10.57647/AP.2026.1001.07

Assessment and Modeling of Land Use/Land Cover Change Dynamics in Anzali International Wetland, Iran

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  • Abbas Rezanezhad1,
  • Mohammad Dehdar Dargahi*1,
  • Hasan Karimzadegan1,
  1. Department of Environment, La.C., Islamic Azad University, Lahijan, Iran

Received: 2025-12-14

Revised: 2026-03-13

Accepted: 2026-04-18

Published in Issue 2026-06-30

Copyright (c) 2026 Abbas Rezanezhad, Mohammad Dehdar Dargahi, Hasan Karimzadegan (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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Rezanezhad, A., Dehdar Dargahi, M., & Karimzadegan, H. (2026). Assessment and Modeling of Land Use/Land Cover Change Dynamics in Anzali International Wetland, Iran. Anthropogenic Pollution, 10(1). https://doi.org/10.57647/AP.2026.1001.07
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Abstract

Natural and human activities in wetland ecosystems result in ongoing land use and cover alterations. Moreover, the quick population growth in these areas intensifies land use changes and the transformation of natural cover, leading to the conversion of wetlands for residential purposes and subsequent infrastructure development. This study used satellite data to investigate and model land use changes in the area surrounding Anzali international wetland from 2000 to 2023; it also employed the Markov chain model, logistic regression, and artificial neural networks to predict potential land use changes by 2050. To determine changes in land use area within the studied ecosystem, land use maps of Anzali International wetland were extracted from Landsat satellite data related to 2000, 2013, and 2023. Then the Support Vector Machine (SVM) classification algorithm was used to identify six classes of land use: agricultural lands, grasslands, forests, reedbeds, built-up lands, and water bodies. The accuracy of land use maps derived from satellite images was assessed using two metrics: overall accuracy and kappa coefficient, yielding values of 89.56% and 0.84 for 2000, 93.83% and 0.91 for 2013, and 98.14% and 0.97 for 2023. They were also validated based on the 30% hold-out sample (91 points) obtained through random stratified sampling. The analysis of the change detection matrix indicated that the most significant alterations in land use from 2000 to 2023 involved the conversion of water bodies to other land uses (which accounted for a net loss of 3,170 hectares, representing a 33.4% reduction compared to the water body area in 2000). In contrast, the area of changes in other land uses showed an increase during this period. Projections for 2050 suggest that current trends will persist and intensify, leading to a further drastic reduction in the area of this body of water to approximately 898 hectares (only 12.7% of its 2000 extent), ultimately reaching its lowest level. Other land uses, particularly reedbeds, are anticipated to increase significantly and expand to cover about 38.1% of the study area by 2050, becoming the predominant land use in the region, while agricultural lands will continue to occupy a substantial part of the area.

Keywords

  • Markov chain models,
  • Logistic regression,
  • Neural networks,
  • Land Change Modeler (LCM),
  • Anzali international wetland
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