A feasibility study of seabed cover classification standard in generating related geospatial data

ABSTRACT This article assesses the feasibility of generating the geospatial data from a national classification standard. In this case, the National Standardization Agency (Badan Standardisasi Nasional) of Indonesia created and published a national seabed cover classification standard called SNI 7987–2014 but has not developed corresponding geospatial data. Geospatial data on seabed cover can be generated by integrating related thematic data, such as those on seafloor surficial sediments, coastal ecosystems, and coastal infrastructure. With consideration for these issues, this research evaluated the feasibility of using SNI 7987–2014 as a means of generating seabed cover geospatial data at scales of 1:250,000 and 1:50,000. To this end, the documentation accompanying the standard was evaluated via descriptive quantitative analysis through weighted scoring, and logical testing, after which overlay, feature selection based on the scored method and remote sensing analysis were carried out to develop the geospatial data prototypes. Results showed that the feasibility levels of using the prototypes for generating data at scales of 1:250,000 and 1:50,000 were 87.5% and 86.5%, respectively, indicating that SNI 7987–2014 can be fully used as the basis for generating geospatial data on seabed cover.

[1]  S. Phinn,et al.  An integrated field and remote sensing approach for mapping Seagrass Cover, Moreton Bay, Australia , 2009 .

[2]  Geoffroy Lamarche,et al.  Benthic Habitat Mapping , 2016 .

[3]  Vasilis Trygonis,et al.  photoQuad: A dedicated seabed image processing software, and a comparative error analysis of four photoquadrat methods , 2012 .

[4]  Tri Dev Acharya,et al.  Evaluation of Seabed Classification using Hyperspectral Data around the Coastal Area of Geoje Island, Korea , 2017, Journal of Coastal Research.

[5]  Dewayany Sutrisno,et al.  The development of spatial decision support system tool for marine spatial planning , 2018, Int. J. Digit. Earth.

[6]  Dongzhi Zhao,et al.  Review of coral reef ecosystem remote sensing , 2014 .

[7]  Wei Liu,et al.  A remote sensing data management system for sea area usage management in China , 2018 .

[8]  Natalie Sambhi Jokowi’s ‘global maritime axis’: smooth sailing or rocky seas ahead? , 2015 .

[9]  Mike Botts,et al.  Promoting the capture of sensor data provenance: a role-based approach to enable data quality assessment, sensor management and interoperability , 2018, Open Geospatial Data, Software and Standards.

[10]  J. Burt,et al.  Twenty-year changes in coral near Muscat, Oman estimated from manta board tow observations. , 2015, Marine environmental research.

[11]  Dongmei Chen,et al.  Mapping mangrove forests using multi-tidal remotely-sensed data and a decision-tree-based procedure , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[12]  Samsung Lim,et al.  Vulnerability assessment of ecological conditions in Seribu Islands, Indonesia , 2012 .

[13]  S. Pioch,et al.  A comparative study of the accuracy and effectiveness of Line and Point Intercept Transect methods for coral reef monitoring in the southwestern Indian Ocean islands , 2016 .

[14]  Luis Bermudez New frontiers on open standards for geo-spatial science , 2017, Geo spatial Inf. Sci..

[15]  Safril Hidayat dan Ridwan Maritime Axis And Indonesia’s National Security: Challenge And Hope , 2017 .

[16]  Brian J. Todd,et al.  Surficial geology and benthic habitat of the German Bank seabed, Scotian Shelf, Canada , 2011 .

[17]  J. Shaw,et al.  Geologic insights from multibeam bathymetry and seascape maps of the Bay of Fundy, Canada , 2014 .

[18]  Peter Reinartz,et al.  Mapping Mediterranean seagrasses with Sentinel-2 imagery. , 2017, Marine pollution bulletin.

[19]  Dayton Dove,et al.  Spatial prediction of seabed sediment texture classes by cokriging from a legacy database of point observations , 2012 .

[20]  I. Wicaksana Indonesia’s maritime connectivity development: domestic and international challenges , 2017 .

[21]  John van Genderen Perspectives on the nature of geospatial information , 2017, Geo spatial Inf. Sci..

[22]  Xiaohong Zhang,et al.  A novel Stop&Go GPS precise point positioning (PPP) method and its application in geophysical exploration and prospecting , 2012 .

[23]  D. Müller,et al.  Mapping mud content and median grain-size of North Sea sediments – A geostatistical approach , 2018 .

[24]  Paulo Fonseca,et al.  Benthic habitat mapping in a Portuguese Marine Protected Area using EUNIS: An integrated approach , 2015 .

[25]  Gary A. Kendrick,et al.  Description of a remote still photography system for collection of benthic photo-quadrats , 2010 .

[26]  Markus Diesing,et al.  Mapping seabed sediments: Comparison of manual, geostatistical, object-based image analysis and machine learning approaches , 2014 .

[27]  Giyanto,et al.  Variation in the composition of corals, fishes, sponges, echinoderms, ascidians, molluscs, foraminifera and macroalgae across a pronounced in-to-offshore environmental gradient in the Jakarta Bay-Thousand Islands coral reef complex. , 2016, Marine pollution bulletin.

[28]  R. Maniere,et al.  Remote sensing techniques adapted to high resolution mapping of tropical coastal marine ecosystems (coral reefs, seagrass beds and mangrove) , 1998 .

[29]  K. Pikelj,et al.  The performance, application and integration of various seabed classification systems suitable for mapping Posidonia oceanica (L.) Delile meadows. , 2014, The Science of the total environment.

[30]  C. Delacourt,et al.  Hyperspectral remote sensing of coral reefs by semi-analytical model inversion - Comparison of different inversion setups , 2017 .