Applicability of Grassland Production Estimation Using Remote Sensing for the Mongolian Plateau by Comparing Typical Regions in China and Mongolia

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.

Spatiotemporal Dynamics of Grasslands Using Landsat Data in Livestock Micro-Watersheds in Amazonas (NW Peru)

“…In recent years, new remote sensing technologies, such as GEE, radar images and the use of Remotely Piloted Aircraft System (RPAS) equipped with hyperspectral cameras and machine learning algorithms, have allowed more accurate predictions of grassland quality. Grassland mapping allows distinguishing different grassland ecologies that influence management practices, degradation and productivity over time [99][100][101]. In our study, multitemporal images were analyzed that allowed us to monitor the dynamics of the grasslands in the study area, reporting consistent accuracies and demonstrating the increase in the grasslands in both micro-watersheds.…”

Sustainable application of GF-6 WFV satellite data in desert steppe: A village-scale grazing study in China

“…Current research on grass production in grasslands used for grazing using remote sensing technology still focuses on the steps needed to estimate biomass (Reinermann, Asam, and Kuenzer 2020;Zhao et al, 2020;Li et al, 2022). The results of these studies are reliable and well worthwhile in terms of the accuracy of estimation.…”