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Makale detayı · 2015 · article

Shoreline Extraction and Change Detection using 1:5000 Scale Orthophoto Maps: A Case Study of Latvia-Riga

Dergi International Journal of Environment and Geoinformatics
ISSN2148-9173
YÖKSİS OpenAlex Açık erişim · diamond TR Index
Yıl2015
Atıf15OpenAlex
Yüzdelik%79,9
FWCI1,21,00 = dünya ortalaması

Veri kaynağı ayrımı

  • YÖKSİSYÖKSİS makale kaydı
  • YÖKSİS dergi adıInternational Journal of Environment and Geoinformatics
  • OpenAlexOpenAlex zenginleştirmesi (özet, atıf, konular)

Özet

OpenAlex İngilizce

Normal 0 21 false false false TR X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Normal Tablo"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman",serif; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} Coastal management requires rapid, up-to-date, and correct information. Thus, the determination of coastal movements and its directions has primary importance for coastal managers. For monitoring the change of shorelines, remote sensing data, very high resolution aerial images and orthophoto maps are utilized for detections of change on shorelines. It is possible to monitor coastal changes by extracting the coastline from orthophoto maps. Along the Baltic Sea and Riga Gulf, Latvian coastline length is 496 km. It is rich of coastal resources and natural biodiversity. Around 120 km of coastline are affected by significant coastal changes caused by climate change, storms, erosion, human activities and other reasons and they must be monitored. In this study, an object-oriented approach has been proposed to detect shoreline and detect the changes by using 1:5000 scaled orthophoto maps of Riga-Latvia (3bands, R, G, and NIR) in the years of 2007 and 2013. As many of the authors have mentioned, object-oriented classification method can be more successful than the pixel-based methods especially for high resolution images to avoid mix-classification. In the presented study the eCognition object-oriented fuzzy image processing software has been used. The results were compared to the results derived from manual digitizing. Extracted and manually digitized shorelines have been divided in 5 m segments in x axis. The y coordinates of the new nodes were taken from the original “.dxf” file or computed by interpolation. Thus, the RMS errors of selected points were calculated

Konular

Atıflar

OpenAlex cited_by_count. WoS veya Scopus atıf sayısı değildir; o kaynaklar için ayrı kolon yoktur.

15atıfOpenAlex · cited_by_count (önbellek / veritabanı)

Yerel katalogda bu makaleye atıf yapan 6 yayın (OpenAlex referans eşleşmesi; tam dünya listesi değildir).

  1. 2017 THE EFFICIENCY OF RANDOM FOREST METHOD FOR SHORELINE EXTRACTION FROM LANDSAT-8 AND GOKTURK-2 IMAGERIESAtıf 24 · OpenAlex
  2. 2019 New Approach in Integrated Basin Modelling: Melen Airborne LIDARAtıf 5 · OpenAlex
  3. 2018 An Extended Approach of Particle Swarm Optimization for Shoreline Extraction from RASAT ImageryAtıf 2 · OpenAlex
  4. 2018 An Extended Approach of Particle Swarm Optimization for ShorelineExtraction from RASAT ImageryAtıf 2 · OpenAlex
  5. 2018 An Extended Approach of Particle Swarm Optimization for Shoreline Extraction from RASAT ImageryAtıf 2 · OpenAlex
  6. 2017 COMPARISON OF TWO SIMPLIFICATION METHODS FOR SHORELINE EXTRACTION FROM DIGITAL ORTHOPHOTO IMAGESAtıf 0 · OpenAlex

Yazarlar

7
  1. BÜLENT BAYRAM 1
  2. Inese Janpaule 2
  3. EMİN ÖZGÜR AVŞAR 3
  4. MUSTAFA OĞURLU 4
  5. Salih Bozkurt 5
  6. HATİCE ÇATAL REİS GÜMÜŞHANE ÜNİVERSİTESİ 6
  7. DURSUN ZAFER ŞEKER 7