Land use/land cover classification with Landsat-8 and Landsat-9 satellite images: a comparative analysis between forest- and agriculture-dominated landscapes using different machine learning methods

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dc.authoridEkrem Saralioğlu / 0000-0002-0609-3338en_US
dc.authoridCan Vatandaşlar / 0000-0001-5552-5670en_US
dc.contributor.authorSaralioğlu, Ekrem
dc.contributor.authorVatandaşlar, Can
dc.date.accessioned2022-12-26T11:21:12Z
dc.date.available2022-12-26T11:21:12Z
dc.date.issued2022
dc.departmentAÇÜ, Mühendislik Fakültesi, Harita Mühendisliği Bölümüen_US
dc.description.abstractThe Landsat program, which started in 1972 with Landsat-1, continues today with its newest satellite, Landsat-9, launched on 27 October 2021. The Landsat-9 data have been freely distributed since 10 February 2022 on the Earth Explorer platform. However, no scientific study on Landsat-9 for land use/land cover (LULC) mapping has yet been published, focusing on specific eco-systems. Therefore, the present study investigates the potential of Landsat-9 images for LULC classification in forest and agricultural systems. To achieve this, we selected two study areas, i.e. Kaynarca (forest-dominated) and Hocalar (agriculture-dominated), from different ecoregions of Turkey. Then, we mapped their LULCs using Landsat-8 and Landsat-9 data with the Support Vector Machine, K-Nearest Neighbors (K-NN), Light Gradient Boosting Machine (LightGBM), and 3D Convolutional Neural Network (3D-CNN) methods. The classification accuracies were assessed with the F1-score, taking the stand-types maps of the case areas as reference. It was seen that the best maps were generated by the 3D-CNN method with accuracy rates of 88.0% for Kaynarca (Landsat-8) and 87.4% for Hocalar (Landsat-9) at the landscape level. Unlike other methods, 3D-CNN removed the “salt-and-pepper effect” on the maps providing better spatial structure for further analyses. Regardless of the satellite missions, the mapping accuracies for the “productive forest” and “agriculture” classes were?>?90% for Kaynarca and Hocalar, respectively. The comparative results suggest that Landsat-9 offers satisfactory LULC maps with similar classification accuracies as Landsat-8 and can be effectively used as a freely available remote sensing resource in monitoring and mapping forest- and agriculture-dominated landscapes.
dc.identifier.citationSaralioglu, E., & Vatandaslar, C. (2022). Land use/land cover classification with Landsat-8 and Landsat-9 satellite images: a comparative analysis between forest- and agriculture-dominated landscapes using different machine learning methods. Acta Geodaetica et Geophysica, 57(4), 695–716.en_US
dc.identifier.doi10.1007/s40328-022-00400-9
dc.identifier.endpage716en_US
dc.identifier.issue4en_US
dc.identifier.scopusqualityQ2
dc.identifier.startpage695en_US
dc.identifier.urihttps://doi.org/10.1007/s40328-022-00400-9
dc.identifier.urihttps://hdl.handle.net/11494/4328
dc.identifier.volume57en_US
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorSaralioğlu, Ekrem
dc.institutionauthorVatandaşlar, Can
dc.language.isoenen_US
dc.publisherAkademiai Kiado ZRten_US
dc.relation.ispartofActa Geodaetica et Geophysica
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subject3D-CNNen_US
dc.subjectComputer visionen_US
dc.subjectDeep learningen_US
dc.subjectNatural resource monitoringen_US
dc.subjectOperational Land Imager (OLI-2)en_US
dc.subjectSVMen_US
dc.titleLand use/land cover classification with Landsat-8 and Landsat-9 satellite images: a comparative analysis between forest- and agriculture-dominated landscapes using different machine learning methodsen_US
dc.typeArticle

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