@inproceedings{88e3da2a0d4b45069b35612b295a8c6d,
title = "Land cover mapping at national scale with Sentinel-2 and LUCAS: a case study in Portugal",
abstract = "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.",
keywords = "Land Cover, LUCAS survey, National mapping, Oversampling, Random Forest, Sentinel-2",
author = "Benevides, {Pedro Jos{\'e}} and Nuno Silva and Hugo Costa and Moreira, {Francisco D.} and Daniel Moraes and Mauro Castelli and M{\'a}rio Caetano",
note = "info:eu-repo/grantAgreement/FCT/3599-PPCDT/PCIF%2FSSI%2F0102%2F2017/PT# info:eu-repo/grantAgreement/FCT/3599-PPCDT/PCIF%2FMOS%2F0046%2F2017/PT# Benevides, P. J., Silva, N., Costa, H., Moreira, F. D., Moraes, D., Castelli, M., & Caetano, M. (2021). Land cover mapping at national scale with Sentinel-2 and LUCAS: a case study in Portugal. In C. M. U. Neale, & A. Maltese (Eds.), Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII (Vol. 11856). [1185606] (Proceedings of SPIE). SPIE-International Society for Optical Engineering. https://doi.org/10.1117/12.2598789--------------------------------------This work has been supported by project SCAPEFIRE (PCIF/MOS/0046/2017), project foRESTER (PCIF/SSI/0102/2017), and by Centro de Investiga{\c c}{\~a}o em Gest{\~a}o de Informa{\c c}{\~a}o (MagIC), all funded by the Portuguese Foundation for Science and Technology (FCT). Value-added data processed by CNES for the Theia data centre www.theia-land.fr using Copernicus products. The satellite image pre-processing uses algorithms developed by Theia's Scientific Expertise Centers.; Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII ; Conference date: 13-09-2021 Through 18-09-2021",
year = "2021",
month = sep,
day = "12",
doi = "10.1117/12.2598789",
language = "English",
isbn = "9781510645561",
volume = "11856",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE-International Society for Optical Engineering",
editor = "Neale, {Christopher M. U.} and Antonino Maltese",
booktitle = "Remote Sensing for Agriculture, Ecosystems, and Hydrology XXIII",
}
