Random forest method for analysis of remote sensing inversion of aboveground biomass and grazing intensity of grasslands in Inner Mongolia, China

ABSTRACT

The quantification of grassland above-ground biomass (AGB) and grazing intensity (GI) and their distribution in space is of great significance to grassland management and eco-conservation. Remote-sensing technology is widely applied, but it is difficult to measure accurately when monitoring GI. In this study, the neural network, random forest and statistical function models of the relationship between Landsat NDVI and AGB were constructed by field survey and literature data collection in Inner Mongolia grassland, China. By comparing the accuracy among the three models, we constructed a remote-sensing retrieving model of grass AGB. We also estimated the grassland AGB during the peak growing season (August) for Inner Mongolia. Frequency histograms were then made to identify AGB thresholds under four GI levels (light or ungrazed, moderate grazing, overgrazing and extreme grazing) for each of three grassland types (meadow steppe, typical steppe and desert steppe). This study shows that the random forest model simulates grass AGB more accurately than other models. The spatial distribution of AGB in Inner Mongolia grasslands showed a tendency of decreasing from southeast to northwest, with an increasing trend in the last 10 years. The four GI levels in 2021 accounted for 18%, 25%, 36% and 21% of the grasslands in Inner Mongolia, respectively, and over the last 10 years the GI first improved and then deteriorated. This study provides a guideline to remote monitoring for grassland AGB and GI, and supplies scientific support for sustainable management and grassland restoration of large-scale grasslands.

KEYWORDS:

• Inner Mongolia Grassland
• Aboveground biomass
• Grazing intensity
• Neural Networks
• Random Forest

Acknowledgements

This research was funded by Technological Achievements of Inner Mongolia Autonomous Region of China (Grant no. 2020CG0054 and 2022YFDZ0050); National Natural Science Foundation of China, under grant of 52079063 and 52279017; Inner Mongolia Autonomous Region Science and Technology Leading Talent Team (2022LJRC0007), and the Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region (NMGIRT2313).

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Data availability statement

The data that support the findings of this study are available from the corresponding author, Zhang S.W., upon reasonable request.

Supplementary Material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/01431161.2023.2210724

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [52079063,52279017]; Technological Achievements of Inner Mongolia Autonomous Region of China [2020CG0054,2022YFDZ0050]; Inner Mongolia Autonomous Region Science and Technology Leading Talent Team [2022LJRC0007]; Program for Innovative Research Team in Universities of Inner Mongolia Autonomous Region [NMGIRT2313]