Detecting and Modelling the Trend of Change in the Forest Land Use in Garasu Watershed Area Using Landscape Metrics
Detecting, predicting and quantifying the trends of landscape pattern change in the forests of Gharasu watershed area are necessary so as to assess the crises or prevent them. To this aim, the land use maps belonging to the years 1987, 2002 and 2018 were classified through the maximum likelihood method, and the forest area changes were estimated. Then, through the Geomod model and the forest change probability map derived from the multi-criteria evaluation method, a forestland map was generated for the year 2041. Moreover, the quantitative characteristics and the spatial distribution of the forested area were evaluated using ten landscape metrics. The results revealed that 2632 hectares had been deforested over the last 31 years; also, it is predicted that 2084.7 more hectares of the forests will be reduced until 2041. The analysis of the landscape metrics also showed that the forest landscape had become more limited and fragmented, as well as becoming less regular and integrated. Through the landscape analysis approach, six of the ten metrics used in this study proved to have a regular trend of change. They include class area, number of patches, patch density, patch area mean, limiting circle and pore size. Thus, it can be concluded that Geomod is a quite successful model in predicting the forest areas for the year 2041.
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Monitoring the effects of land use and land cover changes on habitat quality in the Haraz watershed
S. Bazargan, F. Rajaei*, F. Ahmadi Mirqaed, M. Gholipour
Iranian Journal of Applied Ecology, -
Introducing a Novel Approach for Participatory Land- Use Planning Using Golestan Land- Use Planning Decision Support System
*, Abdolrassoul Salman Mahiny, Amir Sadoddin, Abdolreza Bahremand
Environmental Researches, -
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Leila Dehghani Firoozabadi, Alireza Ildoromi *, MirMehrdad Mirsanjari,
Journal of Geography and Planning, -
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Maryam Mokhtari *, , Seyed Ahmad Almasi
Journal of Civil Engineering, Winter 2020