The change detection in coastal settlements using image processing techniques: a case study of Korfez


Sahin G., Cabuk S. N., Çetin M.

Environmental Science and Pollution Research, cilt.29, sa.10, ss.15172-15187, 2022 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29 Sayı: 10
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s11356-021-16660-x
  • Dergi Adı: Environmental Science and Pollution Research
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, IBZ Online, ABI/INFORM, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, EMBASE, Environment Index, Geobase, MEDLINE, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.15172-15187
  • Anahtar Kelimeler: Coastal change detection, Geographic information systems, Remote sensing
  • Ondokuz Mayıs Üniversitesi Adresli: Hayır

Özet

Coastal areas all over the world are usually exposed to intensive change and transformation processes resulting in a variety of natural, physical, and socio-economic problems. Körfez province, located along the İzmit Bay of Marmara Sea, Turkey, has been one of these coastal areas that has become a major point for industrial facilities and highly populated urbanized area since 1960s. Therefore, the analysis of the spatial changes in the land use patterns of the province has a critical role in the success of the physical planning processes and the protection of the coastal areas. In order to highlight this critical role, temporal change detection analysis for Körfez province covering a 6-year period and 5 land use classes was made using 2009 and 2015 SPOT imagery and thematic maps. Reclassified CORINE data, development plans, and land survey results were benefited for the supervised classification of the images. Four hundred eighty control points for each year were used to achieve a strong accuracy tested by Kappa coefficient. The spatio-temporal change detection results revealed a 22.5% and 2.3% decrease in agricultural lands and sea areas respectively, while there was an increase of 16.6% in forest-green areas, 6.4% in settlement areas, and 74.1% in lake areas. The results are believed to be significant input data to facilitate coastal and physical development planning over the area, and thus make sustainable land use decisions to protect the delicate landscape and coastal characteristics, while providing a sound risk management plan.