Soil gravels and plant species configuration control vegetation restoration in Qinghai-Tibet Plateau

Zhou, jinxing; You, Yonggang; Wu, Xinliang; Han, Lihe; Lu, Yapei; Rebi, Ansa; Dong, Qian; Wang, Lina; Zhang, Pengcheng

doi:10.22541/au.169518801.12293461/v1

Cited by 1 publication

(1 citation statement)

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“…The annual gridded grazing intensity maps of the QTP spanning from 1990 to 2020 are accessible at the following link: https://figshare.com/s/ad2bbc7117a56d4fd88d (Zhou et al, 2023). Each map is catalogued by year and recorded in GeoTIFF format, with values represented in SU/hm 2 per year.…”

Section: Data Availabilitymentioning

confidence: 99%

Annual high-resolution grazing intensity maps on the Qinghai-Tibet Plateau from 1990 to 2020

Zhou,

Niu,

et al. 2023

Preprint

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Abstract. Grazing activities constitute the paramount challenge to grassland conservation over the Qinghai-Tibet Plateau (QTP), underscoring the urgency for obtaining detailed extent, patterns, and trends of grazing information to access efficient grassland management and sustainable development. Here, to inform these issues, we provided the first annual Gridded Dataset of Grazing Intensity maps (GDGI) with a resolution of 100 meters from 1990 to 2020 for the QTP. Five most commonly used machine learning algorithms were leveraged to develop livestock spatialization model, which spatially disaggregate the livestock census data at the county level into a detailed 100 m× 100 m grid, based on seven key predictors from terrain, climate, land cover and socioeconomic factors. Among these algorithms, the extreme trees (ET) model performed the best in representing the complex nonlinear relationship between various environmental factors and livestock intensity, with an average absolute error of just 0.081 SU/hm2, a rate outperforming the other models by 21.58 %~414.60 %. By using the ET model, we further generated the GDGI dataset for the QTP to reveal the spatio-temporal heterogeneity and variation in grazing intensities. The GDGI indicates grazing intensity decreased from 1990 to 2001 period, and fluctuated thereafter. Encouragingly, comparing with other open-access datasets for grazing distribution on the QTP, the GDGI has the highest accuracy, with the determinant coefficient (R2) exceed 0.8. Given its high resolution, recentness and robustness, we believe that the GDGI can significantly enhance understanding of the substantial threats to grasslands emanating from overgrazing activities. Furthermore, the GDGI product holds considerable potential as a foundational source for research, facilitating rational utilization of grasslands, refined environmental impact assessments, and the sustainable development of animal husbandry. The GDGI product developed in this study is available at https://figshare.com/s/ad2bbc7117a56d4fd88d (Zhou et al., 2023).

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Section: Data Availabilitymentioning

confidence: 99%