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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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Abstract

The 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.

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Data availability

Publicly available Landsat-9 and Landsat-8 satellite images that can be downloaded from the USGS website (https://earthexplorer.usgs.gov/) were used in this study.

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Acknowledgements

The authors greatly acknowledge the reviewer(s) for the constructive comments that improved the quality of this work.

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Authors and Affiliations

  1. Department of Geomatic Engineering, Artvin Coruh University, 08100, Artvin, Turkey

    Ekrem Saralioglu

  2. Department of Forest Engineering, Artvin Coruh University, 08100, Artvin, Turkey

    Can Vatandaslar

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  1. Ekrem Saralioglu
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  2. Can Vatandaslar
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Contributions

Classification, accuracy assessment and final maps were produced by Ekrem Saralioglu. Provision and preparation of the training data to be used for classification and the creation of the study area map were made by Can Vatandaslar. Both authors contributed to the writing and review of the article.

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Correspondence to Ekrem Saralioglu.

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Saralioglu, E., Vatandaslar, C. 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 Geod Geophys 57, 695–716 (2022). https://doi.org/10.1007/s40328-022-00400-9

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