Impact of Tree Crown Transmittance on Surface Reflectance Retrieval in the Shade for High Spatial Resolution Imaging Spectroscopy: A Simulation Analysis Based on Tree Modeling Scenarios

Abstract

With the advancement of high spatial resolution imaging spectroscopy, an accurate surface reflectance retrieval is needed to derive relevant physical variables for land cover mapping, soil, and vegetation monitoring. One challenge is to deal with tree shadows using atmospheric correction models if the tree crown transmittance Tc is not properly taken into account. This requires knowledge of the complex radiation mechanisms that occur in tree crowns, which can be provided by coupling the physical modeling of canopy radiative transfer codes (here DART) and the 3D representations of trees. First in this study, a sensitivity analysis carried out on DART simulations with an empirical 3D tree model led to a statistical regression predicting Tc from the tree leaf area index (LAI) and the solar zenith angle with good performances (RMSE ≤ 4.3% and R2 ≥ 0.91 for LAI ≤ 4 m2.m-2). Secondly, more realistic 3D voxel-grid tree models derived from terrestrial LiDAR measurements over two trees were considered. The comparison of DART-simulated Tc from these models with the previous predicted Tc over 0.4–2.5 µm showed three main sources of inaccuracy quoted in order of importance: 1) the global tree geometry shape (mean bias up to 21.5%), 2) the transmittance fraction associated to multiple scattering, Tscat (maximum bias up to 13%), and 3) the degree of realism of the tree representation (mean bias up to 7.5%). Results showed that neglecting Tc leads to very inaccurate reflectance retrieval (mean bias > 0.04), particularly if the background reflectance is high, and in the near and shortwave infrared – NIR and SWIR – due to Tscat. The transmittance fraction associated to the non-intercepted transmitted light, Tdir, can reach up to 95% in the SWIR, and Tscat up to 20% in the NIR. Their spatial contributions computed in the tree shadow have a maximum dispersion of 27% and 8% respectively. Investigating how to approximate Tdir and Tscat spectral and spatial variability along with the most appropriate tree 3D modeling is crucial to improve reflectance retrieval in tree shadows when using atmospheric correction models.

Visual Digital Forest Model Based on a Remote Sensing Data and Forest Inventory Data

“…Subsequently, the scale map allowed the formation of three-dimensional trees on the area where the tree species actually grew, with their exact positioning. Previous studies developed technologies for the three-dimensional modeling of plants based on the information obtained by remote methods [20][21][22][23]. After constructing the weight map, we proceeded to place objects by superimposing tree coordinate point features from map A1 (Figure 3) over point features from modeled region B1 (Figure 4).…”

“…2021, 13, x FOR PEER REVIEW 5 of 15 mation of three-dimensional trees on the area where the tree species actually grew, with their exact positioning. Previous studies developed technologies for the three-dimensional modeling of plants based on the information obtained by remote methods[20][21][22][23].…”

Tree Crown Delineation on Uav Imagery Using Combination of Machine Learning Algorithms With Majority Voting

“…In fact, due to the conditions of each region, season and type of each tree, the shape of crown has a growth rate in different directions, even if they are of the same type (Wu et al, 2021). Accurate description of crown shape is also an important input in physiological models (Adeline et al, 2021) and it is very effective in correctly identifying the species and the exact position of tree. Since access to a forest area is difficult, in order to obtain information about trees such as the area of crown and the shape of trees, it is necessary to use an accurate and economical method such as using remotely sensed data obtained by unmanned aerial vehicles (UAVs) (Gülci, 2019).…”

Effects of Mixture Mode on the Canopy Bidirectional Reflectance of Coniferous–Broadleaved Mixed Plantations

“…For instance, RGM (the radiosity-graphics combined model) [21] and RAPID (radiosity applicable to porous individual objects model) [22] are based on radiance algorithms, Raytran [23] and LESS (large-scale remote sensing data and image simulation framework) [24] are based on ray tracing, and DART (Discrete Anisotropic Radiative Transfer model) [25] is based on the discrete coordinate method. DART has been extensively adopted and successfully validated by many scientific researches, either in applications or in modeling [26][27][28][29]. In addition to its accuracy, DART offers the advantage of easily creating any landscape, which is very suitable for the simulation of the directional reflectance of plantations with different mixture modes in this study.…”