Spatial quantification method of grassland utilization intensity on the Qinghai-Tibetan Plateau: A case study on the Selinco basin.

Existing methods for spatial quantification of grassland utilization intensity cannot meet the demand for accurate detection of the spatial distribution of grassland utilization intensity in the Qinghai-Tibetan Plateau with high spatial resolution. In this paper, a method based on remote-sensing observations and simulations of grassland growth dynamics is proposed. The grassland enhanced vegetation index (EVI) time-series curve during the growing season characterizes the growth of grassland in the corresponding pixel; The deviation between the observed and potential EVI curves indicates the disturbance on grassland growth imposed by human activities, and it can characterize the grassland utilization intensity during the growing season. Based on the main idea described above, absolute and relative disturbances are calculated and used as quantitative indicators of grassland utilization intensity defined from different perspectives. Livestock amount at the pixel scale is obtained by pixel-by-pixel calculations based on the function relationship at the township scale between absolute disturbance and livestock density, which is specific quantitative indicator that considers the mode of grassland utilization. In simulating the potential EVI of grassland, the lag and accumulation effects of meteorological factors are investigated at the daily scale using a multi-objective genetic algorithm. Further, the nonlinear functions between multiple environmental factors (e.g., grassland type, topography, soil, meteorology) and the grassland EVI are established using an error back-propagation feedforward artificial neural network (ANN-BP) with parameter optimization. Finally, the potential EVIs of all grassland pixels are simulated on the basis of this model. The method is applied to the Selinco basin on the Qinghai-Tibetan Plateau and validated by examining the spatial consistency of the results with township-scale livestock density and grazing pressure. The final results indicate that the proposed method can accurately detect the spatial distribution of grassland utilization intensity which is appliable in the similar regions.

Random and systematic change analysis in land use change at the category level-A case study on Mu Us area of China.

Random and systematic change analysis is gradually becoming a common method for effectively detecting land use change signals from land transition matrix, but most researches focus only on the change characteristics at the transition level. This paper attempted to distinguish random and systematic changes at the category level, and to clarify the meanings of these two types of changes, as well as their indicative significances of change causes. This paper first calculated the random expected value of change area at the category level, and the deviation of the actual change area from the expected value. Then we proposed a method for setting a threshold of the deviation to clearly distinguish random and systematic changes. This method could eliminate the influence of land use classification errors on the distinction. Through analyzing the mathematical formulas of random expected area, this paper further clarified the meanings of random and systematic changes as well as their indicative significances to change causes. Land use change in Mu Us area of China was used as a case study. Practice showed that detecting and analyzing the random and systematic signals at the category level could accurately determine the change trend of land use, which could help to explore the relationship between land use change and external influences, especially human activities.