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Background: Dry deciduous forest (DDF) is distributed throughout the monsoon area of South-east Asia. Leaf morphological and photosynthetic traits are essential for adaptation to the environmental variability and usually change with tree height and season. Aims: To clarify the differences in leaf traits as influenced by tree height, light conditions and season for two dominant dipterocarps of DDF:Dipterocarpus tuberculatusandShorea obtusa. Methods: We measured changes in leaf traits with tree height, namely leaf mass per area (LMA), maximum photosynthetic rate (Amax), stomatal conductance (gs) and nitrogen concentration (N), in the rainy and early and mid-dry seasons. To identify the effects of height and light intensity on leaf traits, we conducted multiple regression analyses using leaf traits as the dependent variables and the height, light and season as independent variables. Results: Tree height was more important for gradients in most leaf traits, especially in LMA in both species, than light intensity. Tall canopy tree leaves had greater LMA,Amaxand N than shorter tree leaves in the rainy and early dry seasons, althoughgswas almost constant. The lowestAmaxandgswere recorded in immature leaves, sampled in the mid-dry season, at all tree heights. Conclusions: The two dipterocarp species achieved highAmaxby having highgs, LMA and N in a high-light canopy environment, whereas larger, thinner leaves with low respiratory costs may contribute to efficient light capture under low-light conditions in the understorey. [ABSTRACT FROM PUBLISHER]

We developed an empirical model to estimate aboveground carbon density with variables derived from airborne Light Detection and Ranging (LiDAR) in tropical seasonal forests in Cambodia, and assessed the effects of LiDAR pulse density on the accurate estimation of aboveground carbon density. First, we tested the applicability of variables used for estimating aboveground carbon density with the original LiDAR pulse density data (26 pulse m). Aboveground carbon density was regressed against variables derived from airborne LiDAR. Three individual height variable models were developed along with a canopy density model, and three other models combined canopy height and canopy density variables. The influence of forest type on model accuracy was also assessed. Next, the relationship between pulse density and estimation accuracy was investigated using the best regression model. The accuracy of the models were compared based on seven LiDAR point densities consisting of 0.25, 1, 2, 3, 4, 5 and 10 pulse m. The best model was obtained using the single mean canopy height (MCH) model ( R = 0.92) with the original pulse density data. The relationship between MCH and aboveground carbon density was found to be consistent under different forest types. The differences between predicted and measured residual mean of squares of deviations were less than 1.5 Mg C ha between each pulse density. We concluded that aboveground carbon density can be estimated using MCH derived from airborne LiDAR in tropical seasonal forests in Cambodia even with a low pulse density of 0.25 pulse m without stratifying the study area based on forest type. [ABSTRACT FROM AUTHOR]