Background: Digital hemispherical photography (DHP) is widely used to estimate the leaf area index (LAI) of forest plots due to its advantages of high efficiency and low cost. A crucial step in the LAI estimation of forest plots via DHP is choosing a sampling scheme. However, various sampling schemes involving DHP have been used for the LAI estimation of forest plots. To date, the impact of sampling schemes on LAI estimation from DHP has not been comprehensively investigated. Methods: In this study, 13 commonly used sampling schemes which belong to five sampling types (i.e. dispersed, square, cross, transect and circle) were adopted in the LAI estimation of five Larix principis-rupprechtii plots (25 m × 25 m). An additional sampling scheme (with a sample size of 89) was generated on the basis of all the sample points of the 13 sampling schemes. Three typical inversion models and four canopy element clumping index (Ω_e) algorithms were involved in the LAI estimation. The impacts of the sampling schemes on four variables, including gap fraction, Ω_e, effective plant area index (PAI_e) and LAI estimation from DHP were analysed. The LAI estimates obtained with different sampling schemes were then compared with those obtained from litter collection measurements. Results: Large differences were observed for all four variable estimates (i.e. gap fraction, Ω_e, PAI_e and LAI) under different sampling schemes. The differences in impact of sampling schemes on LAI estimation were not obvious for the three inversion models, if the four Ω_e algorithms, except for the traditional gap-size analysis algorithm were adopted in the estimation. The accuracy of LAI estimation was not always improved with an increase in sample size. Moreover, results indicated that with the appropriate inversion model, Ω_e algorithm and sampling scheme, the maximum estimation error of DHP-estimated LAI at elementary sampling unit can be less than 20%, which is required by the global climate observing system, except in forest plots with extremely large LAI values (~ > 6.0). However, obtaining an LAI from DHP with an estimation error lower than 5% is impossible regardless of which combination of inversion model, Ω_e algorithm and sampling scheme is used. Conclusion: The LAI estimation of L. principis-rupprechtii forests from DHP was largely affected by the sampling schemes adopted in the estimation. Thus, the sampling scheme should be seriously considered in the LAI estimation. One square and two transect sampling schemes (with sample sizes ranging from 3 to 9) were recommended to be used to estimate the LAI of L. principis-rupprechtii forests with the smallest mean relative error (MRE). By contrast, three cross and one dispersed sampling schemes were identified to provide LAI estimates with relatively large MREs.

Optical methods require model inversion to infer plant area index (PAI) and woody area index (WAI) of leaf-on and leaf-off forest canopy from gap fraction or radiation attenuation measurements. Several inversion models have been developed previously, however, a thorough comparison of those inversion models in obtaining the PAI and WAI of leaf-on and leaf-off forest canopy has not been conducted so far. In the present study, an explicit 3D forest scene series with different PAI, WAI, phenological periods, stand density, tree species composition, plant functional types, canopy element clumping index, and woody component clumping index was generated using 50 detailed 3D tree models. The explicit 3D forest scene series was then used to assess the performance of seven commonly used inversion models to estimate the PAI and WAI of the leaf-on and leaf-off forest canopy. The PAI and WAI estimated from the seven inversion models and simulated digital hemispherical photography images were compared with the true PAI and WAI of leaf-on and leaf-off forest scenes. Factors that contributed to the differences between the estimates of the seven inversion models were analyzed. Results show that both the factors of inversion model, canopy element and woody component projection functions, canopy element and woody component estimation algorithms, and segment size are contributed to the differences between the PAI and WAI estimated from the seven inversion models. There is no universally valid combination of inversion model, needle-to-shoot area ratio, canopy element and woody component clumping index estimation algorithm, and segment size that can accurately measure the PAI and WAI of all leaf-on and leaf-off forest canopies. The performance of the combinations of inversion model, needle-to-shoot area ratio, canopy element and woody component clumping index estimation algorithm, and segment size to estimate the PAI and WAI of leaf-on and leaf-off forest canopies is the function of the inversion model as well as the canopy element and woody component clumping index estimation algorithm, segment size, PAI, WAI, tree species composition, and plant functional types. The impact of canopy element and woody component projection function measurements on the PAI and WAI estimation of the leaf-on and leaf-off forest canopy can be reduced to a low level (<4%) by adopting appropriate inversion models.

OntoPlant is a virtual plant software package, which has been developed at Spatial Information Research Center of Fujian in Fuzhou University since 2002. The purpose of the software package is to provide an integrated software solution to realistic plant modelling, real-time scene rendering, growth simulation and applications at different scales from individual, stand (population, community) to landscape. Its key components are ParaTree, LSTree, VisForest, and ForestSIMA. The software package is implemented with VC++, OpenGL Graphics standard, while ForestSIMA is also based on ArcGIS ArcObjects developer kit. ParaTree and LSTree are core of OntoPlant. They are used to build individual plant model. ParaTree combines geometric modelling and parameterized modelling. The shape characteristic of tree organ is depicted with a group of parameters, the values of parameters can be edited by interactive graphical parameter curve component tool. This tool provides friendly interface and intuitive interactive means, namely, what you see is what you get. The ParaTree can simulate realistic 3D plants at any age or season by modifying the parameters, pruning the tree. It is adaptive to simulate the architecture of tree. It doesn't require users to have much knowledge of biology. While the opposite is true for LSTree. LSTree is based on the theory of L-systems, which is faithful to the plant morphology and the physiological growth process. This system makes it possible to model the development of plants with efficiency and flexibility as a process of string rewriting. VisForest is a software platform for creating and editing 3D scenes. It allows the fusion of geospatial data (terrain, images) with 3D objects (plants and house) in a georeferenced environment, definition of 3D scene component, distribution density and form of plants. The ForestSIMA is an application designed to assist in landscape level analysis and planning of forest ecosystems by automating the tasks of stand projection, graphical and tabular summarization, stand visualization, and landscape visualization. Its current and potential applications include plant modelling, growth simulation, assisted design and resource management in botany, ecology, applied plant sciences (horticulture, agriculture and forestry), landscape design, entertainment (games, films, and animations), military simulation and education.

