Remote sensing and landscape modelling improve forest classification. One approach combines variables based on forest phenology and climate to characterisefunctional rather than structural or compositional characteristics of ecosystems (phenoclusters). However, there are few studies about the correlation between these new modelling approaches and forest classifications based on ground surveys. Our objective was to define the conservation value of different functional forests, based on phenocluster categories, for Nothofagus antarctica forests in Tierra del Fuego. We used different available features model outputs standardised and homogenised at 90-m spatial resolution (phenoclusters, ecosystem services, potential biodiversity), and ground truthdata from 145 stands (soil characteristics, forest structure, animal stocking rate, understory biodiversity). The phenocluster categories were compared using uni- and multivariate analyses. The use of phenocluster categories allowed sorting of the N. antarctica forest type into contrasting subtypes with different characteristics, including (i) cultural, regulating, and provisioning ecosystem services and potential biodiversity at landscape level (F = 1.8-87.6), (ii) soil organic carbon, nitrogen, and phosphorous properties (F = 4.2-5.2), (iii) tree dominant height, overstory crown cover, basal area, and bark volume forest structure (F = 0.1-6.3), animal stock (F = 1.0-1.9), and (iv) understory plant richness (F = 1.0-9.4) at stand level. Significant differences were detected in the multivariate analyses (classifications and ordinations) supporting the split of this forest type into four functional forest subtypes: (i) coastal forests near the Atlantic Ocean, (ii) highland forests close to the steppe, (iii) ecotone areas associated with N. pumilioforests, and (iv) degraded and secondary forests. The cyclic and seasonal greenness information provided by the phenoclusters were directly related to plant understory diversity, where functional rather than structural or compositional characteristics of forest ecosystems were the main explanatory variable. Our findings can support better management and conservation proposals, e.g. different management strategies for each phenocluster category, or selection of representative forests into a reserve network design based on phenoclusters rather than forest types defined by tree canopy-cover composition.