基于稀疏样点的蒙古国产草量估算方法研究

产草量是衡量草原生产力和诊断草原健康状况的指标,是草地资源管理的重要依据。近年来,遥感数据结合地面实测数据建模已成为产草量估算的重要手段。充足的实测样点信息是产草量遥感建模估算的基础。受境外采样多重因素的制约,蒙古国产草量估算研究中无法获取足够且分布均匀的实测样点,估产模型的精度受到影响,这一问题目前尚未发现有好的解决方法。本研究选取中蒙铁路沿线（蒙古段）两侧200 km缓冲区作为研究区,针对产草量遥感估算中野外样点稀少且分布不均的问题,引入P-BSHADE方法,基于多年NDVI数据和获取的少量地面实测样点数据,考虑草地分布的非均匀性以及样点之间的相关性,对均匀分布的模拟样点处的产草量数据进行插值实验。结果显示,P-BSHADE法的插值效果优于Kriging法,可得到均匀分布于研究区的样点。基于以上实测样点和插值样点,结合NDVI、EVI、PsnNet 3种植被指数进行遥感建模,最优模型精度达到80%,精度优于已有相关研究。选取其中最优的基于NDVI的指数模型对研究区2000—2019年产草量进行反演,获得的产草量空间格局与年际变化与已有研究结果趋势吻合,进一步印证了结果的可靠性和插值方法的可行性。本研究通过插值的方式改善数据源从而提高估算模型精度是一种全新的思路与尝试,对于“一带一路”等境外区域资源环境监测具有借鉴意义。

Study on Estimation Method of Mongolia Grassland Production based on Sparse Samples

Grasslands are one of the most widely distributed land cover vegetation types across the globe. They play a significant role in developing animal husbandry, protecting biodiversity, maintaining soil and water, and keeping ecological balance. Estimating grassland production, a fundamental variable in grassland resource management, is helpful to measure grassland productivity and diagnose its health status. In recent years, the combination of remote sensing and ground measurements into models has become an important method of estimating grassland production. Normally, large number of measurements are required for remote sensing modeling. Mongolia is an example of a traditional grassland animal husbandry country with the largest per capita grassland area in the world and is also part of the China-Mongolia-Russia Economic Corridor under the “Belt and Road” initiative. Constrained by multiple factors of overseas sampling, it is usually difficult to obtain sufficient, accurate, and evenly distributed production samples. Thus, the accuracy of estimation models will be affected. Until now, there is still no effective solutions to get more samples. In this study, a 200-kilometer buffer zone along the China-Mongolia Railway (Mongolia) was taken as the study area. Given the inhomogeneity of grassland distribution and the correlation between the samples, the Point estimation model of BSHADE (P-BSHADE) was introduced. We derived the grassland production dataset in the study area from 2000 to 2019 based on the sample measurements and interpolated samples, and a combination of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Net Photosynthesis (PsnNet) for remote sensing modeling. Our method extrapolated sparse and unevenly distributed sampling points to supplement ground information by spatial interpolation, and used both the measured sample points and interpolated sample points for modeling. Six types of linear models and exponential models were established using above three vegetation indices. Our results show that the accuracy of the optimal model was 80%, higher than that from previous studies. The spatial pattern and interannual variation of grassland production estimated in our study were consistent with previous studies, which further confirmed the accuracy of our results and the feasibility of the interpolation method. Using interpolation method to optimize the data source is an entirely new attempt that improve the accuracy of the model estimation, which could be potentially applied to other overseas regions to monitor grassland resources.