Mapping VHR Water Depth, Seabed and Land Cover Using Google Earth Data

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 p...

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Vydané v:	ISPRS international journal of geo-information Ročník 3; číslo 4; s. 1157 - 1179
Hlavní autori:	Collin, Antoine, Nadaoka, Kazuo, Nakamura, Takashi
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Basel MDPI AG 01.12.2014 MDPI
Predmet:	Acoustics bathymetry Biodiversity and Ecology Bioinformatics Coastal Coastal areas coastal mapping Coastal zone Coastal zone management coasts Computer Science data collection Earth Ecology, environment Ecosystems Electromagnetism Engineering Sciences Environment and Society Environmental Engineering Environmental Sciences Geography Global Changes Google Earth Humanities and Social Sciences Image Processing Internet Japan Land cover Life Sciences Machine Learning Mapping Methodology Modeling and Simulation Neural and Evolutionary Computing Ocean floor Optics Photonic QuickBird Sea beds Signal and Image processing stakeholders Statistics very high resolution visible Water depth Japan bathymetry very high resolution visible coastal mapping Google Earth QuickBird
ISSN:	2220-9964, 2220-9964
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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 (R2GE = 0.71 and R2QB = 0.69), seabed cover (OAGE = 89.70% and OAQB = 80.40%, n = 15 classes) and land cover (OAGE = 95.32% and OAQB = 88.71%, n = 11 classes); however, the GE dataset significantly outperformed the QB dataset for all three mappings (ZWater depth = 6.29, ZSeabed = 4.10, ZLand = 3.28, αtwo-tailed < 0.002). The integration of freely available elevation data into both RGB datasets significantly improved the land cover classification accuracy (OAGE = 99.17% and OAQB = 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.
AbstractList	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 (R2GE = 0.71 and R2QB = 0.69), seabed cover (OAGE = 89.70% and OAQB = 80.40%, n = 15 classes) and land cover (OAGE = 95.32% and OAQB = 88.71%, n = 11 classes); however, the GE dataset significantly outperformed the QB dataset for all three mappings (ZWater depth = 6.29, ZSeabed = 4.10, ZLand = 3.28, αtwo-tailed < 0.002). The integration of freely available elevation data into both RGB datasets significantly improved the land cover classification accuracy (OAGE = 99.17% and OAQB = 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. 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 (R2GE = 0.71 and R2QB = 0.69), seabed cover (OAGE = 89.70% and OAQB = 80.40%, n = 15 classes) and land cover (OAGE = 95.32% and OAQB = 88.71%, n = 11 classes); however, the GE dataset significantly outperformed the QB dataset for all three mappings (ZWₐₜₑᵣ dₑₚₜₕ = 6.29, ZSₑₐbₑd = 4.10, ZLₐₙd = 3.28, αₜwₒ₋ₜₐᵢₗₑd < 0.002). The integration of freely available elevation data into both RGB datasets significantly improved the land cover classification accuracy (OAGE = 99.17% and OAQB = 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. 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 (R2GE = 0.71 and R2QB = 0.69), seabed cover (OAGE = 89.70% and OAQB = 80.40%, n = 15 classes) and land cover (OAGE = 95.32% and OAQB = 88.71%, n = 11 classes); however, the GE dataset significantly outperformed the QB dataset for all three mappings (ZWater depth = 6.29, ZSeabed = 4.10, ZLand = 3.28, alpha two-tailed < 0.002). The integration of freely available elevation data into both RGB datasets significantly improved the land cover classification accuracy (OAGE = 99.17% and OAQB = 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.
Author	Nadaoka, Kazuo Collin, Antoine Nakamura, Takashi
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Title	Mapping VHR Water Depth, Seabed and Land Cover Using Google Earth Data
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