Classification of Agricultural Crops with Random Forest and Support Vector Machine Algorithms Using Sentinel-2 and Landsat-8 Images
Murat Güven Tuğaç, Fatih Fehmi Şimşek, Harun TorunlarMonitoring crop development and mapping cultivated areas are important for reducing risks to food security due to climate change. Remote sensing techniques contribute significantly to the efficient and effective management of agricultural production. In this study, agricultural fields (sunflower, wheat, maize, oat, chickpea, sugar beet, alfalfa, onion, fallow) and other fields (nonagricultural, pasture, lake) were identified by using Random Forest (RF) and Support Vector Machines (SVM) machine learning algorithms with Sentinel-2 and Landsat-8 images in the area covering Polatlı, Haymana and Gölbaşı districts of Ankara province Multi-temporal images were used to distinguish winter and summer crops, taking into account crop development periods. As a result of classification; the overall accuracy of RF and SVM models with S2 images are 89.5% and 84.6% and kappa coefficients are 0.88 and 0.83, while the overall accuracy of RF and SVM models with L8 images are 79% and 78.1% and kappa coefficients are 0.76 and 0.75. RF model was found to have higher prediction accuracy than SVM. Sentinel-2 imagery has a higher accuracy in all classes compared to Landsat-8, indicating that Sentinel-2 imagery with its high temporal and spatial resolution is more suitable and has a great potential for agricultural crop pattern detection.
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References
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APA
Tuğaç, M.G., Şimşek, F.F., & Torunlar, H. (2024). Classification of Agricultural Crops with Random Forest and Support Vector Machine Algorithms Using Sentinel-2 and Landsat-8 Images. International Journal of Environment and Geoinformatics, 11(3), 106-118. https://doi.org/10.30897/ijegeo.1479116
AMA
Tuğaç M G, Şimşek F F, Torunlar H. Classification of Agricultural Crops with Random Forest and Support Vector Machine Algorithms Using Sentinel-2 and Landsat-8 Images. International Journal of Environment and Geoinformatics. 2024;11(3):106-118. https://doi.org/10.30897/ijegeo.1479116
ABNT
Tuğaç, M.G.; Şimşek, F.F.; Torunlar, H. Classification of Agricultural Crops with Random Forest and Support Vector Machine Algorithms Using Sentinel-2 and Landsat-8 Images. International Journal of Environment and Geoinformatics, [Publisher Location], v. 11, n. 3, p. 106-118, 2024.
Chicago: Author-Date Style
Tuğaç, Murat Güven, and Fatih Fehmi Şimşek and Harun Torunlar. 2024. “Classification of Agricultural Crops with Random Forest and Support Vector Machine Algorithms Using Sentinel-2 and Landsat-8 Images.” International Journal of Environment and Geoinformatics 11, no. 3: 106-118. https://doi.org/10.30897/ijegeo.1479116
Chicago: Humanities Style
Tuğaç, Murat Güven, and Fatih Fehmi Şimşek and Harun Torunlar. “Classification of Agricultural Crops with Random Forest and Support Vector Machine Algorithms Using Sentinel-2 and Landsat-8 Images.” International Journal of Environment and Geoinformatics 11, no. 3 (Dec. 2024): 106-118. https://doi.org/10.30897/ijegeo.1479116
Harvard: Australian Style
Tuğaç, MG & Şimşek, FF & Torunlar, H 2024, 'Classification of Agricultural Crops with Random Forest and Support Vector Machine Algorithms Using Sentinel-2 and Landsat-8 Images', International Journal of Environment and Geoinformatics, vol. 11, no. 3, pp. 106-118, viewed 23 Dec. 2024, https://doi.org/10.30897/ijegeo.1479116
Harvard: Author-Date Style
Tuğaç, M.G. and Şimşek, F.F. and Torunlar, H. (2024) ‘Classification of Agricultural Crops with Random Forest and Support Vector Machine Algorithms Using Sentinel-2 and Landsat-8 Images’, International Journal of Environment and Geoinformatics, 11(3), pp. 106-118. https://doi.org/10.30897/ijegeo.1479116 (23 Dec. 2024).
MLA
Tuğaç, Murat Güven, and Fatih Fehmi Şimşek and Harun Torunlar. “Classification of Agricultural Crops with Random Forest and Support Vector Machine Algorithms Using Sentinel-2 and Landsat-8 Images.” International Journal of Environment and Geoinformatics, vol. 11, no. 3, 2024, pp. 106-118. [Database Container], https://doi.org/10.30897/ijegeo.1479116
Vancouver
Tuğaç MG, Şimşek FF, Torunlar H. Classification of Agricultural Crops with Random Forest and Support Vector Machine Algorithms Using Sentinel-2 and Landsat-8 Images. International Journal of Environment and Geoinformatics [Internet]. 23 Dec. 2024 [cited 23 Dec. 2024];11(3):106-118. Available from: https://doi.org/10.30897/ijegeo.1479116 doi: 10.30897/ijegeo.1479116
ISNAD
Tuğaç, MuratGüven - Şimşek, FatihFehmi - Torunlar, Harun. “Classification of Agricultural Crops with Random Forest and Support Vector Machine Algorithms Using Sentinel-2 and Landsat-8 Images”. International Journal of Environment and Geoinformatics 11/3 (Dec. 2024): 106-118. https://doi.org/10.30897/ijegeo.1479116