Chin J Plant Ecol ›› 2022, Vol. 46 ›› Issue (10): 1280-1288.DOI: 10.17521/cjpe.2022.0235

Special Issue: 遥感生态学 植被生态学

• Research Articles • Previous Articles     Next Articles

Estimation of grassland aboveground biomass using digital photograph and canopy structure measurements

LIU Chao1,2, LI Ping1, WU Yun-Tao1,2, PAN Sheng-Nan1,2, JIA Zhou1,2, LIU Ling-Li1,*()   

  1. 1State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
    2University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2022-06-06 Accepted:2022-08-16 Online:2022-10-20 Published:2022-08-28
  • Contact: LIU Ling-Li
  • Supported by:
    Strategic Priority Research Program of Chinese Academy of Sciences(XDA23080301)

Abstract:

Aims Aboveground biomass (AGB) is one of the most important factors affecting grassland ecosystem function and is commonly measured in grassland research. AGB is often measured using the harvest method, which can cause great disturbance to plant communities, especially for those long-term monitoring plots. A non-destructive method for AGB estimation is thus needed.

Methods Here, we conducted field measurements at a land-use manipulation experiment in a typical steppe in Nei Mongol, China. We obtained the fractional vegetation cover (FVC) using digital photographs. We also measured leaf area index (LAI), vegetation height, and plant species richness. Three different models were used to estimate AGB: univariate regression model, stepwise regression model, and random forest model.

Important findings We found that FVC, LAI, mean vegetation height, maximum vegetation height and richness were highly correlated with AGB variation. AGB can be accurately predicted by a stepwise regression model developed based on the local plant community. The determination coefficient (R2) and root-mean-square error (RMSE) of the stepwise regression model can reach 0.91 and 35.60 g·m-2, respectively. Overall, our study provides a rapid and non-destructive method for AGB measurement that can be used as an alternative to the traditional harvest method.

Key words: fractional vegetation cover, leaf area index, vegetation height, stepwise regression model, random forest model, maximum likelihood classification