Between the tides: Modelling the elevation of Australia's exposed intertidal zone at continental scale

被引:74
作者
Bishop-Taylor, Robbi [1 ]
Sagar, Stephen [1 ]
Lymburner, Leo [1 ]
Beaman, Robin J. [2 ]
机构
[1] Geosci Australia, Cnr Jerrabomberra Ave & Hindmarsh Dr, Symonston, ACT 2609, Australia
[2] James Cook Univ, Coll Sci & Engn, Cairns, Qld 4870, Australia
关键词
Digital elevation model; Intertidal zone; Remote sensing; Continental-scale; Tidal modelling; Australia; Landsat; WATER INDEX NDWI; TIDAL FLATS; SATELLITE IMAGERY; TIME-SERIES; AREAS; CONSERVATION; COMPOSITES; BATHYMETRY; TOPOGRAPHY; EXTRACTION;
D O I
10.1016/j.ecss.2019.03.006
中图分类号
Q17 [水生生物学];
学科分类号
071004 ;
摘要
The intertidal zone represents a critical transition between marine and terrestrial ecosystems, supporting a complex mosaic of highly productive and biologically diverse habitats. However, our understanding of these important coastal environments is limited by a lack of spatially consistent topographic data, which can be extremely challenging and costly to obtain at continental-scale. Satellite remote sensing represents an important resource for monitoring extensive coastal zones. Previous approaches to modelling the elevation of the intertidal zone using earth observation (EO) data have been restricted to small study regions or have relied on manual image interpretation, thus limiting their ability to be applied consistently over large geographic extents. In this study, we present an automated open-source approach to generate satellite-derived elevation data for over 15,387 km(2) of intertidal terrain across the entire Australian coastline. Our approach combines global tidal modelling with a 30-year time series archive of spatially and spectrally calibrated Landsat satellite data managed within the Digital Earth Australia (DEA) platform. The resulting National Intertidal Digital Elevation Model (NIDEM) dataset provides an unprecedented three-dimensional representation of Australia's vast exposed intertidal zone at 25 m spatial resolution. We validate our model against LiDAR, RTK GPS and multibeam bathymetry datasets, finding that modelled elevations are highly accurate across sandy beach ( +/- 0.41 m RMSE) and tidal flat environments ( +/- 0.39 m RMSE). Model performance was least accurate ( +/- 2.98 m RMSE) within rocky shores and reefs and other complex coastal environments with extreme and variable tidal regimes. We discuss key challenges associated with modelling intertidal elevation including tidal model performance and biased observations from sun-synchronous satellites, and suggest future directions to improve the accuracy and utility of continental-scale intertidal elevation modelling. Our model can be applied to tidally-influenced coastal environments globally, addressing a key gap between the availability of sub-tidal bathymetry and terrestrial elevation data.
引用
收藏
页码:115 / 128
页数:14
相关论文
共 104 条
[31]   Seasonal Composite Landsat TM/ETM plus Images Using the Medoid (a Multi-Dimensional Median) [J].
Flood, Neil .
REMOTE SENSING, 2013, 5 (12) :6481-6500
[32]  
Galbraith H, 2002, WATERBIRDS, V25, P173, DOI 10.1675/1524-4695(2002)025[0173:GCCASL]2.0.CO
[33]  
2
[34]  
Gallant J., 2010, 1 2 SRTM DERIVED DIG
[35]   Bathymetric mapping by means of remote sensing: methods, accuracy and limitations [J].
Gao, Jay .
PROGRESS IN PHYSICAL GEOGRAPHY-EARTH AND ENVIRONMENT, 2009, 33 (01) :103-116
[36]   Evaluating shoreline identification using optical satellite images [J].
Garcia-Rubio, Gabriela ;
Huntley, David ;
Russell, Paul .
MARINE GEOLOGY, 2015, 359 :96-105
[37]   Remote sensing of coastlines: detection, extraction and monitoring [J].
Gens, R. .
INTERNATIONAL JOURNAL OF REMOTE SENSING, 2010, 31 (07) :1819-1836
[38]  
Geoscience Australia, 2015, Digital elevation model (DEM) 5 metre grid of Australia derived from LiDAR. Metadata Statement
[39]   Coastal processes and sedimentary facies in the Zohreh River Delta (Northern Persian Gulf) [J].
Gharibreza, Mohammadreza ;
Habibi, Alireza ;
Imamjomeh, Sayed Reza ;
Ashraf, Muhammad Aqeel .
CATENA, 2014, 122 :150-158
[40]   Google Earth Engine: Planetary-scale geospatial analysis for everyone [J].
Gorelick, Noel ;
Hancher, Matt ;
Dixon, Mike ;
Ilyushchenko, Simon ;
Thau, David ;
Moore, Rebecca .
REMOTE SENSING OF ENVIRONMENT, 2017, 202 :18-27