Simulating Imaging Spectroscopy in Tropical Forest with 3D Radiative Transfer Modeling

Abstract

Optical remote sensing can contribute to biodiversity monitoring and species composition mapping in tropical forests. Inferring ecological information from canopy reflectance is complex and data availability suitable to such a task is limiting, which makes simulation tools particularly important in this context. We explored the capability of the 3D radiative transfer model DART (Discrete Anisotropic Radiative Transfer) to simulate top of canopy reflectance acquired with airborne imaging spectroscopy in a complex tropical forest, and to reproduce spectral dissimilarity within and among species, as well as species discrimination based on spectral information. We focused on two factors contributing to these canopy reflectance properties: the horizontal variability in leaf optical properties (LOP) and the fraction of non-photosynthetic vegetation (NPVf). The variability in LOP was induced by changes in leaf pigment content, and defined for each pixel based on a hybrid approach combining radiative transfer modeling and spectral indices. The influence of LOP variability on simulated reflectance was tested by considering variability at species, individual tree crown and pixel level. We incorporated NPVf into simulations following two approaches, either considering NPVf as a part of wood area density in each voxel or using leaf brown pigments. We validated the different scenarios by comparing simulated scenes with experimental airborne imaging spectroscopy using statistical metrics, spectral dissimilarity (within crowns, within species, and among species dissimilarity) and supervised classification for species discrimination. The simulation of NPVf based on leaf brown pigments resulted in the closest match between measured and simulated canopy reflectance. The definition of LOP at pixel level resulted in conservation of the spectral dissimilarity and expected performances for species discrimination. Therefore, we recommend future research on forest biodiversity using physical modeling of remote-sensing data to account for LOP variability within crowns and species. Our simulation framework could contribute to better understanding of performances of species discrimination and the relationship between spectral variations and taxonomic and functional dimensions of biodiversity. This work contributes to the improved integration of physical modeling tools for applications, focusing on remotely sensed monitoring of biodiversity in complex ecosystems, for current sensors, and for the preparation of future multispectral and hyperspectral satellite missions.

UAV‐Lidar reveals that canopy structure mediates the influence of edge effects on forest diversity, function and microclimate

“…A better accounting for these effects could allow deriving quantitative estimates of plant area within 3D voxels (Vincent et al, 2017). Radiative transfer models based on these estimates and integrating sun angle and sky light (Ebengo et al, 2021; Gastellu‐Etchegorry, 2008) would then provide more precise estimates of micrometeorological variables at any given time. Moving towards such approaches should help to understand and predict the current and future responses of forest microclimate and functioning to changes in disturbance regimes driven by ongoing climate and land‐use changes (De Frenne et al, 2021; Zellweger et al, 2020).…”

Simulating the 3D Distribution of Absorbed Shortwave Radiation in a Tree Crown: Comparison of Simplified and Monte Carlo Models

“…For denser canopies, Ebengo et al. (2021) compared the hyperspectral image obtained by airborne observation and that obtained by numerical simulation in rainforests and showed that the difference in bias between treatments of woody elements was small even for NIR. Schneider et al.…”

“…Widlowski et al (2014) also showed that omitting woody elements resulted in a non-negligible bias in the calculation of the bidirectional reflectance factor for the savanna canopy. For denser canopies, Ebengo et al (2021) compared the hyperspectral image obtained by airborne observation and that obtained by numerical simulation in rainforests and showed that the difference in bias between treatments of woody elements was small even for NIR. Schneider et al (2014) also showed that the simulated at-sensor radiance in the NIR domain decreased only slightly by adding woody elements in the calculation for an old temperate mixed forest.…”

Gaussian Process Regression Hybrid Models for the Top-of-Atmosphere Retrieval of Vegetation Traits Applied to PRISMA and EnMAP Imagery

“…Other methods such as multifidelity [98] can improve the efficiency of the retrieval while improving the accuracy of the output product. Additionally, the use of more advanced three-dimensional RTMs, such as DART e.g., [99] could be leveraged to generate more data and feed these large-scale approaches to construct more accurate models. To further broaden the applicability and implementation on other hyperspectral missions and in a range of geographical settings would significantly enhance the generalisability of the models e.g., [100].…”