Spatiotemporal dynamics of grassland aboveground biomass and its driving factors in North China over the past 20 years

Jing Ge; Mengjing Hou; Tiangang Liang; Qisheng Feng; Xinyue Meng; Jie Liu; Xuying Bao; Hongyuan Gao

doi:10.1016/j.scitotenv.2022.154226

Spatiotemporal dynamics of grassland aboveground biomass and its driving factors in North China over the past 20 years

Sci Total Environ. 2022 Jun 20:826:154226. doi: 10.1016/j.scitotenv.2022.154226. Epub 2022 Feb 28.

Authors

Jing Ge¹, Mengjing Hou¹, Tiangang Liang², Qisheng Feng¹, Xinyue Meng¹, Jie Liu¹, Xuying Bao¹, Hongyuan Gao¹

Affiliations

¹ State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, PR China.
² State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, Engineering Research Center of Grassland Industry, Ministry of Education, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, PR China. Electronic address: tgliang@lzu.edu.cn.

PMID: 35240176
DOI: 10.1016/j.scitotenv.2022.154226

Abstract

Although remote sensing has enabled rapid monitoring of grassland aboveground biomass (AGB) at a regional scale, it is still a difficult challenge to construct an accurate estimation model of grassland AGB in a vast region to support the AGB dynamics analysis over a long time series. In this study, extensive grassland AGB measurements (collected in North China during the grassland growing season of 2000-2019), MODIS data, and environmental factors (climate, topography and soil) were employed to construct the grassland AGB models using four machine learning algorithms (random forest, support vector machine, artificial neural network and extreme learning machine) combined with four variable selections. The spatial distributions of annual grassland AGB from 2000 to 2019 were simulated based on the optimal AGB model. The temporal change and future trend of AGB series from 2000 to 2019 were comprehensively analyzed by the slope model and Hurst exponent. The influences of natural and anthropogenic factors on grassland AGB dynamics were explored quantitatively using the Geodetector model. The results showed that (1) the random forest model constructed from the variables selected by the successive projections algorithm is the optimal grassland AGB model. (2) The 20-year average grassland AGB in North China showed an overall spatial distribution of being low in the central and western parts and high in the southeastern part. (3) The annual maximum grassland AGB in most regions (82.71%) showed an increasing trend during 2000-2019; and most of the grasslands with a decreasing trend of AGB were located in regions with low AGB values and arid climates. (4) The future trend of grassland AGB after the study period may be optimistic, as reflected by more grassland AGB was predicted to increase rather than decrease (70.38% vs. 29.62%). (5) The main driving factors of spatiotemporal dynamics of grassland AGB were precipitation, soil type, and livestock density; the interactive influence of two drivers on AGB showed mutual enhancement.

Keywords: Climate change; Driving factors; Grassland AGB; Model assessment; North China; Spatiotemporal dynamics.

MeSH terms

Biomass
China
Desert Climate
Grassland*
Soil*
Support Vector Machine

Substances

Soil