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Article

Estimating Grazing Pressure from Satellite Time Series Without Reliance on Total Production

1
State Key Laboratory of Herbage Improvement and Grassland Agro-Ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730000, China
2
School of Environment, The University of Auckland, Auckland 1010, New Zealand
3
State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China
4
Institute of Ecology, College of Urban and Environmental Sciences, Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing 100871, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(22), 3781; https://doi.org/10.3390/rs17223781 (registering DOI)
Submission received: 14 September 2025 / Revised: 4 November 2025 / Accepted: 18 November 2025 / Published: 20 November 2025
(This article belongs to the Section Ecological Remote Sensing)

Abstract

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.
Keywords: grazing pressure; grazing intensity; residual biomass; machine learning models; Qinghai-Tibet plateau grazing pressure; grazing intensity; residual biomass; machine learning models; Qinghai-Tibet plateau

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MDPI and ACS Style

Shi, Y.; Gao, J.; Brierley, G.; Li, X.; He, J.-S. Estimating Grazing Pressure from Satellite Time Series Without Reliance on Total Production. Remote Sens. 2025, 17, 3781. https://doi.org/10.3390/rs17223781

AMA Style

Shi Y, Gao J, Brierley G, Li X, He J-S. Estimating Grazing Pressure from Satellite Time Series Without Reliance on Total Production. Remote Sensing. 2025; 17(22):3781. https://doi.org/10.3390/rs17223781

Chicago/Turabian Style

Shi, Yan, Jay Gao, Gary Brierley, Xilai Li, and Jin-Sheng He. 2025. "Estimating Grazing Pressure from Satellite Time Series Without Reliance on Total Production" Remote Sensing 17, no. 22: 3781. https://doi.org/10.3390/rs17223781

APA Style

Shi, Y., Gao, J., Brierley, G., Li, X., & He, J.-S. (2025). Estimating Grazing Pressure from Satellite Time Series Without Reliance on Total Production. Remote Sensing, 17(22), 3781. https://doi.org/10.3390/rs17223781

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