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Elevation gradients provide opportunities to explore environmental controls on forest structure and functioning. We used airborne imaging spectroscopy and lidar (light detection and ranging) to quantify changes in three-dimensional forest structure and canopy functional traits in twenty 25 ha landscapes distributed along a 3300 m elevation gradient from lowland Amazonia to treeline in the Peruvian Andes. Elevation was positively correlated with lidar-estimated canopy gap density and understory vegetation cover, and negatively related to canopy height and the vertical partitioning of vegetation in canopies. Increases in canopy gap density were tightly linked to increases in understory plant cover, and larger gaps (20–200 m2) produced 25–30 times the response in understory cover than did smaller gaps (< 5 m2). Vegetation NDVI and photosynthetic fractional cover decreased, while exposed non-photosynthetic vegetation and bare soil increased, with elevation. Scaling of gap size to gap frequency (λ) was, however, nearly constant along the elevation gradient. When combined with other canopy structural and functional trait information, this suggests near-constant canopy turnover rates from the lowlands to treeline, which occurs independent of decreasing biomass or productivity with increasing elevation. Our results provide the first landscape-scale quantification of forest structure and canopy functional traits with changing elevation, thereby improving our understanding of disturbance, demography and ecosystem processes in the Andes-to-Amazon corridor.

High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or light detection and ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high-resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (> 40%) of the Colombian Amazon – a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon maps have 14% uncertainty at 1 ha resolution, and the regional map based on stratification has 28% uncertainty in any given hectare. High-resolution approaches with quantifiable pixel-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.