Mapping seagrass condition using google earth imagery

Abstract

In order to manage seagrass ecosystems, detailed information on seagrass condition is needed. Remote sensing could be used to obtain information about seagrass condition. The high resolution images in Google Earth provide methodological development opportunities for seagrass condition mapping. This study aimed to assess the reliability of Google Earth imagery as a direct source of data for mapping seagrass condition. This study combined image processing and field survey. The image downloaded from Google Earth was a picture file in JPEG format which came from a GeoEye1 image. Image classification was done using the maximum likelihood method to obtain a map indicating seafloor typology. The resulting classes were seagrass beds, live coral, sand and dead coral, and deep sea. The seagrass class was further subdivided to represent conditions based on seagrass percentage cover. Classification accuracy was assessed using an error matrix to calculate overall accuracy and the Kappa Coefficient. Such a mapping method need not be expensive because Google Earth imagery can be downloaded for free.The results of this study showed that Google Earth imagery can be a reliable direct source of seagrass condition mapping data with good accuracy. The resulting map can provide detailed information when it comes from a high-resolution image.