Supplemental material
Articles
Fusing Landsat and SAR Data for Mapping Tropical Deforestation through Machine Learning Classification and the PVts-β Non-Seasonal Detection Approach
Fusion des données Landsat et RSO pour la cartographie de la déforestation tropicale grâce à la classification par apprentissage automatique et à l’approche de détection non saisonnière PVts-β
Yonatan Tarazonaa Department of Signal Theory and Communications, School of Telecommunication Engineering, Universitat Politecnica de Catalunya, 08034Barcelona, Spain;f American Program in GIS and Remote Sensing, California15112, PeruCorrespondence[email protected]
https://orcid.org/0000-0002-5208-1004
https://orcid.org/0000-0002-5208-1004, Alaitz Zabalab Grumets Research Group, Department Geografia, Edifici B, Universitat Autònoma de Barcelona, Bellaterra, 08193Catalonia, Spainhttps://orcid.org/0000-0002-3931-4221
https://orcid.org/0000-0002-3931-4221, Xavier Ponsb Grumets Research Group, Department Geografia, Edifici B, Universitat Autònoma de Barcelona, Bellaterra, 08193Catalonia, Spainhttps://orcid.org/0000-0002-6924-1641
https://orcid.org/0000-0002-6924-1641, Antoni Broquetasa Department of Signal Theory and Communications, School of Telecommunication Engineering, Universitat Politecnica de Catalunya, 08034Barcelona, Spainhttps://orcid.org/0000-0001-9801-9145
https://orcid.org/0000-0001-9801-9145, Jakub Nowosadc Institute of Geoecology and Geoinformation, Adam Mickiewicz University, Krygowskiego 10, 61-680, Poznan, Polandhttps://orcid.org/0000-0002-1057-3721
https://orcid.org/0000-0002-1057-3721 & Hamdi A. Zurqanid Department of Forestry and Environmental Conservation, Clemson University, 261, Lehotsky Hall Box, Clemson340317, SC, USA;e University of Arkansas Agricultural Experiment Station, Arkansas Forest Resources Center, University of Arkansas at Monticello, Monticello, AR71655, USAhttps://orcid.org/0000-0002-9250-4534
https://orcid.org/0000-0002-9250-4534