Abstract
Grasslands on the Mongolian Plateau are critical for supporting local sustainable development. Sufficient measured sample information is the basis of remote sensing modeling and estimation of grassland production. Limited by field inventory costs, it is difficult to collect sufficient and widely distributed samples in the Mongolian Plateau, especially in transboundary areas, which affects the results of grassland production estimation. Here, considering that the measured sample points are sparse, this study took Xilingol League of Inner Mongolia Autonomous Region in China and Dornogovi Province in Mongolia as the study areas, introduced multiple interpolation methods for interpolation experiments, established a statistical regression model based on the above measured and interpolated samples combined with the normalized differential vegetation index, and discussed the applicability of grassland production estimation. The comparison results revealed that the point estimation biased sample hospital-based area disease estimation method and radial basis function showed the best interpolation results for grassland production in Xilingol League and Dornogovi Province, respectively. The power function model was suitable for grassland production estimation in both regions. By inversion, we obtained annual grassland production for 2010–2021 and the uneven spatial distribution of grassland production in both regions. In these two regions, the spatial change in grassland production showed a decreasing trend from northeast to southwest, and the interannual change generally showed a dynamic upward trend. The growth rate of grassland output was faster in Xilingol League than in Dornogovi Province with similar physical geography and climate conditions, indicating that the animal husbandry regulation policies play important roles beyond the influence of climate change. The study recommended grassland estimation methods for an area with sparse samples and the results can be used to support decision making for sustainable animal husbandry and grassland succession management.
Highlights
Grassland resources are the most widely available natural resources in the MongolianPlateau and are major supporting pillars for animal husbandry [1,2]
During the ordinary kriging (OK), radial basis function (RBF), or inverse distance weighting (IDW) interpolation experiments, 20% of measurement sample points were randomly selected as samples
The grassland production distribution in Xilingol League and Dornogovi Province for 2010–2021 showed spatial variations, with gradual decreasing trends from northeast to southwest in both regions, which was similar to the zonal distribution pattern based on the vegetation types of grasslands
Summary
Grassland resources are the most widely available natural resources in the MongolianPlateau and are major supporting pillars for animal husbandry [1,2]. Estimating grassland production in the Mongolian Plateau facilitates raising awareness of resource and environmental issues in the Mongolian Plateau and the monitoring and management of the dynamic evolution of local grassland resources and land degradation, thereby providing scientific data and support for implementing decisions on sustainable animal husbandry and grassland succession management. Interpolation methods allow a wider spatial distribution of the sample points to be obtained [9]. Qiao [14] used multiple data sources and performed geostatistical spatial interpolation using a quadratic spline function on vegetation in Yili grasslands in northern China to obtain aboveground biomass data. Compared with grassland resource surveys, spatial interpolation methods have been widely studied in the regional ecosystem, environment, and public health domains. Wang [15] developed a new technology, biased sample hospital-based area disease estimation (B-SHADE), that used records from sentinel hospitals to estimate regional disease incidence and prevalence; further, it corrected data errors recorded by individual hospitals and generated the best linear unbiased estimation
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
