Comparative Analysis of Supervised and Unsupervised Classification of Sentinel-2 Imagery for Land Use and Land Cover Mapping: A Case Study of Gurlan District, Uzbekistan
Kalit so'zlar:
Gurlan District, Land Use and Land Cover, Sentinel-2, Supervised Classification, Unsupervised Classification, Remote SensingAnnotatsiya
Accurate land use and land cover (LULC) mapping is critical for sustainable land management, particularly in agricultural regions like Gurlan District, Uzbekistan. This study evaluates the performance of supervised (Random Forest, RF) and unsupervised (K-Means) classification methods using Sentinel-2 imagery for 2016, 2020, and 2024. Focusing on agricultural lands, built-up areas, and water bodies, the study assesses classification accuracy and detail in a semi-arid region. Results show that supervised classification outperforms unsupervised methods, with Sentinel-2 achieving higher overall accuracy (93% in 2024) due to its superior spatial resolution and spectral bands. Temporal analysis revealed a decline in agricultural lands and an increase in built-up areas. These findings underscore the efficacy of Sentinel-2 and supervised classification for precise LULC mapping, offering insights for regional planning and resource management.
Foydalanilgan adabiyotlar
1. Esfandeh, S.; Danehkar, A.; Salmanmahiny, A.; Sadeghi, S.M.M.; Marcu, M.V. Climate Change Risk of Urban Growth and Land Use/Land Cover Conversion: An In-Depth Review of the Recent Research in Iran. Sustainability 2021, 14, 338. [CrossRef]
2. Yao, Y.; Yan, X.; Luo, P.; Liang, Y.; Ren, S.; Hu, Y.; Han, J.; Guan, Q. Classifying Land-Use Patterns by Integrating Time-Series Electricity Data and High-Spatial Resolution Remote Sensing Imagery. Int. J. Appl. Earth Observ. Geoinf. 2022, 106, 102664. [CrossRef]
3. Qian, X.; Zhang, L. An Integration Method to Improve the Quality of Global Land Cover. Adv. Space Res. 2022, 69, 1427–1438.[CrossRef]
4. Schulz, D.; Yin, H.; Tischbein, B.; Verleysdonk, S.; Adamou, R.; Kumar, N. Land Use Mapping Using Sentinel-1 and Sentinel-2 Time Series in a Heterogeneous Landscape in Niger, Sahel. ISPRS J. Photogramm. Remote Sens. 2021, 178, 97–111. [CrossRef]
5. Viana, C.M.; Girão, I.; Rocha, J. Long-Term Satellite Image Time-series for land use/land cover change detection using refined open source data in a rural region. Remote Sens. 2019, 11, 1104. [CrossRef]
6. Praticò, S.; Solano, F.; Di Fazio, S.; Modica, G. Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation. Remote Sens. 2021, 13, 586. [CrossRef]
7. Sobhani, P.; Esmaeilzadeh, H.; Barghjelveh, S.; Sadeghi, S.M.M.; Marcu, M.V. Habitat Integrity in Protected Areas Threatened by LULC Changes and Fragmentation: A Case Study in Tehran Province, Iran. Land 2021, 11, 6. [CrossRef]
8. Steinhausen, M.J.; Wagner, P.D.; Narasimhan, B.; Waske, B. Combining Sentinel-1 and Sentinel-2 Data for Improved Land Use and Land Cover Mapping of Monsoon Regions. Int. J. Appl. Earth Observ. Geoinf. 2018, 73, 595–604. [CrossRef]
9. Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24-31.[CrossRef]
10. Claverie, M., Ju, J., Masek, J. G., et al. (2018). The Harmonized Landsat and Sentinel-2 surface reflectance data set. Remote Sensing of Environment, 219, 145-161. [CrossRef]
11. ESA. (2024). Sentinel-2 user guide. Retrieved from [CrossRef]
12. Liu, C.; Li, W.; Zhu, G.; Zhou, H.; Yan, H.; Xue, P. Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture. Remote Sens. 2020, 12, 3139. [CrossRef]
13. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20(1), 37–46. [CrossRef]
