Document Type : Original Research Paper
Authors
Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran
Abstract
Barckground and Objectives: Rapid urbanization has made land use change studies essential for urban planning. Parand, a new town near Tehran, has undergone significant development over the past two decades. This study aims to quantitatively monitor land use changes, focusing on the expansion of built-up areas and reduction of barren land, compare land use maps for two periods, assess classification accuracy, and analyze the spatial pattern of Parand’s urban growth and its implications.
Methods: A multi-sensor approach was applied to analyze urban development, using Landsat-7 (2000) as the baseline and Sentinel-2 (2024) as the contemporary dataset, with Sentinel-1 radar data to improve urban texture separation. Data processing was performed in Google Earth Engine. Four classes—vegetation, built-up, barren land, and roads—were classified using Random Forest. Training samples were derived from visual interpretation and ancillary data. Validation involved an error matrix, overall accuracy, and the kappa coefficient. Outputs included binary built-up maps, classified land use maps, and statistical tables for both periods.
Findings: The results of the analyses indicated that the area of built-up land in Parand increased substantially during the study period. In 2000 the built-up area was approximately 3.66 square kilometers, whereas in 2024 it exceeded 14.31 square kilometers. This growth represents an increase of more than fourfold over two decades. At the same time, barren land, which covered 146.66 square kilometers in 2000, decreased to 134.50 square kilometers in 2024, indicating the conversion of a large portion of these lands into urban areas. Spatial analysis of the changes showed that urban growth was mainly concentrated in the eastern and southern sectors of Parand, shifting from an initially dispersed pattern to a more cohesive and organized structure. Comparison of classification performance also showed that the 2024 map, with an overall accuracy of 95.97 percent and a kappa coefficient of 0.9444, had higher quality than the 2000 map, which had an overall accuracy of 89.06 percent and a kappa of 0.8201. This difference is attributed to the higher spatial resolution of the newer data and the fusion of optical and radar sources. Vegetation and roads showed little change, and their relative stability is consistent with the more permanent nature of these classes.
Conclusion: Findings show that combining optical and radar data with machine learning in a cloud environment is effective for long-term urban monitoring. Rapid built-up expansion, especially in eastern and southern Parand, highlights the need for revised land use policies, infrastructure development, and agricultural land protection. This approach supports sustainable urban planning and investment prioritization, though challenges such as radar noise, limited field data, and class separability remain.
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© 2026 The Author(s). This is an open-access article distributed under the terms and conditions of the Creative Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
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