THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIES
Coastal monitoring plays a vital role in environmental planning and hazard management related issues. Since shorelines are fundamental data for environment management, disaster management, coastal erosion studies, modelling of sediment transport and coastal morphodynamics, various techniques have be...
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2017-11-01
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Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
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doaj-4df49f2db5e24ea9b263bc8a2cc7e7b02020-11-24T22:48:09ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502017-11-01IV-4-W414114510.5194/isprs-annals-IV-4-W4-141-2017THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIESB. Bayram0F. Erdem1B. Akpinar2A. K. Ince3S. Bozkurt4H. Catal Reis5D. Z. Seker6Yildiz Technical University, Civil Engineering Faculty, Dept. of Geomatic Engineering, Davutpasa Campus, 34220 Esenler-Istanbul, TurkeyYildiz Technical University, Graduate School of Natural And Applied Sciences, Dept. of Geomatic Engineering, Davutpasa Campus, 34220 Esenler-Istanbul, TurkeyYildiz Technical University, Civil Engineering Faculty, Dept. of Geomatic Engineering, Davutpasa Campus, 34220 Esenler-Istanbul, TurkeyYildiz Technical University, Graduate School of Natural And Applied Sciences, Dept. of Geomatic Engineering, Davutpasa Campus, 34220 Esenler-Istanbul, TurkeyYildiz Technical University, Graduate School of Natural And Applied Sciences, Dept. of Geomatic Engineering, Davutpasa Campus, 34220 Esenler-Istanbul, TurkeyGumushane University, Faculty of Engineering, Dept. of Geomatics, Gumushane, TurkeyITU, Civil Engineering Faculty, 80626 Maslak Istanbul, TurkeyCoastal monitoring plays a vital role in environmental planning and hazard management related issues. Since shorelines are fundamental data for environment management, disaster management, coastal erosion studies, modelling of sediment transport and coastal morphodynamics, various techniques have been developed to extract shorelines. Random Forest is one of these techniques which is used in this study for shoreline extraction.. This algorithm is a machine learning method based on decision trees. Decision trees analyse classes of training data creates rules for classification. In this study, Terkos region has been chosen for the proposed method within the scope of "TUBITAK Project (Project No: 115Y718) titled "Integration of Unmanned Aerial Vehicles for Sustainable Coastal Zone Monitoring Model – Three-Dimensional Automatic Coastline Extraction and Analysis: Istanbul-Terkos Example". Random Forest algorithm has been implemented to extract the shoreline of the Black Sea where near the lake from LANDSAT-8 and GOKTURK-2 satellite imageries taken in 2015. The MATLAB environment was used for classification. To obtain land and water-body classes, the Random Forest method has been applied to NIR bands of LANDSAT-8 (5<sup>th</sup> band) and GOKTURK-2 (4<sup>th</sup> band) imageries. Each image has been digitized manually and shorelines obtained for accuracy assessment. According to accuracy assessment results, Random Forest method is efficient for both medium and high resolution images for shoreline extraction studies.https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-4-W4/141/2017/isprs-annals-IV-4-W4-141-2017.pdf |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
B. Bayram F. Erdem B. Akpinar A. K. Ince S. Bozkurt H. Catal Reis D. Z. Seker |
spellingShingle |
B. Bayram F. Erdem B. Akpinar A. K. Ince S. Bozkurt H. Catal Reis D. Z. Seker THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIES ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
author_facet |
B. Bayram F. Erdem B. Akpinar A. K. Ince S. Bozkurt H. Catal Reis D. Z. Seker |
author_sort |
B. Bayram |
title |
THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIES |
title_short |
THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIES |
title_full |
THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIES |
title_fullStr |
THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIES |
title_full_unstemmed |
THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIES |
title_sort |
efficiency of random forest method for shoreline extraction from landsat-8 and gokturk-2 imageries |
publisher |
Copernicus Publications |
series |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
issn |
2194-9042 2194-9050 |
publishDate |
2017-11-01 |
description |
Coastal monitoring plays a vital role in environmental planning and hazard management related issues. Since shorelines are fundamental data for environment management, disaster management, coastal erosion studies, modelling of sediment transport and coastal morphodynamics, various techniques have been developed to extract shorelines. Random Forest is one of these techniques which is used in this study for shoreline extraction.. This algorithm is a machine learning method based on decision trees. Decision trees analyse classes of training data creates rules for classification. In this study, Terkos region has been chosen for the proposed method within the scope of "TUBITAK Project (Project No: 115Y718) titled "Integration of Unmanned Aerial Vehicles for Sustainable Coastal Zone Monitoring Model – Three-Dimensional Automatic Coastline Extraction and Analysis: Istanbul-Terkos Example". Random Forest algorithm has been implemented to extract the shoreline of the Black Sea where near the lake from LANDSAT-8 and GOKTURK-2 satellite imageries taken in 2015. The MATLAB environment was used for classification. To obtain land and water-body classes, the Random Forest method has been applied to NIR bands of LANDSAT-8 (5<sup>th</sup> band) and GOKTURK-2 (4<sup>th</sup> band) imageries. Each image has been digitized manually and shorelines obtained for accuracy assessment. According to accuracy assessment results, Random Forest method is efficient for both medium and high resolution images for shoreline extraction studies. |
url |
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/IV-4-W4/141/2017/isprs-annals-IV-4-W4-141-2017.pdf |
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