Comparison of Five Slope Correction Methods for Leaf Area Index Estimation From Hemispherical Photography

Abstract

We compare five slope correction methods developed by Walter et al., Montes et al., Schleppi et al., España et al., and Gonsamo et al. (referred to as WAL, MON, SCH, ESP, and GON, respectively) using artificial fisheye pictures simulated by graphics software and a lookup table (LUT) retrieval method. The LUT is built by simulating the directional gap fraction as a function of leaf area index (LAI) and average leaf inclination angle (ALIA) using the Poisson law. LAI and ALIA estimates correspond to the case of the LUT that provides the lowest root-mean-square error between the observed gap fractions after slope correction and the simulated ones. Three LAI values (1.5, 3.5, and 5.5), four ALIA values (26.8 • , 45 • , 57.5 • , and 63.2 • ), and three slope angles (0 • , 20 • , and 50 • ) constituted 36 samples of random scenes. ESP is recommended because its results are accurate and independent on the leaf angle distribution (LAD), while GON only performs well for spherical LAD. The three other methods present less good performances with underestimation or overestimation of LAI and/or ALIA depending on the LAD, and the recommended order for them is MON, SCH, and WAL. Index Terms-Average leaf inclination angle (ALIA), digital hemispherical photography (DHP), leaf area index (LAI), slope correction, topographic mask.Manuscript

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

“…The iterative and optimization method estimates the PAI and LAI of the leaf-on forest canopy by searching the designed gap fraction values that closely match the measured gap fraction values, and the estimates calculated based on the target designed gap fraction are the PAI and LAI of the leaf-on forest canopy [21]. The LUT method estimates the PAI and LAI of the leaf-on forest canopy using the principle similar to the iterative and optimization method, but it simplifies the optimization process of the iterative and optimization method using a table with limited combinations of PAI, LAI, and gap fraction [15,20]. Different zenith angle ranges have been used by the aforementioned inversion models to estimate the LAI or PAI of leaf-on forest canopy from optical methods, such as 0-45 • [18,24,27,28].…”

“…Various inversion models have been proposed and applied to estimate the PAI and LAI of leaf-on forest canopy from optical methods, such as the Beer law or Poisson model [10][11][12][13][14][15], Miller theory [15][16][17][18][19], Look-Up Table (LUT) [15,20], and iterative optimization technique [21]. Amongst these inversion models, Miller theory and Beer law are the most widely used.…”

“…Two key components of leaf-on forest canopy, such as the needles of shoots and woody component, were not modeled by the forest scenes generated in previous studies [18,20,46]. However, these two components significantly affected the gap fraction measurements, thereby influencing the PAI and LAI estimation via optical methods [8,9,15,47].…”

“…However, these two components significantly affected the gap fraction measurements, thereby influencing the PAI and LAI estimation via optical methods [8,9,15,47]. Compared with the forest scenes in previous studies [20,26,46], the explicit 3D forest scenes in the present study provide a detailed description of all components of forest canopy, such as stems, branches, needles, shoots, leaves, flowers, and fruits, using a large number of small primitive shapes (e.g., triangles, cylinders, spheres, and ellipsoids) [48,49]. The explicit 3D tree models which were used to generate the explicit 3D forest scenes were constructed based on the field measurements of structural attributes (e.g., height, diameter at breast height [DBH], crown width, leaf length, leaf width, first branch height, and number of branch levels) of the tree canopy and available single-tree modeling methods (e.g., parametric and L-system-based modeling) [49,50].…”

“…Although some studies attempted to assess the effect of inversion models on the PAI estimation of the leaf-on forest canopy, few commonly used inversion models were assessed by these studies, and the number of field plots covered were limited [16,18,[22][23][24]. Moreover, the zenith angle dependent of Ω e and G e were not considered by some studies in evaluating the performance of inversion models to estimate the PAI of leaf-on forest canopy [20,22,24]. However, previous studies showed that the PAI and WAI estimation of the leaf-on and leaf-off forest canopy from optical methods was significantly affected by the Ω e , Ω w , G e and G w [15,18,34,36,45]; therefore, they should be considered in evaluating the performance of the inversion models to estimate the PAI and WAI estimation of the leaf-on and leaf-off forest canopy.…”