Abstract Monitoring changes in grassland cover is essential in assessment of grassland health as well as the effects of anthropogenic interventions and global climate change on grassland ecosystems. Remote sensing is an effective approach for providing rapid and dynamic monitoring of vegetation cover over large grassland areas. In this study, four types of remote sensing retrieval models (i.e., pixel dichotomy models, univariate vegetation index (VI) regression models, multivariate regression models, and a support vector machine (SVM) model) are built to derive grassland cover based on moderate resolution imaging spectroradiometer (MODIS) data and the measured grassland cover data collected by unmanned aerial vehicle during the grassland peak growing season from 2014 to 2016. The optimal model is then used to map the spatial distribution of grassland cover and its dynamic change in the headwater region of the Huanghe River (Yellow River) (HRHR) of the northeastern Tibetan Plateau over the 16 years period (2001 to 2016). The results show that (1) the pixel dichotomy models based on MODIS VI data are inappropriate for estimating grassland cover in the HRHR when their endmembers (VIsoil and VIveg) are determined based only on the MODIS data; (2) the multivariate regression models present better performance than the univariate VI (normalized difference vegetation index (NDVI) or enhanced vegetation index (EVI)) models; (3) MODIS NDVI outperforms MODIS EVI for modeling grassland cover in the study area; (4) the SVM model based on nine factors is the optimal model (R2: 0. 75 and RMSE: 6.85%) for monitoring alpine grassland cover in the study area; and (5) majority of the grassland area (59.9%) of the HRHR showed increase in yearly maximum grassland cover from 2001 to 2016, while the average yearly maximum grassland cover for the 16 years exhibited a generally increasing trend from west to east and from north to south. This study provides a more suitable remote sensing inversion model to greatly improve the accuracy of modeling alpine grassland cover in the HRHR, and to better assess grassland health status and the impacts of warming climate to grasslands in regions of remote and harsh environments.

中文翻译：

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基于MODIS数据和支持向量机回归的黄河源区高寒草地覆盖建模

摘要 监测草地覆盖变化对于评估草地健康以及人为干预和全球气候变化对草地生态系统的影响至关重要。遥感是一种有效的方法，可以对大草原区域的植被覆盖进行快速动态监测。本研究建立了四种遥感反演模型（即像素二分模型、单变量植被指数（VI）回归模型、多元回归模型和支持向量机（SVM）模型），基于中等分辨率成像光谱仪（MODIS）数据和无人机在2014-2016年草地生长高峰期采集的实测草地覆盖数据。然后利用最优模型绘制了青藏高原东北部黄河（黄河）源区（HRHR）16年（2001-2016年）草地覆盖空间分布及其动态变化图。结果表明：（1）基于MODIS VI数据的像元二分模型在其端元（VIsoil和VIveg）仅基于MODIS数据确定时，不适用于HRHR草地覆盖估计；(2) 多元回归模型比单变量VI（归一化差异植被指数（NDVI）或增强植被指数（EVI））模型表现更好；(3) MODIS NDVI 在模拟研究区草地覆盖方面优于 MODIS EVI；(4)基于九个因素的SVM模型为最优模型(R2: 0. 75 and RMSE: 6. 85%) 用于监测研究区高寒草地覆盖；(5) 2001-2016 年，HRHR 的大部分草地面积（59.9%）呈现出年最大草地覆盖率的增加，而 16 年的年均最大草地覆盖率总体呈自西向东增加的趋势。从北到南。本研究提供了更合适的遥感反演模型，大大提高了HRHR高寒草地覆盖建模的准确性，更好地评估了草地健康状况以及气候变暖对偏远恶劣环境地区草地的影响。而16年平均最大草地覆盖率总体呈自西向东、自北向南增加的趋势。本研究提供了更合适的遥感反演模型，大大提高了HRHR高寒草地覆盖建模的准确性，更好地评估草原健康状况以及气候变暖对偏远恶劣环境地区草地的影响。而16年平均最大草地覆盖率总体呈自西向东、自北向南增加的趋势。本研究提供了更合适的遥感反演模型，大大提高了HRHR高寒草地覆盖建模的准确性，更好地评估了草地健康状况以及气候变暖对偏远恶劣环境地区草地的影响。