Spatial quantification method of grassland utilization intensity on the Qinghai-Tibetan Plateau: A case study on the Selinco basin

Spatiotemporal patterns and alleviating of grassland overgrazing under current and future conditions in Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) is a crucial region for biodiversity conservation and global ecosystem services supply, and its grassland carrying capacity (GCC) is directly related to both herders' livelihoods and the implementation of ecosystem conservation and restoration programs. However, the spatiotemporal distribution of GCC within the QTP and grasslands' responses to future climate change remain unclear. Here, we evaluated the spatiotemporal characteristics of GCC for 60 years and its load during 2000-2020, and then classified levels of early-warning based on whether the grassland is currently overgrazed, and whether the overgrazing is increasing over time. Results showed that (1) GCC increased from 86.15 million to 97.70 million sheep units (SU) during 2000-2020, and GCC was higher in the east than that in the west QTP. (2) Overgrazing was alleviated due to livestock reduction after 2010 and GCC has grown more, but remains serious. The counties with stronger GCC showed more serious overgrazing. (3) Annual mean GCC was projected to increase by 0.05 SU·km-2 and 1.22 SU·km-2 under Shared Socioeconomic Pathways (SSPs) 126 and SSP460, respectively. Overgrazing is likely to be greatly alleviated in the future with climate change and livestock reductions in the QTP. Our study gives practical advice for adjusting the development pattern of grassland animal husbandry and provides recommendations for key implementation areas of ecosystem conservation and restoration programs of grassland ecosystems.

A long-term high-resolution dataset of grasslands grazing intensity in China

Grazing is a significant anthropogenic disturbance to grasslands, impacting their function and composition, and affecting carbon budgets and greenhouse gas emissions. However, accurate evaluations of grazing impacts are limited by the absence of long-term high-resolution grazing intensity data (i.e., the number of livestock per unit area). This study utilized census livestock data and a satellite-based vegetation index to develop the first Long-term High-resolution Grazing Intensity (LHGI) dataset of grassland in seven pastoral provinces in western China from 1980 to 2022. The LHGI dataset effectively captured spatial variations in grazing intensity, with validation at 73 sites showing a correlation coefficient (R2) of 0.78. The county-level validation showed an averaged R2 values of 0.73 ± 0.03 from 1980 to 2022. This dataset serves as a vital resource for estimating grassland carbon cycling and livestock system CH4 emissions, as well as contributing to grassland management.

A high-resolution gridded grazing dataset of grassland ecosystem on the Qinghai-Tibet Plateau in 1982-2015

Grazing intensity, characterized by high spatial heterogeneity, is a vital parameter to accurately depict human disturbance and its effects on grassland ecosystems. Grazing census data provide useful county-scale information; however, they do not accurately delineate spatial heterogeneity within counties, and a high-resolution dataset is urgently needed. Therefore, we built a methodological framework combining the cross-scale feature extraction method and a random forest model to spatialize census data after fully considering four features affecting grazing, and produced a high-resolution gridded grazing dataset on the Qinghai-Tibet Plateau in 1982-2015. The proposed method (R2 = 0.80) exhibited 35.59% higher accuracy than the traditional method. Our dataset were highly consistent with census data (R2 of spatial accuracy = 0.96, NSE of temporal accuracy = 0.96) and field data (R2 of spatial accuracy = 0.77). Compared with public datasets, our dataset featured a higher temporal resolution (1982-2015) and spatial resolution (over two times higher). Thus, it has the potential to elucidate the spatiotemporal variation in human activities and guide the sustainable management of grassland ecosystem.

NDVI-Based Greening of Alpine Steppe and Its Relationships with Climatic Change and Grazing Intensity in the Southwestern Tibetan Plateau

Alpine vegetation on the Southwestern Tibetan Plateau (SWTP) is sensitive and vulnerable to climate change and human activities. Climate warming and human actions (mainly ecological restoration, social-economic development, and grazing) have already caused the degradation of alpine grasslands on the Tibetan Plateau (TP) to some extent. However, it remains unclear how human activities (mainly grazing) have regulated vegetation variation under climate change and ecological restoration since 2000. This study used the normalized difference vegetation index (NDVI) and social statistic data to explore the spatiotemporal changes and the relationship between the NDVI and climatic change, human activities, and grazing intensity. The results revealed that the NDVI increased by 0.006/10a from 2000 to 2020. Significant greening, mainly distributed in Rikaze, with partial browning, has been found in the SWTP. The correlation analysis results showed that precipitation is the most critical factor affecting the spatial distribution of NDVI, and the NDVI is correlated positively with temperature and precipitation in most parts of the SWTP. We found that climate change and human activities co-affected the vegetation change in the SWTP, and human activities leading to vegetation greening since 2000. The NDVI and grazing intensity were mainly negatively correlated, and the grazing caused vegetation degradation to some extent. This study provides practical support for grassland use, grazing management, ecological restoration, and regional sustainable development for the TP and similar alpine areas.