Headwater stream ecosystem: an important source of greenhouse gases to the atmosphere - Li, Mingxu; Peng, Changhui; Zhang, Kerou; Xu, Li; Wang, Jianming; Yang, Yan; Li, Peng; Liu, Zelin; He, Nianpeng (Water Research, 2021)

Combined control of multiple extreme climate stressors on autumn vegetation phenology on the Tibetan Plateau under past and future climate change - Li, Peng; Liu, Zelin; Zhou, Xiaolu; Xie, Binggeng; Li, Zhongwu; Luo, Yunpeng; Zhu, Qiuan; Peng, Changhui (Agricultural and Forest Meteorology, 2021)

Strong controls of daily minimum temperature on the autumn photosynthetic phenology of subtropical vegetation in China - Ren, Peixin; Liu, Zelin; Zhou, Xiaolu; Peng, Changhui; Xiao, Jingfeng; Wang, Songhan; Li, Xing; Li, Peng (Forest Ecosystems, 2021)
Abstract Background Vegetation phenology research has largely focused on temperate deciduous forests, thus limiting our understanding of the response of evergreen vegetation to climate change in tropical and subtropical regions. Results Using satellite solar-induced chlorophyll fluorescence (SIF) and MODIS enhanced vegetation index (EVI) data, we applied two methods to evaluate temporal and spatial patterns of the end of the growing season (EGS) in subtropical vegetation in China, and analyze the dependence of EGS on preseason maximum and minimum temperatures as well as cumulative precipitation. Our results indicated that the averaged EGS derived from the SIF and EVI based on the two methods (dynamic threshold method and derivative method) was later than that derived from gross primary productivity (GPP) based on the eddy covariance technique, and the time-lag for EGS sif and EGS evi was approximately 2 weeks and 4 weeks, respectively. We found that EGS was positively correlated with preseason minimum temperature and cumulative precipitation (accounting for more than 73% and 62% of the study areas, respectively), but negatively correlated with preseason maximum temperature (accounting for more than 59% of the study areas). In addition, EGS was more sensitive to the changes in the preseason minimum temperature than to other climatic factors, and an increase in the preseason minimum temperature significantly delayed the EGS in evergreen forests, shrub and grassland. Conclusions Our results indicated that the SIF outperformed traditional vegetation indices in capturing the autumn photosynthetic phenology of evergreen forest in the subtropical region of China. We found that minimum temperature plays a significant role in determining autumn photosynthetic phenology in the study region. These findings contribute to improving our understanding of the response of the EGS to climate change in subtropical vegetation of China, and provide a new perspective for accurately evaluating the role played by evergreen vegetation in the regional carbon budget.

Global response of terrestrial gross primary productivity to climate extremes - Yuan, Minshu; Zhu, Qiuan; Zhang, Jiang; Liu, Jinxun; Chen, Huai; Peng, Changhui; Li, Peng; Li, Mingxu; Wang, Meng; Zhao, Pengxiang (Science of The Total Environment, 2021)

Response of Vegetation Photosynthetic Phenology to Urbanization in Dongting Lake Basin, China - Li, Peng; Sun, Mai; Liu, Yuxin; Ren, Peixin; Peng, Changhui; Zhou, Xiaolu; Tang, Jiayi (Remote Sensing, 2021)
Urbanization can induce environmental changes such as the urban heat island effect, which in turn influence the terrestrial ecosystem. However, the effect of urbanization on the phenology of subtropical vegetation remains relatively unexplored. This study analyzed the changing trend of vegetation photosynthetic phenology in Dongting Lake basin, China, and its response to urbanization using nighttime light and chlorophyll fluorescence datasets. Our results indicated the start of the growing season (SOS) of vegetation in the study area was significantly advanced by 0.70 days per year, whereas the end of the growing season (EOS) was delayed by 0.24 days per year during 2000–2017. We found that urbanization promoted the SOS advance and EOS delay. With increasing urbanization intensity, the sensitivity of SOS to urbanization firstly increased then decreased, while the sensitivity of EOS to urbanization decreased with urbanization intensity. The climate sensitivity of vegetation phenology varied with urbanization intensity; urbanization induced an earlier SOS by increasing preseason minimum temperatures and a later EOS by increasing preseason precipitation. These findings improve our understanding of the vegetation phenology response to urbanization in subtropical regions and highlight the need to integrate human activities into future vegetation phenology models.

Analysis and Prediction of Expansion of Central Cities Based on Nighttime Light Data in Hunan Province, China - Liu, Yuxin; He, Tian; Wang, Yi; Peng, Changhui; Du, Hui; Yuan, Shuai; Li, Peng (Sustainability, 2021)
Quantifying the characteristics of urban expansion as well as influencing factors is essential for the simulation and prediction of urban expansion. In this study, we extracted the built-up regions of 14 central cities in the Hunan province using the DMSP-OLS night light remote sensing datasets from 1992 to 2018, and evaluated the spatial and temporal characteristics of the built-up regions in terms of the area, expansion speed, and main expansion direction. The backpropagation (BP) neural network and autoregressive integrated moving average (ARIMA) model were used to predict the area of the built-up regions from 2019 to 2026. The model predictions were based on the GDP, ratio of the secondary industry output to the GDP, ratio of the tertiary industry output to the GDP, year-end urban population, and urban road area. The results demonstrated that the built-up area and expansion speed of the central cities in the eastern part of the Hunan province were significantly higher than those in the western part. The main expansion directions of the 14 central cities were east and south. The urban road area, year-end urban population, and GDP were the main driving factors of the expansion. The urban expansion model based on the BP neural network provided a high prediction accuracy (R = 0.966). It was estimated that the total area of urban built-up regions in the Hunan province will reach 2463.80 km2 by 2026. These findings provide a new perspective for predicting urban areas rapidly and simply, and it also provides a useful reference for studying the spatial expansion characteristics of central cities and formulating a sustainable urban development strategy during the 14th Five-Year Plan of China.