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MohammadEsmaeil, Z., Navidi, M., Kharazmi, R. (2025). Analysis of the Trends in Land Use and Land Cover Changes in Tehran (1991–2021). , 13(2), 175-190. doi: 10.22092/lmj.2025.370601.393
Zahra MohammadEsmaeil; MirNasser Navidi; Rasoul Kharazmi. "Analysis of the Trends in Land Use and Land Cover Changes in Tehran (1991–2021)". , 13, 2, 2025, 175-190. doi: 10.22092/lmj.2025.370601.393
MohammadEsmaeil, Z., Navidi, M., Kharazmi, R. (2025). 'Analysis of the Trends in Land Use and Land Cover Changes in Tehran (1991–2021)', , 13(2), pp. 175-190. doi: 10.22092/lmj.2025.370601.393
MohammadEsmaeil, Z., Navidi, M., Kharazmi, R. Analysis of the Trends in Land Use and Land Cover Changes in Tehran (1991–2021). , 2025; 13(2): 175-190. doi: 10.22092/lmj.2025.370601.393

Analysis of the Trends in Land Use and Land Cover Changes in Tehran (1991–2021)

مدیریت اراضی
Article 4, Volume 13, Issue 2, January 2026, Pages 175-190    PDF (1.38 M)
Document Type: Research
DOI: 10.22092/lmj.2025.370601.393
Authors
Zahra MohammadEsmaeil* 1; MirNasser Navidi2; Rasoul Kharazmi3
1Researcher, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Tehran, Iran.
2Associate prof., at Soil and Water research institute, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran.
3Assistant prof., at Soil and Water research institute, Agricultural Research Education and Extension Organization (AREEO), Tehran, Iran.
Abstract
Land use and land cover (LULC) changes have always had profound impacts on environmental sustainability and natural resources, especially in a city like Tehran as one of the most densely populated metropolises in the Middle East. The urban dynamics in Tehran was investigated in this study using remote sensing data, including Landsat (TM, ETM+, and OLI) and Sentinel-2 images from 1991 to 2021 collected from Google Earth Engine and USGS platforms before being pre-processed using geometric, radiometric, and atmospheric corrections via the FLAASH algorithm. To identify the LULC patterns, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI) were calculated, followed by unsupervised classification using the K-means algorithm to delineate five primary land use classes: built-up areas, agricultural lands, orchards, barren lands, and water bodies. Subsequently, supervised classification methods, including Support Vector Machine (SVM), Minimum Distance (MD), and Maximum Likelihood Classifier (MLC), were evaluated to reveal the superior performance (an overall accuracy of 91% and a Kappa coefficient of 0.89) of SVM. Change detection using the post-classification comparison method revealed a 39.7% increase in built-up areas (from 410.36 to 573.51 km²) but decreases of 69.3% and 9% in agricultural and orchard lands, respectively. The expansion in built-up areas, primarily towards western and southern stretches of Tehran, has led to the conversion of fertile agricultural lands into urban and industrial uses, posing such risks as intensified urban heat islands (projected to increase by 3.43 °C by 2050), diminished food security, and water and soil resource degradation. These findings underscore the urgent need for sustainable urban policies, including protection of the remaining agricultural lands, promotion of vertical urban development, and integrated water resource management, to steer Tehran towards environmental sustainability and provide a model for other developing metropolises.
Keywords
LULC; Multitemporal Satellite Imagery; Support Vector Machine; Cross-Validation; Kappa Coefficient
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