The spatial distribution of plant diversity and biomass informs management decisions to maintain biodiversity and carbon stocks in tropical forests. Optical remotely sensed data is often used for supporting such activities; however, it is difficult to estimate these variables in areas of high biomass. New technologies, such as airborne LiDAR, have been used to overcome such limitations. LiDAR has been increasingly used to map carbon stocks in tropical forests, but has rarely been used to estimate plant species diversity. In this study, we first evaluated the effect of using different plot sizes and plot designs on improving the prediction accuracy of species richness and biomass from LiDAR metrics using multiple linear regression. Second, we developed a general model to predict species richness and biomass from LiDAR metrics for two different types of tropical dry forest using regression analysis. Third, we evaluated the relative roles of vegetation structure and habitat heterogeneity in explaining the observed patterns of biodiversity and biomass, using variation partition analysis and LiDAR metrics. The results showed that with increasing plot size, there is an increase of the accuracy of biomass estimations. In contrast, for species richness, the inclusion of different habitat conditions (cluster of four plots over an area of 1.0 ha) provides better estimations. We also show that models of plant diversity and biomass can be derived from small footprint LiDAR at both local and regional scales. Finally, we found that a large portion of the variation in species richness can be exclusively attributed to habitat heterogeneity, while biomass was mainly explained by vegetation structure.

a b s t r a c tThe objective of this study was to determine whether leaf area index (LAI) can be accurately estimated in intensively managed pine plantations using multiple-return airborne laser scanner (lidar) data. In situ measurements of LAI were made using the LiCor LAI-2000 Plant Canopy Analyzer on 109 plots under a variety of stand conditions (i.e., stand age, nutritional regime, and stem density) in North Carolina and Virginia, USA in late summer, 2008. Distributional metrics were calculated for each plot using small footprint lidar data (average pulse density 5 pulses per square meter; up to four returns per pulse) acquired in the month preceding the field measurements. Distributional metrics were calculated for each plot using all vegetation returns, as well as using ten 1 m deep crown density slices (a new technique introduced in this study), five above and five below the mode of the vegetation returns for each plot. These metrics were used as independent variables in best subsets regressions with LAI (measured in situ) as the dependent variable. The best resulting models had an R 2 ranging from 0.61 (for a 2-variable model) to 0.83 (for a 6-variable model). The laser penetration index (LPI) was an important variable regardless of the number of variables used. Other important variables included the mean intensity value, the mean and 20th percentile of the vegetation returns, and various crown density slice metrics. These results indicate that LAI can be estimated accurately using lidar data in intensively managed pine plantations over a wide variety of stand conditions.Published by Elsevier B.V.

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Leaf area is an important forest structural variable which serves as the primary means of mass and energy exchange within vegetated ecosystems. The objective of the current study was to determine if leaf area index (LAI) could be estimated accurately and consistently in five intensively managed pine plantation forests using two multiple-return airborne LiDAR datasets. Field measurements of LAI were made using the LiCOR LAI2000 and LAI2200 instruments within 116 plots were established of varying size and within a variety of stand conditions (i.e. stand age, nutrient regime and stem density) in North Carolina and Virginia in 2008 and 2013. A number of common LiDAR return height and intensity distribution metrics were calculated (e.g. average return height), in addition to ten indices, with two additional variants, utilized in the surrounding literature which have been used to estimate LAI and fractional cover, were calculated from return heights and intensity, for each plot extent. Each of the indices were assessed for correlation with each other, and were used as independent variables in linear regression analysis with field LAI as the dependent variable. All LiDAR derived metrics were also entered into a forward stepwise linear regression. The results from each of the indices varied from an R 2 of 0.33 (S.E. 0.87) to 0.89 (S.E. 0.36). Those indices calculated using ratios of all

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