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Assessment and Prediction of Land Use Changes Using a Modeling Approach in a Geographic Information System Environment (Case Study: Talar Watershed)
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Shamim Ahmadi    , Ataollah Kavian * , Karim Soleimani , Kaka Shahidi , Abdolvahid Khaledi Darvishaan |
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Abstract: (95 Views) |
Abstract Abstract
Introduction
Land Use and Land Cover (LULC) changes have garnered significant attention in recent decades as a key factor in environmental and socio-economic analyses. These changes, particularly in watersheds, have far-reaching impacts on ecosystems and natural resources. Understanding past land use changes and predicting future trends is essential for strategic, dynamic, and forward-looking planning. Today, Remote Sensing (RS) and Geographic Information Systems (GIS) provide efficient, rapid, and cost-effective tools for assessing and predicting land use changes. This study focuses on the Talar Watershed, located in Mazandaran Province, which is part of the Caspian Sea basin. Due to its unique climatic and topographic conditions, this area is considered one of the critical regions in terms of land use changes driven by human activities such as urban development, agriculture, and deforestation. The present study aims to examine land use changes from 1996 to 2021 using satellite images (from the years 1996, 2008, and 2021) and to predict land use for 2046 in the Talar Watershed in Mazandaran Province. Additionally, the intensity and type of changes have been analyzed through a transition probability matrix, along with the rate of land use changes and land use dynamics.
Methodology
In this study, maps and data related to land use and physical and socio-economic variables (including slope, elevation, distance from water bodies, distance from roads, and distance from urban areas) were utilized. Landsat 5 and 8 satellite images with a spatial resolution of 30 meters were used to prepare land use maps. Data processing was conducted using the Google Earth Engine platform, employing a pixel-based algorithm and the Random Forest (RF) method with a Kappa index of 99%. Various indices, such as NDVI for vegetation, NDWI for water bodies, and NDBI for barren lands and urban areas, were used to enhance land use classification. An Elevation map was also used to improve the differentiation of features. Land use maps for three time points—1996, 2008, and 2021—were identified and classified into eight categories: water, forest, built-up areas, rangelands, forested rangelands, barren lands, irrigated agriculture, and rainfed agriculture. For land use change analysis and prediction, the MOLUSCE plugin in QGIS software was used. The analysis of changes from 1996 to 2021 was performed using intensity analysis and transition potential modeling. Additionally, predictive modeling for 2046 and its comparison with the current situation was conducted. Analyses were based on transition probability matrices and the Kappa index.
Results
The analysis of land use changes revealed significant transformations in the study area from 1996 to 2021. Specifically, the extent of forested rangelands and forest areas decreased by 20% and 9%, respectively, while built-up and rangeland areas increased by 34% and 23%, respectively. The rate of land use changes indicated that rangelands and barren lands experienced the most significant changes from 1996 to 2021. Positive changes were observed in built-up and rangeland areas, while negative trends were evident in forested and water body areas. The transition probability matrix showed that rangelands and forest areas are among the most stable land use classes, while barren lands are the most unstable. Land use simulation using the Markov chain and Cellular Automata (CA-ANN) model for 2046 predicted further decreases in forested and forest areas and an increase in built-up and rangeland areas, indicating a continuation of the current trend. These changes are primarily due to the reduction in forested rangelands and forest areas, influenced by social and economic factors, and indicate pressure on natural resources and the risk of biodiversity loss. Additionally, there is evidence of increased construction in dried-up water bodies caused by climate change, as well as a decline in forested lands. Each of these changes may have negative environmental consequences and could potentially lead to natural hazards in the future.
Discussion and Conclusion
In this research, land use change modeling was carried out to evaluate the accuracy of the Markov chain model and Cellular Automata (CA-ANN) of the MOLUSCE plugin using the Kappa index. The results of this study demonstrate the model’s acceptable accuracy in predicting future land use changes. The findings indicate that land use changes in the Talar watershed are significantly influenced by physical and socio-economic factors. In particular, the reduction of forested lands and wooded rangelands, along with the increase in residential and rangeland areas, underscores the need for periodic assessments of natural resources and efforts to plan and enhance these resources. The expansion of residential areas and the fragmentation of forested lands and wooded rangelands may lead to further environmental degradation and hydrological changes. According to the predictions, these pressures will persist in the future; therefore, the formulation and implementation of effective land use management policies and sustainable development strategies are essential to mitigate negative impacts and maintain a balance in natural resources. This study emphasizes the necessity of precise evaluations, strategic planning, and the adoption of appropriate strategies at both local and national levels to reduce forest degradation and preserve biodiversity and natural resources. Furthermore, the findings of this research can serve as a foundation for similar studies in other regions, aiding in the improvement of environmental management and sustainable development in vulnerable areas.
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Article number: 2 |
| Keywords: Detection, MOLUSCE, prediction, simulation, Talar, land use change dynamics |
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Type of Study: Research |
Subject:
Special
Received: 2024/09/3 | Accepted: 2024/10/29 | Published: 2025/06/9
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ahmadi S, Kavian A, Soleimani K, Shahidi K, Khaledi Darvishaan A. Assessment and Prediction of Land Use Changes Using a Modeling Approach in a Geographic Information System Environment (Case Study: Talar Watershed). jwmseir 2025; 19 (68) : 2 URL: http://jwmsei.ir/article-1-1175-en.html
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