Land-cover classification using advanced land observation satellite imagery: A case study of the peri-urban region of Antakya

Guzelmansur A., Kilic S.

JOURNAL OF FOOD AGRICULTURE & ENVIRONMENT, cilt.11, sa.2, ss.1178-1181, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 11 Sayı: 2
  • Basım Tarihi: 2013
  • Dergi Adı: JOURNAL OF FOOD AGRICULTURE & ENVIRONMENT
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, Food Science & Technology Abstracts, Veterinary Science Database
  • Sayfa Sayıları: ss.1178-1181
  • Çukurova Üniversitesi Adresli: Evet

Özet

The aim of the study was to examine the potential maximum likelihood classification in the mapping of basic land cover/land use classes by using ALOS AVNIR-2 imagery. The two specific objectives were; (a) to develop a maximum likelihood classification scheme for mapping land cover/land use classes using ALOS AVNIR-2 imagery, (b) to estimate the accuracy of the used method. Land cover nomenclature is classified according to the Coordination of Information on the Environment (CORINE) Level 2 and 3 classifications. Ten urban land cover classes were used in this study: river, wetland vegetation, forest, mining area, shadow, mountain forest, cemetery, agriculture, built up area, industrial area. The classification accuracy was assessed using 218 pixels were stratified randomly distributed throughout the study area and independent of training sites used by the supervised classification algorithm. The results show that overall classification accuracies is 81.19% and overall kappa statistics is 0.7845.