Abstract

Google Earth (GE) provides very high resolution (VHR) natural-colored (red-green-blue, RGB) images based on commercial spaceborne sensors over worldwide coastal areas. GE is rarely used as a direct data source to address coastal issues despite the tremendous potential of data transferability. This paper describes an inexpensive and easy-to-implement methodology to construct a GE natural-colored dataset with a submeter pixel size over 44 km² to accurately map the water depth, seabed and land cover along a seamless coastal area in subtropical Japan (Shiraho, Ishigaki Island). The valuation of the GE images for the three mapping types was quantified by comparison with directly-purchased images. We found that both RGB GE-derived mosaic and pansharpened QuickBird (QB) imagery yielded satisfactory results for mapping water depth (R²_GE = 0.71 and R²_QB = 0.69), seabed cover (OA_GE = 89.70% and OA_QB = 80.40%, n = 15 classes) and land cover (OA_GE = 95.32% and OA_QB = 88.71%, n = 11 classes); however, the GE dataset significantly outperformed the QB dataset for all three mappings (Z_{Water depth} = 6.29, Z_Seabed = 4.10, Z_Land = 3.28, α_two-tailed < 0.002). The integration of freely available elevation data into both RGB datasets significantly improved the land cover classification accuracy (OA_GE = 99.17% and OA_QB = 97.80%). Implications and limitations of our findings provide insights for the use of GE VHR data by stakeholders tasked with integrated coastal zone management. View Full-Text