanced trend at a rate of 0.29 d/a while the EOS was delayed by 0.47 d/a. This variation of phenology varied from different forest types. We also found that the preseason temperature played a major role in effecting the forest phenology. The temperature in winter of the previous year had a significant effect on SOS in current year. Temperature in autumn of the current year had a significant effect on EOS.

Exploring the relative importance of preseason climatic drivers on spring phenology dynamics in the Greater Khingan Mountains, Northeast China

“…Yu et al (2017) revealed that the spring phenology of Northeast China was between Julian days 100 and 140 from 1982 to 2015. Previous studies utilizing remote sensing data from Northeast China have also reported comparable outcomes (Zhao et al, 2016;Zheng et al, 2022). The consistency between these outcomes and the extracted phenological results of our study show that our study results are reliable and have a certain reference value.…”

“…Some are constrained by short study periods (Cai et al, 2012; X. J. Shen et al, 2019), impeding a comprehensive understanding of long‐term phenological changes. Others have not considered the division of the region into distinct ecogeographical areas, hindering exploration of regional disparities (Zhao et al, 2016; Zheng et al, 2022). Moreover, many studies have focused on the relationship between the SOS and climate variables solely from two perspectives (Qiao et al, 2019; Tang et al, 2015): temperature and precipitation, neglecting the influence of solar radiation in controlling SOS.…”

“…Advancing vegetation growing seasons are proven to be mainly attributable to various climatic factors, particularly temperature and precipitation (Y. S. H. Fu et al, 2015). Elevated temperatures may enhance the efficiency of enzymes activity and expedite developmental stages within plants (Zheng et al, 2022). Precipitation has been reported to affect spring phenology (C. Li et al, 2021), particularly in arid areas (Miao et al, 2017).…”

Preliminary Study on the Use of Digital Surface Models for Estimating Vegetation Cover Density in Mountainous Area

“…In the third stage, volume index images based on ASTER, SRTM, and ALOS Palsar were then compared with vegetation indices of NDVI and SAVI, as well as with FCD model based on the spectral transformation of Landsat series images. These indices have been known to have strong correlation with biomass content, stand volume, and carbon storage (Margaretha et al, 2013;Dewa and Danoedoro, 2017;Pahlevi et al, 2021;Zheng et al, 2022). Prior to the application of…”

What Are Contemporary Mexican Conifers Telling Us? A Perspective Offered from Tree Rings Linked to Climate and the NDVI along a Spatial Gradient

“…Likewise, when remote sensing is associated with dendrochronology, the ecological mechanisms that underlie forest growth can be elucidated [23][24][25]. One of the most utilized indices is the normalized difference vegetation index (NDVI) [26,27], yet this requires further research, particularly considering the availability of automated monitoring platforms that operate in near real-time (see Google Earth Engine) [28]. In this way, the convergence of discrete variables from tree rings [10] and continuous variables from NDVI [29] exponentially increase the capacity of the results in terms of the assessment of the timing and rates of forest growth.…”