@article{fdi:010082120,
  title = {{S}imulating imaging spectroscopy in tropical forest with 3{D} radiative transfer modeling},
  author = {{E}bengo, {D}. {M}. and de {B}oissieu, {F}. and {V}incent, {G}r{\'e}goire and {W}eber, {C}. and {F}eret, {J}. {B}.},
  editor = {},
  language = {{ENG}},
  abstract = {{O}ptical remote sensing can contribute to biodiversity monitoring and species composition mapping in tropical forests. {I}nferring 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. {W}e explored the capability of the 3{D} radiative transfer model {DART} ({D}iscrete {A}nisotropic {R}adiative {T}ransfer) 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. {W}e 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 ({NPV}f). {T}he 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. {T}he influence of {LOP} variability on simulated reflectance was tested by considering variability at species, individual tree crown and pixel level. {W}e incorporated {NPV}f into simulations following two approaches, either considering {NPV}f as a part of wood area density in each voxel or using leaf brown pigments. {W}e 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. {T}he simulation of {NPV}f based on leaf brown pigments resulted in the closest match between measured and simulated canopy reflectance. {T}he definition of {LOP} at pixel level resulted in conservation of the spectral dissimilarity and expected performances for species discrimination. {T}herefore, we recommend future research on forest biodiversity using physical modeling of remote-sensing data to account for {LOP} variability within crowns and species. {O}ur 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. {T}his 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.},
  keywords = {diversity mapping ; imaging spectroscopy ; leaf traits ; radiative ; transfer ; {DART} ; {PROSPECT} ; {GUYANE} {FRANCAISE} ; {ZONE} {TROPICALE} ; {PARACOU}},
  booktitle = {},
  journal = {{R}emote {S}ensing},
  volume = {13},
  numero = {11},
  pages = {2120 [31 ]},
  year = {2021},
  DOI = {10.3390/rs13112120},
  URL = {https://www.documentation.ird.fr/hor/fdi:010082120},
}