َArdavan Zarandian; Roya Mousazadeh; Jalil Badamfirooz; Alireza Rahmati
Volume 16, Issue 2 , July 2018, , Pages 111-132
Abstract
Introduction:
Changes in land use and/or land cover (LULC) are associated with many socio-economic and physical environmental factors. Due to the multiplicity and diversity of variables involved and drivers that cause changes, the prediction of future conditions of LULC patterns is complex and faces ...
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Introduction:
Changes in land use and/or land cover (LULC) are associated with many socio-economic and physical environmental factors. Due to the multiplicity and diversity of variables involved and drivers that cause changes, the prediction of future conditions of LULC patterns is complex and faces many uncertainties. Meanwhile, environmental and development planners need to consider clearly how their current decisions may shape the future structure of the landscape. Therefore, in the policy-making and planning process, there is always the question of how to predict future LULC changes. In recent years, thanks to advances in remote sensing knowledge and spatial data generated from satellite imagery as well as evolving modelling tools, it has been possible to simulate complex natural systems and simplify them with a specific emphasis on more important variables depending on the issues being investigated.
Materials and methods:
With this in mind, the present study was conducted in a pilot forested landscape of the Hycanian vegetative region located in Mazandaran Province in northern Iran to detect the changing trends of LULC over the period of 1984-2016 as well as to project and analyze the plausible future shape of the landscape by the year 2040 using InVest scenario-generator software model (Sharp, 2014). To conduct this modelling process, two plausible future scenarios were defined entitled Business As Usual (BAU), which reflected the continuation of current LULC changes with no management intervention, and Balanced Development (BD) involving governmental intervention to prevent current changes through conservation and restoring forest cover along with an adjusted developmental policy for human settlements. Then, the input data required to run the model was provided and the future landscapes under both scenarios were simulated and compared.
Results and discussion:
The results showed that, under the BAU scenario, there will be dramatic changes in the landscape structure which will lead to a significant loss in the natural forest cover, destruction of farmlands and its replacement with human settlements. Conversely, the BD scenario showed how land management through forest conservation and restoration policies, simultaneously with adjusted land conversion for settlement construction, can be transformed into a win-win strategy for a balanced development strategy. Also, in this study, the InVEST scenario generator model was compared with some other models (Azinmehr et al., 2013; Blainski et al., 2017; Eskandari, 2014; Han et al., 2015; Samie et al., 2017) used to simulate LULC, and its advantages and limitations were discussed.
Conclusion:
Finally, the scenario simulation with the method introduced here can be used in different studies (including various environmental assessments, economic valuations, etc.) to make the predictions more accurate. Moreover, this kind of modelling can make insight for planners and decision makers in the fields of development, conservation and land use planning, so that future plans are based on logical assumptions with less uncertainty.
