Study On Models For Monitoring Of Grassland Biomass In The Region Around Qinghai Lake Assisted By Remote Sensing

The grassland is one of the most important vegetation types in China and the most important renewable resource and raw material site of stock raising. It is a natural green protective screen of the earth which plays an important role to human existence and development. The region around Qinghai Lake is the centre of the grassland and the main husbandry base of Qinghai Province. There are masses of the grassland, various herb style and enriched nutritive value. So it is not only directly concern this district's development of animal husbandry but also indirectly influence the sustainable development of economy and society in the area where the ethnic minority is the main populace. In addition the monitoring of the grassland biomass by remote sensing receives the attention of various countries because it has fast, macroscopic, economic and objective merit, can offer important output information and make objective scientific basis for the relevant policies of the country.The main research contents of this dissertation are the following:1. The dissertation mainly probe into the relation model between the vegetation index of remote sensing and the grassland biomass, systematically have collected the kinds of vegetation indexes, compare and analyze the range of application of the different vegetation indexes and offer the basis for the model of the grassland biomass in the basis of the study in the past.2. The correlation analyses were conducted for the rations between the vegetation indices and the sampled grass yield data which were taken as the grassland biomass quotas. The results indicated that there are quite high correlations between the vegetation indices and the grassland yield data. Among them the RVI has the highest correlation coefficient, NDVI in the next, and then successively TVI, MSAVI, infrared index, SAVI, GVI, DVI and W VI, but BVI has the lowest correlation coefficient that there is a negative correlation coefficient between BVI and the biomass. In the general it has better relevant relations between the vegetation indices of remote sensing and the grassland biomass. Therefore it is basically feasible that we establish theVImonitoring model of the grassland biomass by the vegetation indices.3. The linear regression models and the non-linear regression models are established respectively, which can be used in monitoring the grassland biomass based on the vegetation indices. Finally, in order to establish the grassland biomass monitoring models with higher accuracy the different forms of the non-linear regression model were established respectively, including the quadric equation, the cubic equation, the logarithmic equation, and the exponential equation. The repeated tests indicated that the cubic equation is the best one in terms of the suitability of use in the study area. That is to say the cubic equationY=-18.626RVI3+220.317RVI2-648.271RVI+691.093 is the best model which can be used in monitoring grassland biomass based on the vegetation indices in the region around Qinghai Lake.