Forecasting land-cover growth using remotely sensed data: a case study of the Igneada protection area in Turkey

A. Gonca Bozkaya, Filiz Bektas Balcik*, Cigdem Goksel, Hayriye Esbah

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

33 Citations (Scopus)

Abstract

Human activities in many parts of the world have greatly affected natural areas. Therefore, monitoring and forecasting of land-cover changes are important components for sustainable utilization, conservation, and development of these areas. This research has been conducted on Igneada, a legally protected area on the northwest coast of Turkey, which is famous for its unique, mangrove forests. The main focus of this study was to apply a land use and cover model that could quantitatively and graphically present the changes and its impacts on Igneada landscapes in the future. In this study, a Markov chain-based, stochastic Markov model and cellular automata Markov model were used. These models were calibrated using a time series of developed areas derived from Landsat Thematic Mapper (TM) imagery between 1990 and 2010 that also projected future growth to 2030. The results showed that CA Markov yielded reliable information better than St. Markov model. The findings displayed constant but overall slight increase of settlement and forest cover, and slight decrease of agricultural lands. However, even the slightest unsustainable change can put a significant pressure on the sensitive ecosystems of Igneada. Therefore, the management of the protected area should not only focus on the landscape composition but also pay attention to landscape configuration.

Original languageEnglish
Article number59
JournalEnvironmental Monitoring and Assessment
Volume187
Issue number3
DOIs
Publication statusPublished - Mar 2015

Bibliographical note

Publisher Copyright:
© 2015, Springer International Publishing Switzerland.

Keywords

  • Cellular automata Markov model
  • Igneada
  • Image processing
  • Land use/land cover
  • Remote sensing
  • Stochastic Markov model

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