Accurate estimates of forest canopy are essential for the characterization of forest ecosystems. Remotely-sensed techniques provide a unique way to obtain estimates over spatially extensive areas, but theirapplication is limited by the spectral and temporal resolution available from these systems, which isoften not suited to meet regional or local objectives. The use of unmanned aerial vehicles (UAV) as remotesensing platforms has recently gained increasing attention, but their applications in forestry are still atan experimental stage. In this study we described a methodology to obtain rapid and reliable estimates offorest canopy from a small UAV equipped with a commercial RGB camera. The red, green and blue digitalnumbers were converted to the green leaf algorithm (GLA) and to the CIE L∗a∗b∗colour space to obtainestimates of canopy cover, foliage clumping and leaf area index (L) from aerial images. Canopy attributeswere compared with in situ estimates obtained from two digital canopy photographic techniques (coverand fisheye photography).The method was tested in beech forests. UAV images accurately quantified canopy cover even in verydense stand conditions, despite a tendency to not detecting small within-crown gaps in aerial images,leading to a measurement of a quantity much closer to crown cover estimated from in situ cover photog-raphy. Estimates of L from UAV images significantly agreed with that obtained from fisheye images, butthe accuracy of UAV estimates is influenced by the appropriate assumption of leaf angle distribution.We concluded that true colour UAV images can be effectively used to obtain rapid, cheap and mean-ingful estimates of forest canopy attributes at medium-large scales. UAV can combine the advantage ofhigh resolution imagery with quick turnaround series, being therefore suitable for routine forest standmonitoring and real-time applications.