Document Type : Research Article
Authors
1
Ph.D. in Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran
2
Associate Professor, Department of Range and Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran
3
Professor, Department of Environmental Sciences, Macquarie University, Sydney, Australia
Abstract
Investigating the prediction of land use changes is one of the crucial factors for understanding environmental transformations at all temporal and spatial scales. The present research aims to examine the trend of changes and predict the future land use status in the Zolachay Watershed located in West Azerbaijan Province, one of the sub-basins of Lake Urmia, in the last 33 years. For this purpose, first, the Sentinel-2 and Landsat 5,7 images for 1990, 2020, 2016, 2010, 2005, 2000, 1995, and 2023 were acquired from their official sites. Then, needed preprocessing methods were applied in various software environments, and the relevant images were produced inside the e Cognition software environment. Then, the nearest neighbor classification model was executed using the object-based method, and land use and landcover maps were generated. Finally, using the Markov Chain Cellular Automata (CA) method, simulations of land use changes for the year 2030 were performed. To assess the accuracy of the CA Markov model, the land use change map for 2023 was validated against the 2023 classification map. The final results indicate that applying knowledge-based methods, especially the nearest neighbor classification, allows for the product of land use maps with a high accuracy coefficient (Kappa 91%), followed by the Markov (CA) model change maps with an acceptable accuracy of 87%. The final results demonstrate that by 2030, agricultural land use will increase by 15.03%, residential areas by 9.0%, and drylands by about 14%. Soil land use will decrease by 23.68% and pastures by 6.5%. Overall, the final models indicate the high accuracy of knowledge-based and object-based methods, as well as the satisfactory performance of the Markov model in the study of land use changes. The findings of this research can serve as a reference in future environmental planning processes, aiming at sustainable recommendations and prudent land utilization.
Extended Abstract
Introduction
Understanding land use changes is fundamental for analyzing environmental dynamics and managing natural resources at multiple scales. Remote sensing data combined with simulation models like the Cellular Automata-Markov (CA-Markov) enables effective monitoring and spatiotemporal prediction of land use patterns. Advanced object-based classification methods, using software such as eCognition, improve the accuracy of land cover analysis. Numerous global and regional studies demonstrate the efficacy of these approaches in assessing land use trends. This study aims to analyze the land use changes in the Zolachay watershed, a sub-basin of Lake Urmia, from 1990 to 2023 and predict future changes up to 2030, utilizing Sentinel-2 satellite imagery and advanced image processing techniques to provide valuable data for watershed management and environmental planning.
Material and Methods
The Zolachay watershed, a sub-basin of Lake Urmia located in the northwest of the lake, covers an area of 2,258 km². Originating from the Qaradash and Saridash mountains near the Iran-Turkey border, the perennial Zolachay River flows through several villages before emptying into the northwest part of Lake Urmia.
In order to produce land use maps in the present study, Landsat 5, 7 and Sentinel 2 satellite images were used, with a spatial resolution of 30 and 10 meters and having various spectral ranges.
Based on the research objective, which is to detect land use changes, considering the knowledge of the study area and Google Earth images and training points taken by GPS, land use classes were divided based on water area use, residential areas, pasture, salt marsh, gardens and irrigated lands, dry lands and soil. Image classification with the object-oriented method was used using the nearest neighbor NNC algorithm and samples were selected for each class according to the visual characteristics, and classification conditions were defined for each land use class. Land use maps were extracted for 8 periods with the object-oriented method, the area of the classified land uses was calculated. In the next step, land use change prediction was performed using Markov and CA-Markov.
In this study, after formatting the classified images and valuing the land use classes, Markov chain calculations were performed using combination functions in TerrSet 2020 software. Then, land use prediction layers were generated and the final results were evaluated. These results were transferred to other software for additional analyses. Subsequently, the digital layers were imported into ArcGIS 10.8 software, targeted analyses and layer combinations were performed, and the final results were extracted.
Results and Discussion
To verify the accuracy of the classification map of the Zolacha watershed in 2023, it was compared with the forecast map in 2023. The values of Kno, Klocation, Klocationstrata and the standard kappa values Kstandard were obtained as 0.87%, 0.93%, 0.93% and 0.84%, respectively. The results show that the area of agricultural land use has increased rapidly, so that the area of irrigated agriculture land use will increase from 36.8% and 89.6% of the total area of the basin in 2016 to more than 39.23% and 46.21% in 2030. And the area of pasture and soil land use will decrease from 36.20% and 47.93% of the total area of the basin in 2016 to 29.7% and 24.25% in 2030, respectively. More precisely, the area of two land uses, irrigated areas and pastures, has decreased, and dryland, residential, orchards and irrigated lands and salt marshes have increased. And it is predicted that in 2030, the area of land uses, irrigated areas and pastures will decrease, and the area of residential, orchards and irrigated lands, dryland and salt marshes will increase. Finally, the land uses of irrigated areas and orchards, dryland, residential, irrigated areas and salt marshes with an area of 188.74, 157.61, 3.78, 8.1 and 0.12 km2 of total area in 1990 will reach 18.528, 63.484, 36.24, 9.2 and 0.001 km2 of total area in 2030. Based on the results of the land use change trend in the present study, the most land use changes are related to the conversion of soil and pasture land to total irrigated land and orchards, dryland and residential land.
Conclusions
From the results obtained, practical strategies and measures can be taken to control land use change and preserve natural lands. These measures can include measures to preserve sensitive areas such as water areas and pastures, sustainable development of gardens, and upgrading urban infrastructure in residential areas. These approaches seek to maintain ecological and economic balance in the region and can help improve the quality of life of the community and protect the environment. Also, land use change is evaluated as a balancing factor of ecological, hydrological, and economic components, and its effects can affect the economic situation and lifestyle of the people of the region. These findings can be used in the future environmental planning process, especially in the development of sustainable recommendations and principled land use.
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