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Mousavi, Amin, Enjavinezhad, Sayyed Mahmoud, Alavipanah, Seyed Kazem, Raga, María, Maleki, Sedigheh, Naghibi, Seyed Javad. (1404). Land Use and Land Cover Change Assessment Using Support Vector Machine and Random Forest.. , 30(2), 271-286. doi: 10.22059/jdesert.2025.106078
Amin Mousavi; Sayyed Mahmoud Enjavinezhad; Seyed Kazem Alavipanah; María Fernández Raga; Sedigheh Maleki; Seyed Javad Naghibi. "Land Use and Land Cover Change Assessment Using Support Vector Machine and Random Forest.". , 30, 2, 1404, 271-286. doi: 10.22059/jdesert.2025.106078
Mousavi, Amin, Enjavinezhad, Sayyed Mahmoud, Alavipanah, Seyed Kazem, Raga, María, Maleki, Sedigheh, Naghibi, Seyed Javad. (1404). 'Land Use and Land Cover Change Assessment Using Support Vector Machine and Random Forest.', , 30(2), pp. 271-286. doi: 10.22059/jdesert.2025.106078
Mousavi, Amin, Enjavinezhad, Sayyed Mahmoud, Alavipanah, Seyed Kazem, Raga, María, Maleki, Sedigheh, Naghibi, Seyed Javad. Land Use and Land Cover Change Assessment Using Support Vector Machine and Random Forest.. , 1404; 30(2): 271-286. doi: 10.22059/jdesert.2025.106078

Land Use and Land Cover Change Assessment Using Support Vector Machine and Random Forest.

Desert
دوره 30، شماره 2، اسفند 2025، صفحه 271-286    اصل مقاله (1.17 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jdesert.2025.106078
نویسندگان
Amin Mousavi* 1؛ Sayyed Mahmoud Enjavinezhad2؛ Seyed Kazem Alavipanah1؛ María Fernández Raga3؛ Sedigheh Maleki2؛ Seyed Javad Naghibi4
1Department of Remote Sensing and GIS, Faculty of Geography, University of Tehran, Tehran, 1417853933, Iran.
2Department of Soil Sciences, School of Agriculture, Shiraz University, Shiraz, Iran.
33 Department of Chemistry Applied Physics, Industrial Engineering School, University of León, León, Spain.
4Department of Soil Sciences, School of Agriculture, Shiraz University, Shiraz, Iran and Head of Research & Extension Office, Landscape & Green Spaces Organization of Shiraz Municipality, Shiraz, 45366-78, Iran.
چکیده
This study evaluates land use and land cover (LULC) changes in Fars Province, Iran, using machine learning algorithms, specifically comparing the performance of two non-parametric support vector machine (SVM) and random forest models. With rapid urbanization and agricultural expansion, accurate LULC classification is critical for environmental monitoring and land management.  Applying the Google Earth Engine platform, multi-temporal Landsat 8 imagery was assessed. The findings demonstrated all classification methods presented high accuracy metrics and kappa coefficient values. The SVM algorithm attaining a mean overall accuracy of 91.42% for Landsat 8 imagery to show best performance among all evaluated methods. According to LULC change detection performed by the most accurate classification algorithm, the results indicated an increase in urban parks, gardens, and mountainous rangelands, while barren lands experienced a decline. The evaluation of LULC changes impacts on land surface temperature (LST) shows that enhanced vegetation cover played a key role in reducing LST. A remarkable decrease in both maximum and minimum LST values was observed, declining 37.31°C and 22.47°C in 2019 to 34.45°C and 19.98°C in 2023, respectively. Furthermore, the findings highlight integrating high-resolution satellite imagery with the SVM algorithm leads to achieve a highly accurate and efficient approach for LULC mapping. Consequently, this method proves to be a valuable tool for decision-making in natural resource management and urban planning in similar regions.
کلیدواژه‌ها
Google Earth Engine؛ Landsat 8 Imagery؛ Land management؛ Land surface temperature؛ Kappa coefficient
آمار
تعداد مشاهده مقاله: 77
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