Monitoring Land Use Changes Using Satellite Images and an Object-Based Classification Model (Case Study: Mehran Plain)

Document Type : Research Paper

Authors

1 MSc in Soil and Water Conservation, Department of Rangeland and Watershed Management, Faculty of Agriculture, Ilam University, Ilam, Iran.

2 Associate Professor, Department of Rangeland and Watershed Management, Faculty of Agriculture, Ilam University, Ilam, Iran.

3 PhD in Combating Desertification, Department of management arid and desert regions, College of Natural Resources and Desert, Yazd University, Yazd, Iran.

4 Assistant Professor, Department of Rangeland and Watershed Management, Faculty of Agriculture, Ilam University, Ilam, Iran.

Abstract

Land use and land cover (LULC) changes are among the most significant environmental–socioeconomic processes affecting ecosystems, biodiversity, and the provision of ecosystem services. Urban expansion, agricultural development, and the conversion of rangelands into croplands are major drivers of land use change that may lead to land degradation and reduced resource sustainability. Therefore, continuous and accurate monitoring of LULC changes is essential for resource planning and management. The present study aims to investigate land use changes in the Mehran Plain, Ilam Province, using Landsat satellite imagery including Landsat 5 (1996), Landsat 7 (2002 and 2010), and Landsat 8 (2022) through an object-based classification approach. To present and analyze the classification results, indices such as Principal Component Analysis (PCA), the Normalized Difference Vegetation Index (NDVI), and change detection techniques were employed. The results indicated that barren lands achieved producer’s and user’s accuracies exceeding 99% in 2002, demonstrating high spectral separability of remote sensing data for this class. Conversely, the lowest producer’s and user’s accuracies were observed for barren lands in 2010, with values of 79% and 77%, respectively. The highest overall classification accuracy was obtained for 2022, with an overall accuracy of 90.28% and a Kappa coefficient of 86.89%. The greatest land use area changes in the study area were related to agricultural lands and fair and poor rangelands. NDVI results from 1996 to 2022 indicated an increase in vegetation cover in 2022, mainly due to the expansion of agricultural lands. Change detection analysis revealed that the most significant transitions involved the conversion of fair rangelands into agricultural lands and poor rangelands. The findings can contribute to land evaluation, environmental studies, and integrated planning and management for the sustainable utilization of natural resources and the reduction of land degradation in the region

Keywords

Main Subjects

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Geography and Territorial Spatial Arrangement
Volume 16, Issue 59
June 2026
Pages 217-248

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  • Receive Date: 21 June 2024
  • Revise Date: 09 April 2026
  • Accept Date: 13 April 2026

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