CoastSat: A Google Earth Engine-enabled Python']Python toolkit to extract shorelines from publicly available satellite imagery

被引:303
作者
Vos, Kilian [1 ]
Splinter, Kristen D. [1 ]
Harley, Mitchell D. [1 ]
Simmons, Joshua A. [1 ]
Turner, Ian L. [1 ]
机构
[1] UNSW Sydney, Sch Civil & Environm Engn, Water Res Lab, 110 King St, Manly Vale, NSW 2093, Australia
基金
澳大利亚研究理事会;
关键词
Google Earth Engine; Shoreline mapping; Landsat; Sentinel-2; Sub-pixel resolution; IMPACTS; CLIMATE; IDENTIFICATION; VARIABILITY; CALIBRATION;
D O I
10.1016/j.envsoft.2019.104528
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
CoastSat is an open-source software toolkit written in Python that enables the user to obtain time-series of shoreline position at any sandy coastline worldwide from 30+ years (and growing) of publicly available satellite imagery. The toolkit exploits the capabilities of Google Earth Engine to efficiently retrieve Landsat and Sentinel-2 images cropped to any user-defined region of interest. The resulting images are pre-processed to remove cloudy pixels and enhance spatial resolution, before applying a robust and generic shoreline detection algorithm. This novel shoreline detection technique combines a supervised image classification and a sub-pixel resolution border segmentation to map the position of the shoreline with an accuracy of similar to 10 m. The purpose of CoastSat is to provide coastal managers, engineers and scientists a user-friendly and practical toolkit to monitor and explore their coastlines. The software is freely-available on GitHub (https://github.com/kvos/CoastSat) and is accompanied by guided examples (Jupyter Notebook) plus step-by-step README documentation.
引用
收藏
页数:7
相关论文
共 46 条
[1]   Extreme oceanographic forcing and coastal response due to the 2015-2016 El Nino [J].
Barnard, Patrick L. ;
Hoover, Daniel ;
Hubbard, David M. ;
Snyder, Alex ;
Ludka, Bonnie C. ;
Allan, Jonathan ;
Kaminsky, George M. ;
Ruggiero, Peter ;
Gallien, Timu W. ;
Gabel, Laura ;
McCandless, Diana ;
Weiner, Heather M. ;
Cohn, Nicholas ;
Anderson, Dylan L. ;
Serafin, Katherine A. .
NATURE COMMUNICATIONS, 2017, 8
[2]  
Barnard PL, 2015, NAT GEOSCI, V8, P801, DOI [10.1038/NGEO2539, 10.1038/ngeo2539]
[3]   Between the tides: Modelling the elevation of Australia's exposed intertidal zone at continental scale [J].
Bishop-Taylor, Robbi ;
Sagar, Stephen ;
Lymburner, Leo ;
Beaman, Robin J. .
ESTUARINE COASTAL AND SHELF SCIENCE, 2019, 223 :115-128
[4]  
Carrere L., 2016, Proceedings of the ESA living planet symposium, P9
[5]   Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors [J].
Chander, Gyanesh ;
Markham, Brian L. ;
Helder, Dennis L. .
REMOTE SENSING OF ENVIRONMENT, 2009, 113 (05) :893-903
[6]   Superresolution border segmentation and measurement in remote sensing images [J].
Cipolletti, Marina P. ;
Delrieux, Claudio A. ;
Perillo, Gerardo M. E. ;
Cintia Piccolo, M. .
COMPUTERS & GEOSCIENCES, 2012, 40 :87-96
[7]   ARTIFICIAL NEURAL NETWORKS FOR LAND-COVER CLASSIFICATION AND MAPPING [J].
CIVCO, DL .
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SYSTEMS, 1993, 7 (02) :173-186
[8]   Earth's surface water change over the past 30 years [J].
Donchyts, Gennadii ;
Baart, Fedor ;
Winsemius, Hessel ;
Gorelick, Noel ;
Kwadijk, Jaap ;
van de Giesen, Nick .
NATURE CLIMATE CHANGE, 2016, 6 (09) :810-813
[9]   Estuarine suspended particulate matter concentrations from sun-synchronous satellite remote sensing: Tidal and meteorological effects and biases [J].
Eleveld, Marieke A. ;
van der Wal, Daphne ;
van Kessel, Thijs .
REMOTE SENSING OF ENVIRONMENT, 2014, 143 :204-215
[10]   Evaluating shoreline identification using optical satellite images [J].
Garcia-Rubio, Gabriela ;
Huntley, David ;
Russell, Paul .
MARINE GEOLOGY, 2015, 359 :96-105