Abstract：
Estimating the grass yield of a grassland area is of vital theoretical and practical significance for determining grazing capacity and maintaining ecological balance. Due to the spatial inconsistency between sampling and remote sensing data, improving the accuracy of fresh grass yield (FGY) estimation based on remote sensing is difficult. Using vegetation coverage at different spatial scales, this paper proposes a spatial scale transformation (SST)-based estimation model for FGY adopting normalized difference vegetation index (NDVI) as its estimation factor, using the grassland in Xilingol League, Inner Mongolia, as the study area. Results showed that the SST-based FGY estimation model was able to greatly improve estimation precision; the relative estimation error (REE) of the estimation models constructed using linear with intercept zero (linear-0) and power functions were 18.16% and 18.35%, respectively. The estimation models constructed using linear-0 and power functions were employed to estimate the grass yield of the grassland in Xilingol League, and the total FGYs estimated were 8.777 × 1010 kg and 8.583 × 1010 kg, respectively. The two models obtained roughly the same estimates, but there were significant differences between them in the spatial distributions of FGY per unit. Taking net primary productivity (NPP) as an example, the effectiveness of other remote sensing data as estimation factors was further verified, and the results showed that SST-based estimation for FGY also effectively improved the estimation accuracy of grass yield.
摘要：
草原区牧草产量的估算对于确定放牧能力和维持生态平衡具有重要的理论和现实意义。由于采样数据和遥感数据之间的空间不一致,提高基于遥感的鲜草产量(FGY)估算精度是一个难点。利用不同空间尺度的植被覆盖度，本文提出了一种基于空间尺度变换（SST）的FGY估计模型，采用归一化差异植被指数（NDVI）作为其估计因子，利用锡林郭勒盟的草原，内蒙古,作为研究领域。结果表明，基于SST的FGY估计模型能够大大提高估计精度；使用截距为零的线性（线性-0）和幂函数构建的估计模型的相对估计误差（REE）分别为18.16％和18.35％。分别。利用线性-0和幂函数构建的估算模型对锡林郭勒盟草地的草产量进行了估算，估计的总FGYs为8.777×1010千克和8.583×1010千克，分别。这两个模型得到了大致相同的估计，但它们在单位FGY的空间分布上存在显著差异。以净初级生产力(NPP)为例，进一步验证了其他遥感数据作为估算因子的有效性,结果表明，基于SST的FGY估算还有效地提高了草产量的估算精度。
