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- W4384500538 abstract "Satellite-based data classification performance remains a challenge for research community in the field of land use/land cover mapping. Here we investigated supervised per-pixel classifications performance under different scenarios, based on single and seasonal multispectral data combina-tions of different sensors (Landsat-8 OLI and Sentinel-2 MSI). In case of Landsat, seasonal spectral indices (EVI and NDMI) were included. A typical Mediterranean watershed with a complex landscape comprised of various forest and wetland ecosystems, crops, artificial surfaces, and lake water was selected to test our approach. All available geospatial data from national databases (Forest Map, LPIS, Natura2000 habitats, cadastral parcels, etc.) are used as ancillary data for clas-sification training and validation. We examined and compared the performance of ML, RF, KNN and SVM classifiers under different scenarios for land use/land cover mapping, according to Co-pernicus Land Cover nomenclature. In total, eight land use/land cover classes were identified in Landsat-8 OLI and nine in Sentinel-2 MSI for an acceptable overall accuracy over 85%. A com-parison of the overall classification accuracies shows that Sentinel-2 overall accuracy was slightly higher than Landsat-8 (96.68% vs. 93.02%). Respectively, the best-performed algorithm was ML in Sentinel-2 while in Landsat-8 was KNN. However, machine-learning algorithms have similar results regardless the type of sensor. We concluded that best classification performances achieved using seasonal multispectral data. Future research should be oriented towards integrating time-series multispectral data of different sensors and geospatial ancillary data for land use/land cover mapping." @default.
- W4384500538 created "2023-07-18" @default.
- W4384500538 creator A5057308975 @default.
- W4384500538 creator A5062301336 @default.
- W4384500538 date "2023-07-17" @default.
- W4384500538 modified "2023-09-27" @default.
- W4384500538 title "Comparison of Pixel-Based Classification Algorithms Using Landsat-8 OLI and Sentinel-2 MSI for Land Use/Land Cover Mapping in a Heterogeneous Landscape" @default.
- W4384500538 doi "https://doi.org/10.20944/preprints202307.1043.v1" @default.
- W4384500538 hasPublicationYear "2023" @default.
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