The Estimation of Grassland Aboveground Biomass and Analysis of Its Response to Climatic Factors Using a Random Forest Algorithm in Xinjiang, China

• Published in: Plants (Basel) Vol. 13; no. 4; p. 548
• Main Authors: Dong, Ping, Jing, Changqing, Wang, Gongxin, Shao, Yuqing, Gao, Yingzhi
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.02.2024; MDPI
• Subjects: aboveground biomass; Accuracy; Algorithms; Artificial intelligence; Biomass; Carbon; Carbon cycle; China; Climate; Climate change; climatic factors; Environmental aspects; Estimation; grasses; grassland type; Grasslands; Humidification; Landsat satellites; Livestock; Machine learning; Meadows; Measurement; Meteorological data; physiological state; Plant biomass; random forest; Regional development; Regression analysis; Remote observing; Remote sensing; Spatial distribution; Support vector machines; Sustainable use; temperature; Variables; Vegetation; Vegetation index; Water conservation; China; Loess Plateau; grassland type; aboveground biomass; random forest; climatic factors

Aboveground biomass (AGB) is a key indicator of the physiological status and productivity of grasslands, and its accurate estimation is essential for understanding regional carbon cycles. In this study, we developed a suitable AGB model for grasslands in Xinjiang based on the random forest algorithm...