Quantitative Evaluation of Bitterlich Sampling for Estimating Basal Area in Sparse Boreal Forests and Dense Tropical Forests

Bitterlich sampling is an extensively used technique in worldwide forest inventories. Although it has been proved that estimates of basal area from Bitterlich sampling are mathematically unbiased, its precision for individual forest stands may be fairly poor. An extension of validation efforts to different forest biomes could therefore provide more comprehensive assessment and understanding of the Bitterlich sampling technique. In this study, this technique was quantitatively evaluated by using simulated sparse boreal forests and dense tropical forests from an empirical forest structure model (EFSM). Theoretical estimation of basal areas and practical estimation influenced by the hidden-tree effect were both compared with true basal areas of the simulated forests. The evaluation results indicated that: 1) Bitterlich sampling can yield acceptable accuracy and precision when the count number (CN) of trees was set to 10 for the studied boreal and tropical forests with distinct characteristics, 2) the theoretical estimation of basal area can be improved by increasing the CN values for both forests, and 3) when the hidden-tree effect is encountered, the accuracy for tropical forests will be decreased by increasing the CN values, whereas the accuracy for boreal forests can still be improved. Accordingly, a relatively high CN, at a reasonable cost, is recommended for sparse boreal forests to improve the accuracy of basal area estimation. In contrast, for dense tropical forests, a CN of ten is appropriate to mitigate the hidden-tree effect.

Modeling three-dimensional forest structures to drive canopy radiative transfer simulations of bidirectional reflectance factor

Three-dimensional(3-D)Monte Carlo-based radiative transfer(MCRT)models are usually used for benchmarking in intercomparisons of the canopy radiative transfer(RT)simulations.However,the 3-D MCRT models are rarely applied to develop remote sensing algorithms to estimate essential climate variables of forests,due mainly to the difficulties in obtaining realistic stand structures for different forest biomes over regional to global scales.Fortunately,some of important tree structure parameters such as canopy height and tree density distribution have been available globally.This enables to run the intermediate complexities of the 3-D MCRT models.We consequently developed a statistical approach to generate forest structures with intermediate complexities depending on the inputs of canopy height and tree density.It aims at facilitating applications of the 3-D MCRT models to develop remote sensing retrieval algorithms.The proposed approach was evaluated using field measurements of two boreal forest stands at Estonia and USA,respectively.Results demonstrated that the simulations of bidirectional reflectance factor(BRF)based on the measured forest structures agreed well with the BRF based on the generated structures from the proposed approach with the root mean square error(RMSE)and relative RMSE(rRMSE)ranging from 0.002 to 0.006 and from 0.7%to 19.8%,respectively.Comparison of the computed BRF with corresponding MODIS reflectance data yielded RMSE and rRMSE lower than 0.03 and 20%,respectively.Although the results from the current study are limited in two boreal forest stands,our approach has the potential to generate stand structures for different forest biomes.