Quantifying Grazing Intensity from Aboveground Biomass Differences Using Satellite Data and Machine Learning
文献类型: 外文期刊
作者: Ritu Su;Yong Yang;Shujuan Chang;A. Gudamu;Xiangjun Yun;Xiangyang Song;Aijun Liu
作者机构:
关键词: aboveground biomass;aboveground biomass difference;ecological monitoring;grazing intensity;Inner Mongolia;machine learning;rangeland management;remote sensing
期刊名称: Agronomy
ISSN: 2073-4395
年卷期: 2025 年 15 卷 11 期
页码:
收录情况: SCIE(2025版)
摘要: Accurately quantifying grazing intensity (GI) is crucial for assessing grassland utilization and supporting sustainable management. Traditional livestock-based approaches cannot capture the spatial heterogeneity of grazing or its dynamic response to climate variability. The objective of this study was to develop a remote sensing-based quantitative framework for estimating GI across the Inner Mongolian grasslands. The framework integrates MODIS vegetation indices, ERA5-Land climate variables, topographic factors, and field-measured data and GI was quantified as the proportional difference between potential and satellite-derived aboveground biomass (AGB), providing a spatially explicit measure of forage utilization. In this framework, potential AGB (AGBp) represents the climate-driven growth capacity under ungrazed conditions reconstructed using machine learning models, whereas satellite-derived AGB (AGBs) denotes the standing AGB remaining under current grazing pressure. Validation using 324 paired grazed–ungrazed plots demonstrated strong agreement between modeled and observed GI (R2 = 0.65, RMSE = 0.18). This AGB-difference-based approach provides an effective and scalable tool for large-scale rangeland monitoring, offering quantitative insights into grass–livestock balance, ecological restoration, and adaptive management in arid and semi-arid regions.
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