Effective and accurate assessment of grassland above-ground biomass (AGB) especially via remote sensing (RS), is crucial for forage-livestock balance and ecological environment protection of alpine grasslands. Because of complexity and extensive spatial distribution of natural grassland resources, the RS estimation models based on moderate resolution imaging spectroradiometer (MODIS) data exhibited low accuracy and poor stability. In this study, various methods for estimating the AGB of alpine grassland vegetation using MODIS vegetation indices were evaluated by combining with meteorology, soil, topography geography and in situ measured AGB data (during grassland growing season from 2011 to 2016) in Gannan region. Results show that (1) five out of ten factors (elevation, slope, aspect, topographic position, temperature, precipitation and the concentration of clay and sand in the soil) exert significant effects on grassland AGB, with R2 0.04~0.39, and RMSE 859.68 kg/ha ~ 1075.09 kg/ha, respectively; (2) the accuracy and stability of AGB estimation model can be improved by constructing multivariate models, especially using multivariate non-parameter models; (3) the optimum estimation model is constructed on the basis of random forest algorithm (RF). Compared with univariate/multivariate parameter models, RMSE of RF model decreased 26.45 % - 44.27 %. Meanwhile, RF models can explain 89.41 % variation in AGB . This study presented a more suitable RS inversion model integrated MODIS vegetation indices and other effect factors. Besides, the accuracy of alpine grassland AGB estimation based on MODIS data were greatly improved. Thus, our study provides a scientific basis for effective and accurate estimating alpine grassland AGB.

Grassland height is one of the main factors used to evaluate grassland conditions. However, the retrieval of natural grassland height at the regional scale by remote sensing data and conventional statistical models will result in large errors, especially in the heterogeneous alpine grassland of the Tibetan Plateau (TP). In this article, we aimed to construct a model based on multiple variables (biogeographical, meteorological, and Moderate Resolution Imaging Spectroradiometer (MODIS) product) using a random forest (RF) algorithm to predict the spatial distribution of grassland height in the TP from 2003 to 2017. The results show the following conditions. 1) Seven variables (elevation, slope, aspect, enhanced vegetation index, reflectance in band seven of MODIS (B7), annual accumulated temperature (≥0 °C), and annual precipitation) that were selected by recursive feature elimination from 11 variables have high importance in the RF model. The final model exhibits good performance, with mean R ² and root mean squared error values of 0.51 and 6.15 cm, respectively, which were determined via 10-fold crossvalidation. 2) The mean grassland height (2003-2017) predicted by the RF model ranges from 5 to 10 cm in most areas of the TP, and the mean height is 10 cm. The grassland height in the east and southeast of the TP is significantly higher than that in other areas. 3) This article achieves a relatively accurate estimation of grassland height over a large spatial scale at 500-m spatial resolution, which plays an important role in accurately estimating aboveground biomass and evapotranspiration over grassland.

Algorithm of wireless network selection is used to ensure that users can access to the most suitable network when the connected network can't satisfy users' requirement. Previous network selection algorithms are based on single attribute of wireless network. In the next generation network, it will make error if the algorithm still adopts single standard because the complexity is increased, so the algorithm should adopt several network performance evaluation, it is also known as multiple attribute decision making (MADM). This paper proposes an algorithms based on MADM, named wireless network selection algorithm based on independence of influence factors of QoS (WNSAIFQ). WNSAIFQ complies with the principle that the lower nodes are the reason of the upper nodes strictly, this removes the interdependence between those network's properties effectively. The experiment results based on a hardware network platform showed that this algorithm is better than others in aspects of network selection and error control.