Mapping the dominant forest tree distribution using a combined image classification approach in a complex Eastern Mediterranean basin

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Şatır O., Berberoğlu S., Akça E., Yeler O.

JOURNAL OF SPATIAL SCIENCE, cilt.62, sa.1, ss.157-171, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 62 Sayı: 1
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1080/14498596.2016.1212414
  • Dergi Adı: JOURNAL OF SPATIAL SCIENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.157-171
  • Anahtar Kelimeler: Forest-tree classification, expert knowledge classification, Landsat data set, vegetation index, Mediterranean Region of Turkey, VEGETATION, COVER
  • Van Yüzüncü Yıl Üniversitesi Adresli: Evet

Özet

A land use/cover (LUC) classification strategy based on an unsupervised k-means, object-based and expert knowledge classification technique was cross-checked using Landsat satellite datasets along with ancillary data for mapping dominant forest tree species of the Goksu River Basin in the Eastern Mediterranean Region. Eight dominant forest tree species were classified as juniper (Juniperus excelsa Bieb), Taurus fir (Abies cilicica Ant. & Kotschy), Turkish pine (Pinus brutia Tenore), black pine (Pinus nigra Arnold), cedar (Cedrus libani Rich), oak (Quercus pubescens Schwarz and Quercus cerris Pall), stone pine (Pinus pinea L.) and plane (Platanus orientalis L.). The results of the combined classification approach (CCA) were compared with a traditional maximum likelihood classifier (MLC) for a better understanding of the benefits of the CCA. Kappa values of the CCA and the MLC were derived as 0.77 and 0.44 respectively.