Study On The Remote Sensing Biomass Model And Carbon Storage Of Shrub Forest In Qinghai Loess Hilly Region

The study of biomass and carbon storage is very important for the global climate change and the global carbon cycle. This study is based on the remote sensing technology, environmental science and statistics. The study area is located in the Loess Hilly Region of Qinghai province where is also the arid and semi-arid area. The object of research is dominant species of shrubs in this region, and study with the remote sensing satellite images of the study area, we get the distribution information of the regional vegetation coverage. By using the vegetation coverage data of the study area, the extracted vegetation index NDVI was tested. The relative error of the results were less than 15% which indicate that the NDVI data extracted from the study area is valid.The extraction of NDVI is based on the MODIS data of spatial resolution of 250m and TM data of spatial resolution of 30m. The data could meet the accuracy requirements of the regional biomass model. For the same area of different resolution data sources, higher resolution TM NDVI data and lower resolution MODIS NDVT data were the same for the correlation of shrub biomass and NDVI. For the TM data, the correlation coefficient of biomass of four dominant trees reached to 0.724,0.790,0.775 and 0.727, for MODIS data, the correlation coefficient were 0.659,0.695,0.670 and 0.681. The spatial resolution of the model had little influence on the precision. If time is enough, the TM data was better. The MODIS data also could be used when the accuracy of the model is not considered.For the TM data, the optimal biomass remote sensing model of Seabuckthorn was composite curve model:W=3.899><2.626^NDVI, the optimal biomass remote sensing model of Salix oritrepha was composite equation: W=2.971×2.812^NDVI, the optimal biomass remote sensing model of Potentilla fruticosa was power curve model:W=4.383×NDVI^0.673, the optimal biomass model of Rhododendron was exponential curve model:W=6.279XNDVI^0.510; for MODIS data, the optimal biomass remote sensing model of Seabuckthorn was composite curve model:W=3.016×2.440^NDVI, the optimal biomass remote sensing model of Salix oritrepha was composite equation:W=3.437×2.183^NDVI, the optimal biomass remote sensing model of Potentilla fruticosa was power curve model:W=4.026×NDVI^0.543, the optimal biomass model of Rhododendron was exponential curve model:W=5.777×NDVI^0.376Based on different coverage we can know that the estimation of regional shrubs biomass was 2.28317million tons. In the accurate estimation of biomass based on the combination of carbon density plots obtained in the area of shrub forest carbon reserves of 1.09314 million tons. Finally, the calculated amount of O2 release for 2.91868 million tons. According to the law of the people’s Republic of China forestry industry standard （LY/T1721-2008） calculated by shrub forest in the study area of solid carbon value for 1.09 billion yuan, releasing oxygen value was 2.92 billion yuan, fixing carbon and releasing oxygen value was 4.01 billion yuan. The ecological function of shrub forest of the region was of great value.