Estimating the Severity of Landscape Degradation in Future Management Scenarios based on Modeling the Dynamics of Hoor Al-Azim International Wetland in Iran-Iraq Border

AuthorsJahanishakib Fatemeh,Sharif Joorabian Shooshtari
JournalScientific Reports
Page number1-17
Serial number14
Volume number11877
IF4.259
Paper TypeFull Paper
Published At2024
Journal TypeElectronic
Journal CountryIran, Islamic Republic Of
Journal IndexISI،JCR،Scopus

Abstract

Temporal and spatial changes in land cover in wetland ecosystems indicate the severity of degradation. Understanding such processes in the past, present, and future might be necessary for managing any type of development plan. Therefore, this research has monitored and analyzed the Hoor Al-Azim International Wetland to determine the orientation of its changes in various future scenarios. Wetland status modeling was conducted using developed hybrid approaches and cellular automata along with evaluating the accuracy of the modeled maps. The dynamics of the landscape were simulated using a higher accuracy approach in three scenarios—Water Conservation, Water Decreasing, and Business-as-Usual- to get the level of degradation of the wetland. The results showed that the amount of water in the wetland has decreased in all three periods, and the salt lands and vegetation have undergone drastic changes. The water bodies experienced a reduction of 148,139 ha between 1985 and 2000, followed by a decrease of 9107 ha during the 2000–2015 period. However, based on the results, these developments are expressed better by the developed hybrid approach than the CA-MC approach and are more reliable for future simulation. The figure of merit index, which assesses the hybrid model's accuracy, yielded a value of 18.12%, while the CA-MC model's accuracy was estimated at 14.42%. The assessment of degradation in hexagonal units showed the least degradation in the water conservation scenario compared with the other two scenarios in 2030.

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tags: Temporal-spatial changes, Landscape metrics, Effective variables, Validation, Artificial neural network, Hexagonal units