Semi-automatic shoreline extraction using water index transformation on Landsat 8 OLI imagery in Jepara Regency

Oceanographic conditions, physical development, cultivation, and sedimentation in river estuaries are dynamic trends occured in Jepara Regency. These dynamics need to be understood so it is necessary to determine the position of the shoreline as an impact of morphodynamic to see the latest variations of the shoreline in Jepara Regency. Landsat imagery can be an alternative source of data for shoreline mapping, while shoreline extraction methods can be conducted using water index, which is easy to perform. Regulation published by the Head of the Geospatial Information Agency Number 6 of 2018 can be used as a standard for shoreline maps accuracy obtained from remote sensing imagery. The research objective is to map the Jepara shoreline using NDWI, MNDWI, and AWEI transformations and compare the water index performance. Shoreline data is extracted from Landsat 8 OLI imagery, while the reference shoreline for accuracy assessment is obtained from visual interpretation of PlanetScope imagery. Threshold 0 and subjective threshold based on experiments per coastal physical typology samples are used to separate land-sea. The difference in the shoreline length on the eight shorelines are due to the limited capability of the water index in obtaining the shoreline. MNDWI shoreline with a threshold of 0 gives the lowest RMSE value (RMSE= 25,33 m) among another index, while the NDWI shoreline with a threshold of 0 gives the highest RMSE value (RMSE= 43,77 m).

[1]  W. Zhan,et al.  Landsat 8 OLI image based terrestrial water extraction from heterogeneous backgrounds using a reflectance homogenization approach , 2015 .

[2]  Rasmus Fensholt,et al.  Automated Water Extraction Index: A new technique for surface water mapping using Landsat imagery , 2014 .

[3]  N. M. Farda,et al.  Tidal Correction Effects Analysis on Shoreline Mapping in Jepara Regency , 2018, Journal of Applied Geospatial Information.

[4]  L. Ruiz,et al.  Automatic extraction of shorelines from Landsat TM and ETM+ multi-temporal images with subpixel precision , 2012 .

[5]  P. Murugan,et al.  Assessment of Surface Water Dynamicsin Bangalore Using WRI, NDWI, MNDWI, Supervised Classification and K-T Transformation☆ , 2015 .

[6]  P. Gong,et al.  Continuous monitoring of coastline dynamics in western Florida with a 30-year time series of Landsat imagery , 2016 .

[7]  M. Marfai,et al.  Coastal dynamic and shoreline mapping: multi-sources spatial data analysis in Semarang Indonesia , 2008, Environmental monitoring and assessment.

[8]  Hanqiu Xu Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery , 2006 .

[9]  F. Ling,et al.  Analysis of Coastline Extraction from Landsat-8 OLI Imagery , 2017 .

[10]  P. Wicaksono,et al.  Geometric Accuracy Assessment for Shoreline Derived from NDWI, MNDWI, and AWEI Transformation on Various Coastal Physical Typology in Jepara Regency using Landsat 8 OLI Imagery in 2018 , 2019, Geoplanning: Journal of Geomatics and Planning.

[11]  Fevzi Karsli,et al.  Automatic detection of shoreline change on coastal Ramsar wetlands of Turkey , 2011 .

[12]  P. Gong,et al.  Target Detection Method for Water Mapping Using Landsat 8 OLI/TIRS Imagery , 2015 .

[13]  S. K. McFeeters The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[14]  C. Tucker,et al.  NASA’s Global Orthorectified Landsat Data Set , 2004 .

[15]  Ali Selamat,et al.  Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery , 2014, Remote. Sens..

[16]  Jin Chen,et al.  Comparison and improvement of methods for identifying waterbodies in remotely sensed imagery , 2012 .

[17]  Gulcan Sarp,et al.  Water body extraction and change detection using time series: A case study of Lake Burdur, Turkey , 2017 .

[18]  J. Pethick,et al.  An Introduction to Coastal Geomorphology , 1984 .

[19]  Wang Zongmin,et al.  Water Body Extraction Methods Study Based on RS and GIS , 2011 .