The Research Of Temporal Dynamic Forecasting And Estimation Model Building Of Invasive Species Spartina Alterniflora In Zhejiang Coastal

Wetland is not only a unique ecosystem that located between the terrestrial and aquatic ecosystems and under the influence of the two ecosystems, but also an important part of the earth’s ecological environment and has great potential for resource, environmental, social and economic functions. Tidal wetlands are the most organic part among the earth’s ecosystem and the most active transition zone of interactions various processes between land, ocean and atmosphere, with high energy and natural biological productivity. Healthy wetland ecosystem is an important part for regional ecological security and an important foundation of sustainable development for society economic. Spartina alterniflora, a graminaceous perennial herbaceous plant with characteristics of salt tolerance, submergence tolerance, fast growth, short breeding cycle, and ecological breadth, can facilitate sediment deposit,protect levees, and reconstruct shoals. It is native to the mid-latitude coast of North America—the coastal intertidal zone from Quebec, Canada, to Mexico. It was introduced to China in 1979 by Professor Zhong Chong Xin from Nanjing University and achieved good effects when used for beach protection and siltation promotion.However, as the area covered by Spartina alterniflora has expanded, it has had negative impacts on local natural ecosystems and led to serious economic and social harm. As early as 1983, people in Zhejiang Province planted 16m2 of Spartina alterniflora on a trial basis in the Wumen mudflat of Tongli, Yuhuan County. After30 years of invasion, the plant is distributed from the coast of Hangzhou Bay in the north to southern Aojiang, Cangnan County in the south, occupying an area of 5092 ha, which has had some negative impacts on local natural ecosystems.With the rapid development and wide application of 3S technology and it become an important technical means to study vegetation, especially the surveys and studies for coastal vegetation. This study is focused on invasive species Spartina alterniflora in the tidal wetland based on 3S technology and gained some results as the follows:(1)To acquire the spatial population distribution of the invasive plant Spartina alterniflora in Xiangshan Bay in Ningbo City, Zhejiang Province, this study has explored three temporal remote-sensing images in 2002, 2006, and 2010 by supervised classification and visual interpretation. To explore the topic further,dynamic change processes were also analyzed using the IDRISI software and their spatial distributions in 2010 and 2014 predicted using a CA-Markov model. It was found that, compared with remote-sensing image interpretation maps in 2010 and2013, accuracy verification yielded overall Kappa coefficients of 81.99% and 85.57%respectively, indicating a good predictive result from the CA-Markov model.Therefore, the model can be used for long-term forecasting such as determining the dynamic change processes and evolution trends of Spartina alterniflora in the Xiangshan area over the next 20 years.(2)Combined with the latest Satellite remote sensing data Landsat-OLI and the biomass data by field survey, analyzed the correlation between Spartina alterniflora’s grass biomass and spectral information. Using a linear regression, one non-linear regression and multiple linear regression and other methods to build the biomass estimation models with remote sensing for the invasive species Spartina alterniflora in Zhejiang coastal beach and choose the best model by comparison. The results showed that: 1) Spartina alterniflora biomass measured value was significantly associated with the near infrared(NIR) band, shortwave infrared(SWIR1)band,normalized difference vegetation index(NDVI), difference vegetation index(DVI),vertical vegetation index(PVI), soil adjust vegetation index(SAVI) and vegetation greenness index(GVI) of the Landsat-OLI data, then has a highly significant correlation with renormalization vegetation index(RDVI). 2) We found the nonlinear regression model has a high precision during comparing the linear regression model,non-linear regression model(logarithmic, quadratic, cubic polynomials, complex function, power function, S-shaped curve, growth, exponential) and multiple linear regression model, where the highest precision of biomass estimation models with remote sensing for the invasive species Spartina alterniflora is the S-curve function model of Spartina alterniflora grass biomass measured value and correspondingvegetation greenness index(GVI). 3) Testing the simulation accuracy of the highest precision regression model with the Spartina alterniflora grass biomass measured value that was not involved in the modeling, to obtain the maximum relative error of25.6% and an average relative error of 17.7%, to prove the regression model can simulate the Spartina alterniflora grass biomass better.(3)Using the latest satellite images Landsat-OLI data, interpreting the populations information of invasive species Spartina alterniflora in Zhejiang coastal beach, and calculating the currently aboveground biomass of Spartina alterniflora population in Zhejiang Province based on the remote sensing estimation model.