remote sensing Article Potential of Different Optical and SAR Data in Forest and Land Cover Classification to Support REDD+ MRV Laura Sirro 1,*, Tuomas Häme 1, Yrjö Rauste 1, Jorma Kilpi 1 ID , Jarno Hämäläinen 2, Katja Gunia 2, Bernardus de Jong 3 and Fernando Paz Pellat 4 1 VTT Technical Research Centre of Finland Ltd., P.O. Box 1000, VTT FI-02044, Finland; tuomas.hame@vtt.fi (T.H.); yrjo.rauste@vtt.fi (Y.R.); jorma.kilpi@vtt.fi (J.K.) 2 Arbonaut Ltd., Kaislakatu 2, Joensuu FI-80130, Finland;
[email protected] (J.H.);
[email protected] (K.G.) 3 El Colegio de la Frontera Sur, Av. Rancho Polígono 2-A, Parque Industrial Lerma, Campeche, Campeche CP 24500, Mexico;
[email protected] 4 Colegio de Postgraduados, Km 36.5 Carretera México-Texcoco, Texcoco 56230, Mexico;
[email protected] * Correspondence: laura.sirro@vtt.fi; Tel.: +358-40-538-7769 Received: 9 May 2018; Accepted: 11 June 2018; Published: 14 June 2018 Abstract: The applicability of optical and synthetic aperture radar (SAR) data for land cover classification to support REDD+ (Reducing Emissions from Deforestation and Forest Degradation) MRV (measuring, reporting and verification) services was tested on a tropical to sub-tropical test site. The 100 km by 100 km test site was situated in the State of Chiapas in Mexico. Land cover classifications were computed using RapidEye and Landsat TM optical satellite images and ALOS PALSAR L-band and Envisat ASAR C-band images. Identical sample plot data from Kompsat-2 imagery of one-metre spatial resolution were used for the accuracy assessment.