PreprintArticleVersion 2Preserved in Portico This version is not peer-reviewed
Comparison of pixel-based classification algorithms using Landsat-8 OLI and Sentinel-2 MSI for land use/land cover mapping in a heterogeneous landscape
Version 1
: Received: 14 July 2023 / Approved: 14 July 2023 / Online: 17 July 2023 (09:23:49 CEST)
Version 2
: Received: 10 August 2023 / Approved: 11 August 2023 / Online: 14 August 2023 (09:01:24 CEST)
How to cite:
Vogiatzis, M.; Eleftheriadis, I. Comparison of pixel-based classification algorithms using Landsat-8 OLI and Sentinel-2 MSI for land use/land cover mapping in a heterogeneous landscape. Preprints2023, 2023071043. https://doi.org/10.20944/preprints202307.1043.v2
Vogiatzis, M.; Eleftheriadis, I. Comparison of pixel-based classification algorithms using Landsat-8 OLI and Sentinel-2 MSI for land use/land cover mapping in a heterogeneous landscape. Preprints 2023, 2023071043. https://doi.org/10.20944/preprints202307.1043.v2
Vogiatzis, M.; Eleftheriadis, I. Comparison of pixel-based classification algorithms using Landsat-8 OLI and Sentinel-2 MSI for land use/land cover mapping in a heterogeneous landscape. Preprints2023, 2023071043. https://doi.org/10.20944/preprints202307.1043.v2
APA Style
Vogiatzis, M., & Eleftheriadis, I. (2023). Comparison of pixel-based classification algorithms using Landsat-8 OLI and Sentinel-2 MSI for land use/land cover mapping in a heterogeneous landscape. Preprints. https://doi.org/10.20944/preprints202307.1043.v2
Chicago/Turabian Style
Vogiatzis, M. and Ioannis Eleftheriadis. 2023 "Comparison of pixel-based classification algorithms using Landsat-8 OLI and Sentinel-2 MSI for land use/land cover mapping in a heterogeneous landscape" Preprints. https://doi.org/10.20944/preprints202307.1043.v2
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 combi-nations 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 Copernicus Land Cover (CLC2018) nomenclature. In total, eight land use/land cover classes were identified in Landsat-8 OLI and nine in Sentinel-2a MSI for an acceptable overall accuracy over 85%. A comparison of the overall classification accuracies shows that Sentinel-2a 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 inte-grating time-series multispectral data of different sensors and geospatial ancillary data for land use/land cover mapping.
Keywords
Image classification; Land use/land cover mapping; Accuracy assessment; Landsat-8; Snetinel-2
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
