Estimating Grazing Pressure from Satellite Time Series Without Reliance on Total Production
Accurately assessing grazing impacts is essential for sustaining alpine grasslands. Conventional approaches often rely on total forage productivity, an indirect and uncertain proxy for forage availability. In this study, we propose a novel framework for estimating grazing pressure that integrates residual biomass with grazing intensity, thereby overcoming the limitations and uncertainties inherent in total forage-based assessments. Our results reveal pronounced spatiotemporal variation in grazing intensity: lowland areas experienced the highest intensity early in the growing season, whereas upland areas became more heavily grazed later in the season. However, grazing intensity alone proved insufficient to explain grazing pressure or predict pasture degradation risk. Overlay analyses demonstrated that only 38.8% of high intensity areas identified as under high grazing pressure, and more than 40% of high intensity area exhibiting substantial aboveground biomass. These findings highlight the limited explanatory power of grazing intensity when considered in isolation. By explicitly incorporating standing biomass rather than relying merely on total production, the proposed framework reduces estimation uncertainty, enhances ecological realism, and provides a scalable, more accurate and practical tool for monitoring grassland utilization and degradation.
Authors
- Jay Gao (ORCID: https://orcid.org/0000-0003-2760-523X)
- Yan Shi
- Jin He (ORCID: https://orcid.org/0000-0001-5081-3569)
- Xilai Li (ORCID: https://orcid.org/0000-0001-9171-2481)
- Gary Brierley
Institutions
- Qinghai University (CN)
- University of Auckland (NZ)
- Peking University (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- Remote Sensing
- Published
- 2025-11-20
- DOI
- https://doi.org/10.3390/rs17223781
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation
- Science and Technology Department of Gansu Province
- Alliance of International Science Organizations