Nonlinear relationship of greening and shifts from greening to browning in vegetation with nature and human factors along the Silk Road Economic Belt

Xiaojuan Xu; Huiyu Liu; Fusheng Jiao; Haibo Gong; Zhenshan Lin

doi:10.1016/j.scitotenv.2020.142553

Nonlinear relationship of greening and shifts from greening to browning in vegetation with nature and human factors along the Silk Road Economic Belt

Sci Total Environ. 2021 Apr 20:766:142553. doi: 10.1016/j.scitotenv.2020.142553. Epub 2020 Oct 5.

Authors

Xiaojuan Xu¹, Huiyu Liu², Fusheng Jiao¹, Haibo Gong¹, Zhenshan Lin¹

Affiliations

¹ Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing 210023, China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China; College of Geography Science, Nanjing Normal University, Nanjing 210023, China; Jiangsu Key Laboratory of Environmental Change and Ecological Construction, Nanjing Normal University, Nanjing 210023, China.
² Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing Normal University, Nanjing 210023, China; State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing Normal University, Nanjing 210023, China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China; College of Geography Science, Nanjing Normal University, Nanjing 210023, China; Jiangsu Key Laboratory of Environmental Change and Ecological Construction, Nanjing Normal University, Nanjing 210023, China. Electronic address: liuhuiyu@njnu.edu.cn.

PMID: 33092839
DOI: 10.1016/j.scitotenv.2020.142553

Abstract

Understanding the trends in vegetation evolution and their driving factors is critical to revealing changes in ecosystem's structure and function. However, less is known about the nonlinear relationship between greening trends and greening to browning shifts in vegetation and its driving factors. Based on ensemble empirical mode decomposition (EEMD) and Boosted Regression Tree (BRT) methods, we investigated the nonlinear relationships of the greening trends and greening to browning shifts in vegetation to climate change, elevation, and human activities along the Silk Road Economic Belt (SREB). Results indicated that: (1) For vegetation with greening trends, although the total importance of climate-related variables was greater than human activities (59.57% and 35.33%, respectively), Land use changes (14.91%) and CO₂ (9.41%) were more important than the other nature-related variables. In addition, volumetric soil moisture (8.72%), precipitation of warmest quarter (6.50%), and daily snow water equivalent (5.75%) were also very important. For vegetation with greening to browning shifts, the total contributions of climate-related variables were also larger than human activities (69.10% and 19.36%, respectively). Moreover, elevation (11.54%), vapor pressure deficit (9.85%), annual mean temperature (6.84%), min temperature of the coldest month (5.98%), soil moisture (5.16%), and annual precipitation (5.11%) might be the main reasons. (2) Increased cropland and grassland, along with the land conversions from shrub land/sparse vegetated to forest could promote vegetation greening. Increased rates of CO₂, volumetric soil moisture, warmest quarter precipitation, and daily snow water equivalent could also promote it. For vegetation with greening to browning shifts, low-altitude areas, increased vapor pressure deficit, decreased cold temperature, and drought could promote vegetation shift greening to browning. The nonlinear analysis can correctly reveal the actual trends in vegetation and its responses to driving factors, which will further provide adaptation strategies to protect ecology.

Keywords: Boosted Regression Tree method (BRT); Ensemble empirical mode decomposition (EEMD); Monotonic trends; Nonlinear relationship; Trend shifts.