Application Analysis Of Vegetation Index In The Greater Higgnan Mountains Region

Forest ecosystem is the most important and widely distributed type of ecosystems. But it is more fragile ecosystems, easily affected by natural factors and man-made factors. And forest biomass is the main criteria to reflect and assess of forest ecosystem. At present, the use of remote sensing technology for monitoring natural forest biomass has become a scientific research at the forefront of international forest topics, but results are very different. It's not really predictable which kind of vegetation index and model of relationship between vegetation index and forest biomass be more suitable for monitoring regional forest biomass at this point. It is in urgent need of solving the problem of contemporary forest and remote sensing science.Using the MODIS images of Greater Higgnan Mountains region in 2007, and extracting vegetation indexes(NDVI, RVI, DVI, SAVI, MASVI, and PVI)from the image of the research region, the monadic linear regression models and the non-linear regression models were established, respectively, to express the relations between forest biomass and the vegetation indexes. Results showed that the correlations between sampled biomass and the seven vegetation indexes were highly positive significant, with NDVI being highest, RVI and MSAVI, again MASVI, DVI being respectively, PVI lowest. Generally speaking, compared to the monadic linear regression(R2=0.709), there was an increase in fitting accuracy of curve models, such as cubic polynomial equation(R2=0.812), Second-degree polynomial(R2=0.79). So the curve models were better to reflect the relationship between vegetation index and measured biomass.For VI-biomass regression model, the cubic polynomial model was better than the monadic linear regression models and the other non-linear regression models. And multiple correlation coefficients(R2=0.812)of the cubic polynomial model based on NDVI Was higher than the others, and the error of the model verified by the observation value Was very small, with an average error of 16.8%, firing accuracy of 83.2%, not only indicating that it Was better suited to monitor the forest growing of Northeast, and that it met regional forest monitoring needs, but also the NDVI cubic polynomial model to monitor regional forest biomass was a simple and effective and practical method.