Natural disturbances are the dominant form of forest regeneration and dynamics in unmanaged tropical forests. Monitoring the size distribution of treefall gaps is important to better understand and predict the carbon budget in response to land use and other global changes. In this study, we model the size frequency distribution of natural canopy gaps with a discrete power law distribution. We use a Bayesian framework to introduce and test, using Monte Carlo Markov Chain and Kuo-Mallick algorithms, the effect of local physical environment on gap size distribution. We apply our methodological framework to an original Light Detecting and Ranging dataset in which natural forest gaps were delineated over 30000 ha of unmanaged forest. We highlight strong links between gap size distribution and environment, primarily hydrological conditions and topography, with large gaps being more frequent in floodplains and on hillslopes. In the future, we plan scale up testing of our methodology using satellite data. Additionally, although gap size distribution variation is clearly under environmental control, gap process variation over time should be tested against climate variability. (Résumé d'auteur

Canopy height is a key variable in tropical forest functioning and for regional carbon inventories. We investigate the spatial structure of the canopy height of a tropical forest, its relationship with environmental physical covariates, and the implication for tropical forest height variation mapping. Making use of high-resolution maps of LiDAR-derived Digital Canopy Model (DCM) and environmental covariates from a Digital Elevation Model (DEM) acquired over 30,000 ha of tropical forest in French Guiana, we first show that forest canopy height is spatially correlated up to 2500 m. Forest canopy height is significantly associated with environmental variables, but the degree of correlation varies strongly with pixel resolution. On the whole, bottomland forests generally have lower canopy heights than hillslope or hilltop forests. However, this global picture is very noisy at local scale likely because of the endogenous gap-phase forest dynamic processes. Forest canopy height has been predictively mapped across a pixel resolution going from 6 m to 384 m mimicking a low resolution case of 3 points·km −2 . Results of canopy height mapping indicated that the error for spatial model with environment effects decrease from 8.7 m to 0.91 m, depending of the pixel resolution. Results suggest that, outside the calibration plots, the contribution of environment in shaping the global canopy height distribution is quite limited. This prevents accurate canopy height mapping based only on environmental information, and suggests that precise canopy height maps, for local management purposes, can only be obtained with direct LiDAR monitoring.