Monitoring grazing activities on grassland is crucial for ensuring sustainable grassland development and for protecting it from grazing-led degradation. The Leaf Area Index (LAI), which measures leaf coverage over a surface area, is commonly used as a proxy for grassland condition. However, current studies focus on the year-round or seasonal aggregated LAI change rather than the change that can be attributed explicitly to grazing, which is the important indicator for quantifying grassland grazing. This paper presents a new exponential growth function under grazing with an estimation algorithm, the purpose of which is to extract grazing-led LAI changes for every 8 days' satellite observations. All the analyses are based on the Moderate Resolution Imaging Spectroradiometer (MODIS) MOD15A2H products. An improved MODIS LAI and an expected LAI are produced separately, considering both current and previous grazing-led LAI changes. The differences between expected LAI and improved LAI are then converted to the equivalent carbon mass of grazed material. This grazed carbon mass is then aggregated within the growing season, and compared with the expected carbon mass consumed by livestock (calculated from statistics yearbooks). In addition, Net Primary Productivity (NPP) is produced using the improved LAI, simulated by a Light Use Efficiency with Vegetation Photosynthesis Model (LUE-VPM). This is compared with the NPP produced by LUE-VPM based on original MODIS LAI, MODIS NPP products (MOD17A2H) and grassland monitoring stations' in situ measured data. Results show that the NPP calculated from the improved LAI is statistically the same as in situ converted NPP with a p-value equalling 0.998 (the RMSE between the two is 97.77 gC/m2). Conversely, the p-value between converted in situ measured carbon mass and the MODIS NPP product is 0.011 (the RMSE between the two is 133.98 gC/m2), indicating they are statistically different. The results detailed in this paper provide precise and almost real-time grassland grazing monitoring information for policy makers managing grassland.

中文翻译：

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使用基于 MODIS 叶面积指数的新生长函数量化放牧模式

监测草地放牧活动对于确保草地可持续发展和保护草地免受放牧导致的退化至关重要。叶面积指数 (LAI) 测量表面积上的叶子覆盖率，通常用作草地状况的代理。然而，目前的研究侧重于全年或季节性的总 LAI 变化，而不是可明确归因于放牧的变化，而放牧是量化草地放牧的重要指标。本文提出了一种新的带估计算法的放牧下指数增长函数，其目的是提取每8天卫星观测的放牧导致的LAI变化。所有分析均基于中分辨率成像光谱仪 (MODIS) MOD15A2H 产品。考虑到当前和以前的放牧导致的 LAI 变化，分别产生改进的 MODIS LAI 和预期的 LAI。然后将预期的 LAI 和改进的 LAI 之间的差异转换为放牧材料的等效碳质量。然后将这些放牧的碳量在生长季节内汇总，并与牲畜消耗的预期碳量（根据统计年鉴计算）进行比较。此外，净初级生产力 (NPP) 是使用改进的 LAI 产生的，由光利用效率和植被光合作用模型 (LUE-VPM) 模拟。这与LUE-VPM基于原始MODIS LAI、MODIS NPP产品（MOD17A2H）和草地监测站现场测量数据产生的NPP进行了比较。结果表明，从改进的 LAI 计算出的 NPP 在统计上与原位转化 NPP 相同，p 值等于 0.998（两者之间的 RMSE 为 97.77 gC/m2）。相反，转换的原位测量碳质量与 MODIS NPP 产品之间的 p 值为 0.011（两者之间的 RMSE 为 133.98 gC/m2），表明它们在统计上是不同的。本文详述的结果为管理草地的决策者提供了精确且几乎实时的草地放牧监测信息。