Study on estimation of fractional vegetation coverage based on Google Earth images

Abstract

The Windows screen-capture tools was used to get the Google Earth (GE) images. Compared with the original remote sensing images, although the image quality was reduced and the spectral information was lacking, it has been able to meet the needs of this study. A method for estimating fractional vegetation coverage (FVC) using GE images based on K-Means algorithm was proposed. Firstly, GE image was preliminarily classified by using K-Means algorithm. Secondly, by visual interpretation, the initial classification results were further clustered into 4 types according to the number and brightness feature of land surface types in the image, low brightness (shadow), medium low brightness (high density vegetation), medium high brightness (sparse vegetation), high brightness (bare ground), the FVC of each category was determined by its characteristics and composition. Finally, weighted by the proportion of pixels in the image, took the weighted sum of the FVC of all categories as the FVC of the image. In addition, the field survey data were used to verify the FVC estimated by the proposed method, the results showed that: the precision of estimated vegetation coverage could reach 80% ~ 90%.