Wetland Information Classification Method Based On Different Remote Sensing Images

Wetland is one of the most important ecosystems on the Earth's surface.In recent years,due to the wetland has been affected by many factors,the wetland area is slowly decreasing,and some wetlands have disappeared.The phenomenon has aroused more and more attention from all walks of life in the world to wetlands.Scholars from all walks of life started to carry out different investigations and researches on wetlands for the protection and utilization of wetlands.At this stage,remote sensing technology has gradually become one of the important tools in the study of wetland ecosystems.With the rapid development of remote sensing technology,remote sensing technology has the features of short time and rapid results,and can accurately reflect the land within a large area of the surface.It can accurately reflect the functional changes of land surface in a wide range,and can quickly and accurately grasp the distribution and change information of wetlands in a short time.Shengjin Lake Wetland Reserve is an important nature reserve in Anhui Province,and it is also one of the nature reserves of important international significance for conservation in China.There are many kinds of biological resources in the Reserve,and the aquatic biological resources are extremely abundant.Therefore,there is a high level of research value.In this paper,the Shengjin Lake wetland is taken as the research area,and the land use types in the study area are classified by using different methods.The paper selected GF-1 and Landsat-8 remote sensing images in April 2015,and used remote sensing technology to refer to the land use status map of Shengjin Lake Wetland,administrative maps,topographic maps,and DEM elevation data,etc.to Shengjin Lake Wetland.The method of extracting vegetation information and land use type information was studied,and through the comparative analysis of the accuracy of different results,the information extraction method and image with the highest accuracy are obtained.The results of the study are as follows:1)Study on the method of extracting vegetation information in the study area.By transforming the Landsat-8 image into the tasseled cap transformation,and enhancing the remote sensing image information,the NDVI is estimated by combining the binary image model,and the vegetation information of the remote sensing image in the study area is extracted.At the same time,the vegetation information was directly extracted from the GF-1 image,and the vegetation distribution result map was obtained.Theaccuracy of the vegetation information extracted from the GF-1 image and Landsat-8image was verified.The overall accuracy of the GF-1 image vegetation information extraction was 80.25%,and the Kappa coefficient was 0.7713.The overall accuracy of the Landsat-8 image vegetation information extraction was 84.5%,Kappa coefficient is0.8125.Through the accuracy analysis,it can be concluded that the method of extracting the vegetation information in the study area through the tasseled cap transformation and NDVI values is more accurate,the information extraction effect is better,and the vegetation information extraction is more accurate.2)Using Remote Sensing Images to Study Land Use Classification in the Study Area.For land use classification in Shengjin Lake wetland,land use classification information was extracted by using GF-1 and Landsat-8 images for supervised classification and unsupervised classification.The supervised classification method selects the maximum likelihood method and support vector machine to simultaneously classify GF-1 and Landsat-8 images for land use classification.The unsupervised classification method chooses the K-means method and the ISODATA method to simultaneously perform GF-1 and Landsat-8 respectively.Images are classified by land use and different classification results are obtained.The accuracy of the classification results is verified by the confusion matrix and Kappa coefficient.The overall classification accuracy of the GF-1 using the maximum likelihood method is 88.5%,the Kappa coefficient is 0.8625,the overall classification accuracy using the support vector machine is 89.625%,and the Kappa coefficient is0.8753,the overall classification accuracy using the K-means is 86%,the Kappa coefficient is 0.8365,the overall classification accuracy using the ISODATA method is87.125%,and the Kappa coefficient is 0.8501.Landat-8 imagery uses maximum likelihood method and support vector machine respectively.The overall classification accuracy of K-means and ISODATA method is 87.25%,89%,84.75% and 85.875%,and Kappa coefficient is 0.8513,0.862,0.8212 and 0.8289,respectively.Through the comparison and analysis of the accuracy of the classification results,it can be clearly seen that when using the support vector machine method for GF-1 images,the land use classification accuracy of Shengjin Lake Wetland is higher and the classification effect is better.