Influences of Family Ranches Management Mode on Plant Community Characteristic in Hulunber Meadow Steppe

[Objective] The aim was to explore the management mode on optimal re-sources al ocation of family ranch in meadow steppe. [Method] Three double repre-sentative family ranches were selected in meadow steppe of Hulunber Old Barag Banner, and the study was carried out with the baseline survey. Three family ranches were selected as the demonstrative households for the corporation study, while other three family ranches with the similar conditions were looked as the non-demonstrative households for the comparison. Demonstrative households of the fami-ly ranches reduced the stocking rate, optimized the flock structure and took a winter feeding and other means to explore the different management models on plant com-munity characteristic of family ranch. [Result] The seasonal dynamic of community characteristic in family ranches showed the single-peaks curves. The seasonal dy-namics of community coverage, height and biomass in the demonstrative households showed higher compared with the non-demonstrative households, and community density in the experiment households was lower than that of the control experiment households. Community coverage, height and biomass of degraded grassland in family ranch have a great improve after optimization of management. Community coverage, height , density and biomass were increasing in fencing plot, but decreas-ing in free grazing area. Enclosure improved grassland coverage, vegetation height, density and forage yield. Leymus chinensis played an important role in plant com-munity. The important values of Leymus chinensis, Stipa baicalensis, Cleistogenes squarrosa, and Carex duriuscula were high. Leymus chinensis important value in the demonstrative households of optimal management was higher than that in the non-demonstrative households, and Carex duriuscula important value of the non-demon-strative households was significantly higher than that of the demonstrative house-holds. The indexes of Margalef richnes,Shannon-Wiener diversity, Simpson diversity and Pielou uniformity showed that the demonstrative households were higher than the non-demonstrative households. [Conclusion] The research provides theoretical ref-erences for sustainable development of pastures dominated by family ranch.

Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China

Leaf area index （LAI） is a key parameter for describing vegetation structures and is closely associated with vegetative photosynthesis and energy balance. The accurate retrieval of LAI is important when modeling biophysical processes of vegetation and the productivity of earth systems. The Random Forests （RF） method aggregates an ensemble of deci- sion trees to improve the prediction accuracy and demonstrates a more robust capacity than other regression methods. This study evaluated the RF method for predicting grassland LAI using ground measurements and remote sensing data. Parameter optimization and variable reduction were conducted before model prediction. Two variable reduction methods were examined： the Variable Importance Value method and the principal component analysis （PCA） method. Finally, the sensitivity of RF to highly correlated variables was tested. The results showed that the RF parameters have a small effect on the performance of RF, and a satisfactory prediction was acquired with a root mean square error （RMSE） of 0.1956. The two variable reduction methods for RF prediction produced different results; variable reduction based on the Variable Importance Value method achieved nearly the same prediction accuracy with no reduced prediction, whereas variable re- duction using the PCA method had an obviously degraded result that may have been caused by the loss of subtle variations and the fusion of noise information. After removing highly correlated variables, the relative variable importance remained steady, and the use of variables selected based on the best-performing vegetation indices performed better than the vari- ables with all vegetation indices or those selected based on the most important one. The results in this study demonstrate the practical and powerful ability of the RF method in predicting grassland LAI, which can also be applied to the estimation of other vegetation traits as an alternative to conventional empirical regression models and the selection of relevant variables used in ecological models.

Distribution of cranes in Hulunber Grassland and Daxing'an Mountains Forest Region in Inner Mongolia

The Daxing’an Mountains and Hulunber Grassland are located in the nor-theastern border area of China. This region covers a large area of rich biodiversity. The natural environment here is well protected because of Iow pressure of human activity. There are 5 species of cranes here. They are Red-crowned crane (Grus japonensis), White-napped crane (Grus vipio), Siberian White crane (Grus leucogeranus), Grey crane (Grus lifordi) and Demoiselle crane (Anthropoides Virgo). Red-crowned crane is a breeding species that is widely distributed in this area. The main breeding population of this species is in Heilongjiang Province. They migrate to the south of China in winter. White-napped crane, Grey crane, Demoiselle crane are also summer birds. It remains unknown if Siberian White crane breeds here. Since the population of Red-crowned crane, White-napped crane, Siberian White crane in China are at the edge of endangering, so they are listed in the namelist of national protected species. But Grey crane, Demoiselle crane have a larger population. and are widely distributed.