Accurate in situ leaf area index (LAI) estimates of forest plots are required to validate currently-used LAI map products. Woody-to-total area ratio ( $\alpha$ ) is a crucial parameter in converting the plant area index estimates of forest plots obtained by optical methods into LAI. Although optical methods for estimating the $\alpha$ of forest canopy have been proposed, their performance has never been assessed. In this study, five Larix gmelinii Rupr. forest plots with contrasting plot characteristics (i.e., tree age, tree height, management activities, stand density, and site conditions) were selected. The performance of two commonly used optical methods, namely, multispectral canopy imager (MCI) and digital hemispherical photography (DHP), in estimating the $\alpha$ of L. gmelinii forest plots was evaluated by using the reference $\alpha$ of the selected forest plots. The reference $\alpha$ of forest plots was measured via destructive method by harvesting two or three representative trees in each plot. Large variations were observed amongst the reference $\alpha$ of the selected forest plots (ranging from 0% to 56%). These $\mathsf{\alpha }$ were also highly correlated with the site conditions and management activities in these plots. The effective $\alpha$ ( ${\alpha }_e$ ) or $\alpha$ estimated using the leaf-on and leaf-off periods MCI or DHP images with or without consideration of the clumping effects of canopy element and woody components were 1.57 to 4.63 times the reference $\alpha$ in the five plots. The overestimation of $\alpha$ or ${\alpha }_e$ was mainly caused by the preferential shading of woody components by the shoots in the leaf-on canopy. Accurate $\alpha$ estimates for the L. gmelinii forest plots with errors of less than 20% can be obtained from MCI when the clumping effects of canopy element and woody components are considered in the estimation.

The leaf inclination angle distribution function is a key determinant that influences radiation penetration through forest canopies. In this study, the needle and shoot inclination angle distributions of five contrasting Larix principis-rupprechtii plots were obtained via the frequently used leveled digital camera photography method. We also developed a quasi-automatic method to derive the needle inclination angles based on photographs obtained using the leveled digital camera photography method and further verified using manual measurements. Then, the variations of shoot and needle inclination angle distributions due to height levels, plots, and observation years were investigated. The results showed that the developed quasi-automatic method is effective in deriving needle inclination angles. The shoot and needle inclination angle distributions at the whole-canopy scale tended to be planophile and exhibited minor variations with plots and observation years. The small variations in the needle inclination angle distributions with height level in the five plots might be caused by contrasting light conditions at different height levels. The whole-canopy and height level needle projection functions also tended to be planophile, and minor needle projection function variations with plots and observation years were observed. We attempted to derive the shoot projection functions of the five plots by using a simple and applicable method and further evaluated the performance of the new method.

Optical methods are frequently used as a routine method to obtain the elementary sampling unit (ESU) leaf area index (LAI) of forests. However, few studies have attempted to evaluate whether the ESU LAI obtained from optical methods matches the accuracy required by the LAI map product validation community. In this study, four commonly used optical methods, including digital hemispherical photography (DHP), digital cover photography (DCP), tracing radiation of canopy and architecture (TRAC) and multispectral canopy imager (MCI), were adopted to estimate the ESU (25 m × 25 m) LAI of five Larix principis-rupprechtii forests with contrasting structural characteristics. The impacts of three factors, namely, inversion model, canopy element or woody components clumping index ( ${\Omega }_e$ or ${\Omega }_w$ ) algorithm, and the woody components correction method, on the ESU LAI estimation of the four optical methods were analyzed. Then, the LAI derived from the four optical methods was evaluated using the LAI obtained from litter collection measurements. Results show that the performance of the four optical methods in estimating the ESU LAI of the five forests was largely affected by the three factors. The accuracy of the LAI obtained from the DHP and MCI strongly relied on the inversion model, the ${\Omega }_e$ or ${\Omega }_w$ algorithm, and the woody components correction method adopted in the estimation. Then the best ${\Omega }_e$ or ${\Omega }_w$ algorithm, inversion model and woody components correction method to be used to obtain the ESU LAI of L. principis-rupprechtii forests with the smallest root mean square error (RMSE) and mean absolute error (MAE) were identified. Amongst the three typical woody components correction methods evaluated in this study, the woody-to-total area ratio obtained from the destructive measurements is the most effective method for DHP to derive the ESU LAI with the smallest RMSE and MAE. In contrast, using the woody area index obtained from the leaf-off DHP or DCP images as the woody components correction method would result in a large LAI underestimation. TRAC and MCI outperformed DHP and DCP in the ESU LAI estimation of the five forests, with the smallest RMSE and MAE. All the optical methods, except DCP, are qualified to obtain the ESU LAI of L. principis-rupprechtii forests with an MAE of <20% that is required by the global climate observation system. None of the optical methods, except TRAC, show the potential to obtain the ESU LAI of L. principis-rupprechtii forests with an MAE of <5%.