04 Dec 2023
 | 04 Dec 2023
Status: this preprint is currently under review for the journal ESSD.

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

Jia Zhou, Jin Niu, Ning Wu, and Tao Lu

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 (Zhou et al., 2023).

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Jia Zhou, Jin Niu, Ning Wu, and Tao Lu

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on essd-2023-403', Shiliang Liu, 11 Dec 2023
    • AC1: 'Reply on CC1', Tao Lu, 06 Feb 2024
      • CC2: 'Reply on AC1', Shiliang Liu, 07 Feb 2024
  • RC1: 'Comment on essd-2023-403', Anonymous Referee #1, 06 Jan 2024
    • AC2: 'Reply on RC1', Tao Lu, 06 Feb 2024
  • RC2: 'Comment on essd-2023-403', Anonymous Referee #2, 07 Feb 2024
    • AC4: 'Reply on RC2', Tao Lu, 21 Mar 2024
  • RC3: 'Comment on essd-2023-403', Anonymous Referee #3, 17 Mar 2024
    • AC3: 'Reply on RC3', Tao Lu, 21 Mar 2024
Jia Zhou, Jin Niu, Ning Wu, and Tao Lu

Data sets

Annual high-resolution grazing intensity maps on the Qinghai-Tibet Plateau from 1990 to 2020 Jia Zhou, Jin Niu, Ning Wu, Tao Lu

Jia Zhou, Jin Niu, Ning Wu, and Tao Lu


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Short summary
The study provided an annual 100-meter resolution glimpse into the grazing activities across the Qinghai-Tibet Plateau. The newly minted Gridded Dataset of Grazing Intensity (GDGI) not only boasts exceptional accuracy but also acts as a pivotal resource for further research and strategic planning, with the potential to shape sustainable grazing practices, guide informed environmental stewardship, and ensure the longevity of the region’s precious ecosystems.