Abstract: Optical remote sensing (ORS) is the primary tool to obtain information on regionalscale vegetation cover. Few efforts have been made to identify forest types within subtropical evergreen forests using ORS. Scale effects have been reported in literature, yet the optimal spatial resolution to identify evergreen forest types is still unclear in practical application. In this study, we used four types of ORS imagery \[Pléiades (2 m), RapidEye (5 m), and Landsat-8 (15 m and 30 m)\] to investigate whether the classification of three typical evergreen forest types \[Chinese fir forest (CFF), Masson pine forest (MPF), and evergreen broadleaved forest (EBF)\] in subtropical landscapes would be influenced by scale effects. Moreover, we tested the optimal spatial resolution for forest classification. The Random Forest Model was combined with predictive features derived from spectral reflectance, image texture, and vegetation coverage to map landcover types at four spatial resolutions. The results showed that the overall accuracy (OA) of four land-cover maps had a U-curve tendency with increasing spatial resolution. The 2 m Pléiades image generated the highest classification accuracy (Kappa=0.70, OA=0.77) among four types of images. The accuracy of three evergreen forest types also had a similar U-curve tendency. The ranges of the rate of identification (RI) were RI_CFF=68%-87%, RI_MPF=55%-84%, and RI_EBF=29%-74%. The CFF and MPF generated lower classification errors in terms of omission error (OE_CFF=0.26-0.46; OE_MPF=0.31-0.50) and commission error (CE_CFF=0.32-0.53; CE_MPF=0.31-0.46)compared with the EBF (CE_EBF=0.39-0.66; OE_EBF=0.47-0.71). Our results showed that the identification of forest types in subtropical regions is clearly subject to scale effects. Despite this, Landsat-8 imagery at 30 m resolution can produce the highest mapping precision due to its broader spectrum sensors. We proposed that the practical mapping of forest types in subtropical areas should consider the inherent trade-off between spectral features and spatial resolution of remote sensors rather than blindly pursuing high spatial resolution.

Key words: methanotrophic bacterial community, vegetation, diversity, Gongga Mountain.