Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/194424 
Year of Publication: 
2016
Citation: 
[Journal:] Journal of Urban Management [ISSN:] 2226-5856 [Volume:] 5 [Issue:] 2 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2016 [Pages:] 43-51
Publisher: 
Elsevier, Amsterdam
Abstract: 
As a result of the growing impacts on global environments, it has become important for land use planners to extract, detect, monitor and predict land use/cover changes (LUCCs). The monitoring of LUCCs within a certain time period and predicting future trends of temporal and spatial changes are absolutely necessary. The aim of this research was to analyze and monitor LUCCs in Naghadeh County, Iran over a time span of 27 years and predict the future trend of changes during the period of 2014-2041. Land use/cover (LULC) maps were extracted (as built-up regions, water body, agricultural and bare lands) for 1987, 2000 and 2014 via RS images obtained from Landsat TM, ETM+ and OLI, respectively. The overall classification and Kappa index for all the classified maps were over 85% and 0.8, respectively. The C-A Markov model was used to predict future trend of LULC for the next 27-years. The obtained Kappa agreement statistics results from comparing actual and simulated maps of 2014 suggested the high capability of the model in LUCCs simulation in the study area. The results indicated the growth of built-up regions (urban area) from 1989 to 2014, while there was decrease in bare land. The projected land use for 2041 revealed more urbanization with potential expansion in agricultural and bare lands. Therefore, if the current management trends continue without any attention to sustainability measures, remnant water body and bare lands decline will ensue. This research provides useful and up-to-date information to local land use planners, managers and policy-makers to step up towards sustainable development in the study area.
Subjects: 
CA-markov
GIS
Land use change
Prediction
RS
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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