MONITORING COMPONENTS OF URBAN ENVIRONMENT USING VEGETATION-IMPERVIOUS-SOIL MODEL AND REMOTELY SENSED DATA


Tok E., Kaya S.

JOURNAL OF ENVIRONMENTAL PROTECTION AND ECOLOGY, cilt.15, sa.4, ss.1857-1865, 2014 (SCI İndekslerine Giren Dergi) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 15 Konu: 4
  • Basım Tarihi: 2014
  • Dergi Adı: JOURNAL OF ENVIRONMENTAL PROTECTION AND ECOLOGY
  • Sayfa Sayıları: ss.1857-1865

Özet

The Istanbul Metropolitan area, facing rapid frequently uncontrolled urbanisation, is the most bipolar urbanised city in Turkey. One of the main reasons of urbanisation is the lack of adequate legal enforcement on planning regulations that leads to illegal settlements and uncontrolled urban sprawl. This expansion is affected by physical, socio-economic, and demographic phenomena that influence the morphological patterns of the districts like Sultanbeyli, Kartal and Pendik that are identified as the most significant districts regarding rapid and uncontrolled growth between 1987-1997. This study mainly aimed to monitor spatial growth in these highly urbanised districts in the Istanbul Metropolitan Area through determining the urban characterisation or pattern via Vegetation-Impervious Area-Soil (V-I-S) components model using Landsat TM images belonging to years of 1987 and 1997. The urban movement and its trend in the study area have been analysed by two different methods to confirm their respective accuracies within the conceptual framework of the V-I-S model. Classified and unclassified images were used as model inputs. Maps were generated in four main components of urban land cover: vegetation, impervious area, soil, and water. The results of the V-I-S model applied to the districts were further evaluated and tested in terms of their effectiveness in identifying and measuring the urban ecosystem composition. The results showed that both methods were successful in the V-I-S modelling using Landsat 5 TM images for the analysis of the urban ecology. Unclassified image data indicated approximately similar values as those in the unsupervised classification data. In other words, method analysis presented positive coherent conclusions.