New Coupled Canopy–Light Model (CCLM) to Improve Visual Polymorphism Simulation of Fir Morphology

Abstract

Environmental factors substantially influence the growth of trees. The current studies on tree growth simulation have mainly focused on the effect of environmental factors on diameter at breast height and tree height. However, the influence of environmental factors, especially light, on canopy morphology has not been considered, hindering the accurate understanding of the range of characteristics of tree morphology that occur due to environmental changes. To solve this problem, this study investigated the influence of light on the changes in canopy morphology and constructed a coupled canopy–light model (CCLM) to visually simulate the polymorphism of fir morphology. Using the Huangfengqiao Forestry Farm in You County, Hunan Province, China, as the study area, we selected a typical sample plot. Field surveys of the fir trees in the sample plot were conducted for three consecutive years to obtain longitudinal data of fir tree canopy shape. We constructed the canopy curves using a cubic uniform B-spline to construct 3D models of the fir trees in different years. The topographic and spatial location distribution data of the fir trees were used to construct a 3D scene of the sample plot in the UE4 3D engine, and the light distribution for each part of the canopy was calculated in a 3D scene by using the annual average photosynthetically active radiation (PAR) as the light parameter, which we combined with the ray-tracing algorithm. This study constructed the CCLM from the fir diameter using the breast-height growth model (BDGM) and the height–diameter curve model (HDCM), the fir trees’ canopy shape description from two years, and the light distribution data. We compared the canopy data obtained from canopy simulations using the CCLM with those obtained using a growth model based on spatial structure (GMBOSS) and those obtained from field surveys to identify any difference in the effectiveness of the canopy simulations using the CCLM and GMBOSS. Based on the BDGM and HDCM, we constructed the CCLM of firs with a determination coefficient (R2) of 0.829, combining data on canopy shape descriptions obtained from two years of field surveys and the light distribution data of each part of the canopy obtained through the ray-tracing algorithm. The Euclidean distance between the canopy description data obtained using the CCLM and the canopy description data obtained from the field survey was 15.561; that between the GMBOSS and the field survey was 23.944. A virtual forest stand environment was constructed from the survey data, combining ray-tracing algorithms to construct the CCLM model of fir in a virtual forest stand environment for growth visualization and simulation. Compared with the canopy description data obtained using the GMBOSS, the canopy description data obtained using the CCLM better fit the canopy description data obtained from the field survey, and the Euclidean distance decreased from 23.944 to 15.561.