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  • 标题:Investigating the Use of a Modified NSGA-II Solution for Land-Use Planning in Mediterranean Islands
  • 本地全文:下载
  • 作者:Miltiades Lazoglou ; Polychronis Kolokoussis ; Efi Dimopoulou
  • 期刊名称:Journal of Geographic Information System
  • 印刷版ISSN:2151-1950
  • 电子版ISSN:2151-1969
  • 出版年度:2016
  • 卷号:08
  • 期号:03
  • 页码:369-386
  • DOI:10.4236/jgis.2016.83032
  • 语种:English
  • 出版社:Scientific Research Publishing
  • 摘要:This paper explores the potential application of a modified version of the Non-dominated Sorting Genetic Algorithm (NSGA)-II for land-use planning in Mediterranean islands that constitute a geographical entity with similar characteristics. Study area is the island of Naxos, which is a typical Mediterranean island. In order to monitor the land-use changes of the island for the period 1987-2010, object-based classification of three Landsat images has been carried out. The 1987 land-use classification defined the initial population for the Genetic Algorithm (GA) and the aim was to provide the optimal development scenario for Naxos island taking into consideration legislation, geological characteristics and environmental parameters. The GA was used in order to introduce land use changes while maximizing transformation suitability, compactness, economic return, and minimizing soil erosion. The output of the GA was compared to the actual development of the island. The outcomes confirmed the proposed algorithm’s convergence process, while the GA solutions eventually formed a Pareto Front and performed adequately across all objectives. The GA algorithm has proposed reduction of Irrigated farming land by 16%, increase of Dry farming land by 131%, and the maximum allowed by the defined constraints increase of Urban land (100%), mostly on the eastern and central part of Naxos. These changes significantly differ from the actual development of the island. Economic return after optimization increased by 18%, while soil erosion decreased from 1948 t/y to 1843 t/y.
  • 关键词:Land-Use Planning;Multiobjective Optimization;Genetic Algorithm;NSGA-II;Landsat;OBIA
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