Nonlinear relationship of vegetation greening with nature and human factors and its forecast – A case study of Southwest China

• Published in: Ecological indicators Vol. 111; p. 106009
• Main Authors: Liu, Huiyu, Jiao, Fusheng, Yin, Jingqiu, Li, Tingyou, Gong, Haibo, Wang, Zhaoyue, Lin, Zhenshan
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2020
• Subjects: Bioclimatic variables; Boosted Regression Tree method (BRT); Human activities of different kinds; Nonlinear relationship; Variable interactions; Boosted Regression Tree method (BRT); Human activities of different kinds; Variable interactions; Nonlinear relationship; Bioclimatic variables
• ISSN: 1470-160X; 1872-7034
• DOI: 10.1016/j.ecolind.2019.106009

A workflow of the research method process. [Display omitted] •Bioclimatic variables are very suitable for examining climatic impacts.•It is needed to differentiate human impacts of different kinds.•Importance of climate change, human activity and topography was 42.7%, 33.2%, 18.5%.•All nature and human factors showed nonlinear relationship with vegetation greening.•Future climate changes will facilitate vegetation greening. Vegetation showed a greening trend in most Southwest China. However, the nonlinear relationship of vegetation greening with nature and human factors remains unclear. In this study, we studied the nonlinear relationship and predicted the future changes of the greening with Boosted Regression Tree (BRT) method. Results showed that: (1) Precipitation of Driest Month (Bio14, 20.64%), land use changes (10.39%) and population density (8%) were the three most important factors limiting vegetation greening. Climate changes (42.655%) and human activities (33.163%) were the two most important variable types, but topography was also important (18.481%), which cannot be ignored; (2) Bio14 and elevation had the strongest variable interactions. Climate changes had strong interactions with both human activities and elevation, but human activities and elevation had less interactions; (3) vegetation greening was facilitated by the increasing of Bio14, distance to residential area, temperature annual range (Bio7), but inhibited by the increasing of GDP and precipitation of coldest quarter (Bio19) with changing rates. These factors would have no further impacts when approaching threshold values. The increase of population density improved vegetation greening greatly when it was low, while inhibited the greening strongly when it was high. Elevation increase promoted vegetation greening when elevation <300 m, then inhibited it. For land use changes, ‘Grain for Green’ improved vegetation greening, but urbanization decreased it; (4) Under current environmental condition, area percentage of vegetation browning will greatly increase from 13.88% to 37.69%, which is mostly from insignificant vegetation changes and located in the west. However, future climate changes will facilitate vegetation greening in the most area except the northwest. Our results highlight the importance of nonlinear analysis for determining the drivers and predicting future changes of vegetation greening, and developing adaptation and alleviation strategies for climate changes and human activities in fragile ecosystem.