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Nuno Silva

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Proceedings Article | 12 September 2021 Presentation + Paper

Land cover mapping at national scale with Sentinel-2 and LUCAS: a case study in Portugal

Pedro Benevides, Nuno Silva, Hugo Costa, Francisco Moreira, Daniel Moraes, Mauro Castelli, Mário Caetano

Proceedings Volume 11856, 1185606 (2021) https://doi.org/10.1117/12.2598789

KEYWORDS: Nomenclature, Composites, Agriculture, Image classification, Remote sensing, Machine learning, Satellites, Satellite imaging, Vegetation, Orthophoto maps

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Experiments were carried out to investigate the use of Land Use and Coverage Area frame Survey (LUCAS) dataset and Sentinel-2 imagery to produce a land cover map in Portugal through automated supervised classification. LUCAS is a free land cover land use (LCLU) dataset based in Europe, while Sentinel-2 satellites provide also free images with short revisit frequency. The goal was to evaluate if LUCAS dataset from 2018 can be used as a single reference dataset for land cover classification at national level. The Random Forest (RF) algorithm was used. Some processing steps were undertaken to use LUCAS as reference dataset. The original LUCAS LCLU nomenclature was modified into a new nomenclature composed of 12 and 6 level-2 and level-1 map classes, respectively. Filtering was performed on LUCAS metadata, reducing the initial number of LUCAS points over Portugal from 7168 to 4910. Monthly composites of Sentinel-2 images acquired between October 2017 and September 2018 were used. To reduce the imbalance in LUCAS training points, an oversampling technique based on Synthetic Minority Over-Sampling Technique (SMOTE) was used. An independent validation dataset was produced with 600 points. RF shows an overall accuracy (OA) of 57% for level-2 and 72% for level-1 nomenclatures. When using the oversampling technique, the OA accuracy increases by 3% for level2 and 2% for level-1. The preliminary results of this experiment show that LUCAS dataset used in supervised machine learning classification has potential to produce a reliable land cover map at national scale.

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