S
Sima Fakheran
Researcher at Isfahan University of Technology
Publications - 42
Citations - 646
Sima Fakheran is an academic researcher from Isfahan University of Technology. The author has contributed to research in topics: Biology & Habitat. The author has an hindex of 10, co-authored 30 publications receiving 416 citations. Previous affiliations of Sima Fakheran include University of Zurich.
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Using the SLEUTH Urban Growth Model to Simulate Future Urban Expansion of the Isfahan Metropolitan Area, Iran
TL;DR: In this article, the authors used the SLEUTH model to simulate future urban expansion of Isfahan metropolitan area from 2010 to 2050, by making use of cellular automata methodology.
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Loss of habitat specialists despite conservation management in fen remnants 1995-2006.
TL;DR: Calcareous fens in the pre-Alps of Switzerland suffer from ongoing habitat deterioration and endangered plant species remain threatened, so it is suggested to reduce nutrient inputs and, where necessary, to restore hydrology and adjust grazing management.
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Adaptation and extinction in experimentally fragmented landscapes
Sima Fakheran,Cloé Paul-Victor,Christian Heichinger,Bernhard Schmid,Ueli Grossniklaus,Lindsay A. Turnbull +5 more
TL;DR: Simulations revealed that the observed loss of genetic diversity dwarfed that expected under drift, with dramatic diversity loss, particularly from dynamic landscapes, raising concern over the impact of increased levels of human-induced disturbance in natural landscapes.
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Multi objective land allocation (mola) for zoning ghamishloo wildlife sanctuary in iran
Shila Hajehforooshnia,Shila Hajehforooshnia,Alireza Soffianian,A. Salman Mahiny,Sima Fakheran +4 more
TL;DR: In this paper, the authors used GIS data processing and spatial analysis along with decision analysis techniques to define zones for Ghamishloo Wildlife Sanctuary according to I.U.C.N. category IV in Isfahan Province of Iran.
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Comparing different classification algorithms for monitoring mangrove cover changes in southern Iran
Neda Bihamta Toosi,Neda Bihamta Toosi,Alireza Soffianian,Sima Fakheran,Saeid Pourmanafi,Christian Ginzler,Lars T. Waser +6 more
TL;DR: In this paper, the authors evaluated and compared four supervised classification algorithms based on Landsat time series imagery to detect mangrove cover in southern Iran, and compared the four different predictions resulting from the applied classification algorithms.