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Dariaee Aftabi M, Nafarzadegan A R, Bagheri A. Spatiotemporal Analysis of Land Use and Vegetation Cover in the Takht–Qaleh Qazi Watershed Using Satellite Imagery and Machine Learning Algorithms. E.E.R. 2025; 15 (4)
URL: http://magazine.hormozgan.ac.ir/article-1-902-en.html
URL: http://magazine.hormozgan.ac.ir/article-1-902-en.html
University of Hormozgan , a.r.nafarzadegan@hormozgan.ac.ir
Abstract: (7 Views)
This study aimed to investigate land use changes and temporal vegetation dynamics in the Takht–Qaleh Qazi watershed over the period 2014 to 2024. The research process was designed in two main stages: The first stage involved generating classified land use maps for the target years (2014 and 2024) using satellite imagery and machine learning algorithms. The second stage consisted of analyzing temporal vegetation changes using spectral indices and multi-temporal Landsat and Sentinel-2 images within the Google Earth Engine environment. To reduce dimensionality and remove highly correlated variables, feature selection was conducted in two steps: Pearson correlation analysis via SPSS was first used to exclude highly correlated variables, followed by the Boruta algorithm in R to select the most important variables for modeling. Classification modeling was performed using Support Vector Machine (SVM) and Random Forest (RF) algorithms. Models were trained on training data and validated with evaluation data. Performance was assessed using appropriate statistical metrics. Finally, trained models were applied to the entire study area to produce and save final land use/land cover classification maps.
This study evaluated the performance of two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), for land use classification and spatiotemporal analysis in the Takht–Ghaleh Ghazi watershed from 2014 to 2024. Both models showed improved classification accuracy over time, with SVM achieving superior results, particularly in identifying agricultural and orchard lands. By 2024, SVM accuracy reached 83% (Kappa = 0.75), outperforming RF’s 78% accuracy (Kappa = 0.67). Analysis of land use changes revealed a decrease in agricultural land and water bodies, accompanied by increases in orchards and barren lands, reflecting dynamic land management and environmental pressures in the region. These findings underscore the potential of integrating advanced remote sensing and machine learning techniques for sustainable natural resource management and environmental monitoring.
This study evaluated the performance of two machine learning algorithms, Support Vector Machine (SVM) and Random Forest (RF), for land use classification and spatiotemporal analysis in the Takht–Ghaleh Ghazi watershed from 2014 to 2024. Both models showed improved classification accuracy over time, with SVM achieving superior results, particularly in identifying agricultural and orchard lands. By 2024, SVM accuracy reached 83% (Kappa = 0.75), outperforming RF’s 78% accuracy (Kappa = 0.67). Analysis of land use changes revealed a decrease in agricultural land and water bodies, accompanied by increases in orchards and barren lands, reflecting dynamic land management and environmental pressures in the region. These findings underscore the potential of integrating advanced remote sensing and machine learning techniques for sustainable natural resource management and environmental monitoring.
Received: 2025/07/13
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