Comparison of Seven Inversion Models for Estimating Plant and Woody Area Indices of Leaf-on and Leaf-off Forest Canopy Using Explicit 3D Forest Scenes

Abstract

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.

Retrieval of the Fraction of Radiation Absorbed by Photosynthetic Components (FAPARgreen) for Forest Using a Triple-Source Leaf-Wood-Soil Layer Approach

“…where ratio woody is the mean value of the woody-to-total area ratio for various forest types, as given in the literature [58,64,65]. LAI max is the maximum value of LAI within a given year; this was acquired from MCD15A2H LAI products.…”

“…In this study, we used a constant value of the woody-to-total area ratio for each forest type and derived the WAI from the PAI value for the peak growth stage. We obtained woody-to-total area ratios for different forest types (ENF, EBF, DBF, and DNF) from an extensive literature review [58,[64][65][66][67]-the statistical metrics, including the mean, standard deviation, and coefficient of variation, are shown in Table 4. These results show that the woody-to-total area ratios for different forest types vary from 0.158 to 0.3.…”

Performance of Four Optical Methods in Estimating Leaf Area Index at Elementary Sampling Unit of Larix principis-rupprechtii Forests

“…Optical methods are usually chosen amongst indirect methods as the routine method to derive the ESU LAI of forests due to its high efficiency, low cost, and non-destructiveness to canopies [14,16]. Previous studies reported that the ESU LAI estimation of forests for optical methods is mainly affected by six estimation error sources, including clumping effects, overestimation of woody components, inversion model, sampling scheme, terrain slope, and observation conditions [16][17][18][19]. Progress to correct the six LAI estimation error sources has been achieved recently [15,[19][20][21][22].…”

“…Previous studies reported that the ESU LAI estimation of forests for optical methods is mainly affected by six estimation error sources, including clumping effects, overestimation of woody components, inversion model, sampling scheme, terrain slope, and observation conditions [16][17][18][19]. Progress to correct the six LAI estimation error sources has been achieved recently [15,[19][20][21][22]. For example, Zou et al [19] attempted to evaluate the performance of seven inversion models in the ESU plant and woody area index (PAI and WAI) estimations of forests and recommended three inversion models.…”

“…Progress to correct the six LAI estimation error sources has been achieved recently [15,[19][20][21][22]. For example, Zou et al [19] attempted to evaluate the performance of seven inversion models in the ESU plant and woody area index (PAI and WAI) estimations of forests and recommended three inversion models. Cao et al [22] suggested three solutions to correct the influence of slope on the ESU LAI estimation of forests from DHP.…”