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Related Concept Videos

Residual Plots01:07

Residual Plots

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A residual plot is a statistical representation of data used to analyze correlation and regression results. It helps verify the requirements for drawing specific conclusions about correlation and regression. To obtain the residual plot, first, the residual for each data value is calculated, which is simply the vertical distance between the observed and the predicted value obtained from the regression equation.
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Updated: Sep 9, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Attribution of vegetation changes in China based on improved residual trend method.

Rong Pan1,2,3, Jian-Guo Sun1,2,3, Bo-Yang Hu1,2,3

  • 1Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China.

Ying Yong Sheng Tai Xue Bao = the Journal of Applied Ecology
|August 31, 2025
PubMed
Summary
This summary is machine-generated.

A new spatially integrated model improves vegetation change attribution by reducing human disturbance. This method accurately quantifies climate and human impacts, showing human factors drove 87.9% of China's vegetation changes from 2003-2022.

Keywords:
attribution of vegetation changehuman disturbanceiterationresidual trend methodspatial reference

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Area of Science:

  • Earth and Environmental Sciences
  • Ecology and Conservation
  • Remote Sensing and Geospatial Analysis

Background:

  • The Residual Trend method is crucial for attributing vegetation changes.
  • Accurate attribution requires minimizing human disturbance signals in vegetation-climate models.
  • Traditional pixel-by-pixel models are limited by relying solely on temporal references.

Purpose of the Study:

  • To develop a spatially integrated vegetation-climate relationship model to enhance the Residual Trend method.
  • To accurately distinguish between climate-driven and human-driven vegetation changes.
  • To analyze vegetation dynamics in China from 2003 to 2022.

Main Methods:

  • Construction of a spatially integrated vegetation-climate relationship model.
  • Development of an iterative scheme for selecting spatial references to suppress human disturbance.
  • Application of the enhanced Residual Trend method to analyze vegetation changes in China.

Main Results:

  • China's vegetation showed an overall increasing trend (0.002·a-1) from 2003-2022.
  • Significant spatial variations in vegetation distribution were observed, influenced by the Heihe-Tengchong line.
  • Human factors accounted for 87.9% of vegetation changes, with ecological policies and agricultural management driving improvements, while overgrazing and urbanization caused degradation.

Conclusions:

  • The new spatially integrated model significantly improves the accuracy of the Residual Trend method.
  • The model effectively quantifies the relative contributions of climate and human factors to vegetation change.
  • Human activities are the dominant driver of recent vegetation dynamics in China.