Chin J Plant Ecol ›› 2022, Vol. 46 ›› Issue (10): 1280-1288.DOI: 10.17521/cjpe.2022.0235
Special Issue: 全球变化与生态系统; 生态学研究的方法和技术; 生态遥感及应用; 植被生态学
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LIU Chao1,2, LI Ping1, WU Yun-Tao1,2, PAN Sheng-Nan1,2, JIA Zhou1,2, LIU Ling-Li1,*()
Received:
2022-06-06
Accepted:
2022-08-16
Online:
2022-10-20
Published:
2022-08-28
Contact:
LIU Ling-Li
Supported by:
LIU Chao, LI Ping, WU Yun-Tao, PAN Sheng-Nan, JIA Zhou, LIU Ling-Li. Estimation of grassland aboveground biomass using digital photograph and canopy structure measurements[J]. Chin J Plant Ecol, 2022, 46(10): 1280-1288.
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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2022.0235
Fig. 1 Original digital photo obtained by digital camera (A) and the photo processed by ENVI (B). The green area in figure B indicates vegetation area, and light-colored area indicates non-vegetation area.
Fig. 2 Correlation matrix among variables in grassland aboveground biomass estimation. The color and size of each ellipse represent the correlation direction and degree between variables; the blue and red colors represent positive and negative correlations, respectively, and the correlation degree is stronger when the red or blue color of each ellipse gets deeper. AGB, aboveground biomass; CV, coefficient of variation for vegetation height; FVC, fractional vegetation cover; H', Shannon-Wiener index; hmax, maximum vegetation height; hmean, mean vegetation height; hmin, minimum vegetation height; LAI, leaf area index; MC, moisture content; richness, species richness. *, p < 0.05; ***, p < 0.001.
Fig. 3 Prediction results of univariate regression model of grassland aboveground biomass. The determination coefficient (R2) and root-mean-square error (RMSE) from linear regression (blue) and exponential regression (red) are given.
Fig. 4 Grassland aboveground biomass prediction models and relative importance of variables. The blue and green solid lines represent the fitted lines of the stepwise regression model (A) and random forest model (B), respectively; the grey dashed line denotes the 1:1 line. The determination coefficient (R2) and root mean square error (RMSE) of the two models are also marked by the corresponding color. The relative importance of variables in the stepwise regression model (C) and random forest model (D) are shown; numbers next to the bars indicate the relative importance of each variable; variables connected by colons represent interactions between variables; the variable importance of stepwise regression model is derived from the contributions of different variables to R2, and the variable importance of random forest model is derived from the mean decrease accuracy. CV, coefficient of variation for vegetation height; FVC, fractional vegetation cover; hmean, mean vegetation height; hmax, maximum vegetation height; hmin, minimum vegetation height; H', Shannon-Wiener index; LAI, leaf area index.
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