the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An annual 30 m cultivated pasture dataset of the Tibetan Plateau from 1988 to 2021
Abstract. Cultivated pastures have rapidly developed across the Tibetan Plateau over the past several decades, raising concerns about grassland degradation. Accordingly, considerable attention is focused on the protection of Tibetan grassland ecosystems. However, high-resolution spatial distribution of cultivated pastures on the Tibetan Plateau remains poorly understood, primarily due to the difficulty of discriminating cultivated pastures from other land cover types using remote sensing techniques. The absence of such information hinders efficient agricultural and livestock husbandry management, making it challenging to support ecological protection and restoration efforts. Here, we mapped the cultivated pastures on the Tibetan Plateau at a 30-m resolution for the years 1988 to 2021 using Landsat data on the Google Earth Engine (GEE) cloud computing platform. We built a Random Forest (RF) binary classification model with inputs of the spectral-temporal metrics of Landsat data acquired in the growing season, as well as ancillary topographic data. The model was trained using carefully selected training samples and validated against 2,000 independent random reference points in two pilot study regions with different climates and landscapes. The model achieved an overall accuracy of 97.05 % ± 0.4 % and an F1 spatial consistency score of 82.51 % ± 14.22 % (Precision: 90.04 % ± 6.18 %, Recall: 76.74 % ± 9.91 %), suggesting high confidence in mapping the distribution of cultivated pastures on the plateau. Using the RF model, we then produced a dataset of cultivated pasture maps for the years from 1988 to 2021 for Qinghai Province and the Tibet Autonomous Region on the Tibetan Plateau, covering 77 % of the plateau. At both the province and county levels, the cultivated pasture areas estimated in this study matched well with government statistics in recent years. The area of cultivated pastures on the Tibetan Plateau experienced a significant expansion from 0.46 Mha in 1988 to 1.57 Mha in 2021, with the average annual growth of 33.5 ± 2.5 Kha. To our knowledge, we are the first to map cultivated pastures on the Tibetan Plateau, and our RF binary classification approach holds promise in identifying cultivated pastures in other regions of the world, which could prove invaluable for scientists, policymakers, ecological conservation practitioners, and herdsmen. The dataset is available on Zenodo at https://doi.org/10.5281/zenodo.14271782 (Han et al., 2024).
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Status: open (until 16 Mar 2025)
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RC1: 'Comment on essd-2024-620', Anonymous Referee #1, 06 Feb 2025
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I enjoyed this paper. The knowledge gap is well established, the methodology is solid, the results and discussion address the study objectives. I have a few comments for the authors' consideration.
Specific Comments:
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Since this study focuses specifically on Qinghai and Tibet (not the entire Tibetan Plateau), I recommend adjusting the title to more accurately reflect this scope. Additionally, I suggest adding a boundary outlining these two regions in Figure 1 to clearly define the study area. For Figures 9 and 10, a gray shadow as the background could enhance the visibility of the study area and improve contrast, as the current color is hard to discern. Adding the boundary and shaded areas will help readers understand the study area more effectively and avoid potential misinterpretations. For example, based on Figures 9 and 10, I initially concluded that there is no cultivated pasture in Xinjiang, Gansu, and Sichuan, which could be misleading.
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The Methods section could be made clearer with the following revisions:
- The growing season is first introduced in Section 3.1, while the description of quantile extraction appears in Section 3.2. I suggest merging these two sections to ensure a smoother logical flow. For instance, begin with an introduction to SR and the seven spectral indices, followed by an explanation of the satellite products and the growing season sampling process, and conclude with the topography data. Feel free to disregard this suggestion if it doesn’t fit the structure of the paper.
- The use of quantiles is not entirely clear. Were they used as separate model inputs, or did they interact in some way? If they were used individually, the importance of each quantile likely varies for different pixels. Clarifying this would improve understanding.
- Several types of cultivated pasture were used as training data. Since the model is binary (cultivated vs. other), how were these different types handled in the model? Were they treated as equivalent to cultivated pasture, or did the model account for their distinctions? Additionally, how did the model perform across these various types?
- The performance of the model should be presented more explicitly, particularly regarding the importance of different input drivers.
- The field records used to train the model cover only a portion of the study area. Would it be feasible to extend the training data by using high-resolution satellite images for non-pilot regions?
- Spectral and topographic data alone may not be sufficient to accurately predict cultivated pasture, especially over time. I suggest considering additional drivers such as climate variables, soil properties, and human or livestock populations in the modeling process.
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The predicted area in Figure 8(a) appears to be consistently smaller than government statistics. Figure 11(b) also shows that the prediction for Qinghai is under-estimated. Are these discrepancies related to the limitations mentioned in point 1? They need to be clarified.
Technical Comments:
- Figure 3: For better visualization, use two distinguishable colors for the two categories. This will improve clarity and contrast.
Citation: https://doi.org/10.5194/essd-2024-620-RC1 -
Data sets
An annual 30 m cultivated pasture dataset of the Tibetan Plateau from 1988 to 2021 Binghong Han, Jian Bi, Shengli Tao, Tong Yang, Yongli Tang, Mengshuai Ge, Hao Wang, Zhenong Jin, Jinwei Dong, Zhibiao Nan, and Jin-Sheng He https://doi.org/10.5281/zenodo.14271782
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