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dcterms:title "Model code of peatland distribution and predicted peat thickness in lowland Amazonian Peru" ;
dcterms:identifier "https://catalogue.ceh.ac.uk/id/e337de58-df5e-4412-8aef-28875870f965","https://doi.org/10.5285/e337de58-df5e-4412-8aef-28875870f965" ;
dcterms:bibliographicCitation "Hastie, A., Honorio Coronado, E.H., Reyna Huaymacari, J., Mitchard, E.T., Åkesson, C., Baker, T.R., Cole, L.E.S., Cordova Oroche, C.J., Dargie, G.C., Dávila, N., De Grandi, E.C., del Aguila-Pasquel, J., Del Castillo Torres, D., De la Cruz Paiva, R., Draper, F.C., Flores, G., Grandez Ríos, J., Hergoualc’h, K., Householder, J.E., Janovec, J.P., Lähteenoja, O., Reyna, D., Rodríguez-Veiga, P., Roucoux, K.H., Tobler, M., Wheeler, C.E., Williams, M., Lawson, I.T. (2024). Model code of peatland distribution and predicted peat thickness in lowland Amazonian Peru. NERC EDS Environmental Information Data Centre. https://doi.org/10.5285/e337de58-df5e-4412-8aef-28875870f965" ;
dcterms:description "Two scripts for classifying remotely sensed data used to produce maps of peatland distribution and predicted peat thickness, using random forest classification and regression. Written in JavaScript for use with Google Earth Engine. These are versions of the scripts used in Hastie et al. (2022), https://doi.org/10.1038/s41561-022-00923-4. Users should also cite Rodríguez-Veiga et al. (2020), https://doi.org/10.3390/rs12152380 ." ;
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