Analyzing nonlinear variations in terrestrial vegetation in China during 1982-2012

Area of Science:

• Ecology
• Environmental Science
• Remote Sensing

Background:

• Large-scale quantification of terrestrial vegetation changes is crucial for detecting global environmental change impacts.
• China exhibits an overall trend of vegetation restoration alongside localized degradation, necessitating detailed spatial and temporal analysis.

Purpose of the Study:

• To analyze the temporal and spatial characteristics of terrestrial vegetation growth in China from 1982 to 2012.
• To evaluate the effectiveness of high-order nonlinear fitting for ecological trend analysis.

Main Methods:

• Utilized the Global Inventory Modeling and Mapping Studies (GIMMS3g) dataset (1982-2012).
• Employed nonparametric linear regression combined with high-order nonlinear fitting to analyze vegetation trends.
• Investigated spatial processes of vegetative restoration and degradation.

Main Results:

• High-order curves effectively captured complex vegetation growth patterns, including restoration-degradation-restoration cycles (e.g., Loess Plateau) and degradation-restoration sequences (e.g., Daloushan Mountains).
• Identified distinct drivers of vegetation growth: temperature in Southern China/high-altitude areas, rainfall in the North, with limited climate influence in urban areas.
• Observed inconsistencies between vegetation trend types and land management strategies, indicating significant human activity influence.

Conclusions:

• Vegetation dynamics in China are complex, involving multiple restoration and degradation phases.
• Climate and human activities are key drivers, with regional variations in their influence.
• Future research should focus on quantifying human management effects and refining trend-fitting and driving force analysis methods.