Comparison between Parametric and Non-Parametric Supervised Land Cover Classifications of Sentinel-2 MSI and Landsat-8 OLI Data

Giuseppe Mancino (), Antonio Falciano, Rodolfo Console and Maria Lucia Trivigno
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Giuseppe Mancino: Centro di Geomorfologia Integrata per l’Area del Mediterraneo (CGIAM), Via F. Baracca 175, 85100 Potenza, Italy
Antonio Falciano: Centro di Geomorfologia Integrata per l’Area del Mediterraneo (CGIAM), Via F. Baracca 175, 85100 Potenza, Italy
Rodolfo Console: Centro di Geomorfologia Integrata per l’Area del Mediterraneo (CGIAM), Via F. Baracca 175, 85100 Potenza, Italy
Maria Lucia Trivigno: Centro di Geomorfologia Integrata per l’Area del Mediterraneo (CGIAM), Via F. Baracca 175, 85100 Potenza, Italy

Geographies, 2023, vol. 3, issue 1, 1-28

Abstract: The present research aims at verifying whether there are significant differences between Land Use/Land Cover (LULC) classifications performed using Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) data—abbreviated as L8 and S2. To comprehend the degree of accuracy between these classifications, both L8 and S2 scenes covering the study area located in the Basilicata region (Italy) and acquired within a couple of days in August 2017 were considered. Both images were geometrically and atmospherically corrected and then resampled at 30 m. To identify the ground truth for training and validation, a LULC map and a forest map realized by the Basilicata region were used as references. Then, each point was verified through photo-interpretation using the orthophoto AGEA 2017 (spatial resolution of 20 cm) as a ground truth image and, only in doubtful cases, a direct GPS field survey. MLC and SVM supervised classifications were applied to both types of images and an error matrix was computed using the same reference points (ground truth) to evaluate the classification accuracy of different LULC classes. The contribution of S2′s red-edge bands in improving classifications was also verified. Definitively, ML classifications show better performance than SVM, and Landsat data provide higher accuracy than Sentinel-2.

Keywords: Landsat 8 OLI; Sentinel-2; mapping; Land Use/Land Cover classification; Maximum Likelihood Classification; Support Vector Machine (search for similar items in EconPapers)
JEL-codes: Q1 Q15 Q5 Q53 Q54 Q56 Q57 (search for similar items in EconPapers)
Date: 2023
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