@article{fdi:010093352,
  title = {{S}tate of the art and for remote sensing monitoring of carbon dynamics in {A}frican tropical forests},
  author = {{B}ossy, {T}. and {C}iais, {P}. and {R}enaudineau, {S}. and {W}an, {L}. and {Y}gorra, {B}. and {A}dam, {E}. and {B}arbier, {N}icolas and {B}auters, {M}. and {D}elbart, {N}. and {F}rappart, {F}. and {G}ara, {T}. {W}. and {H}amunyela, {E}. and {I}fo, {S}. {A}. and {J}affrain, {G}. and {M}aisongrande, {P}. and {M}ugabowindekwe, {M}. and {M}ugiraneza, {T}. and {N}ormandin, {C}. and {O}bame, {C}. {V}. and {P}eaucelle, {M}. and {P}inet, {C}. and {P}loton, {P}ierre and {S}agang, {L}. and {S}chwartz, {M}. and {S}ollier, {V}. and {S}onk{\'e}, {B}. and {T}resson, {P}aul and {D}e {T}ruchis, {A}. and {Q}uang, {A}. {V}. and {W}igneron, {J}. {P}.},
  editor = {},
  language = {{ENG}},
  abstract = {{A}frican tropical forests play a crucial role in global carbon dynamics, biodiversity conservation, and climate regulation, yet monitoring their structure, diversity, carbon stocks and changes remains challenging. {R}emote sensing techniques, including multi-spectral data, lidar-based canopy height and vertical structure detection, and radar interferometry, have significantly improved our ability to map forest composition, estimate height and biomass, and detect degradation and deforestation features at a finer scale. {M}achine learning approaches further enhance these capabilities by integrating multiple data sources to produce improved maps of forest attributes and track changes over time. {D}espite these advancements, uncertainties remain due to limited ground-truth validation, and the structural complexity and large spatial heterogeneity of {A}frican forests. {F}uture developments in remote sensing should examine how multi-sensor integration of high-resolution data from instruments such as {P}lanet, {T}andem-{X}, {SPOT} and improved {AI} methods can refine forest composition, carbon storage and function maps, enhance large-scale monitoring of tree height and biomass dynamics, and improve forest degradation and deforestation detection down to tree level. {T}hese advancements will be essential for supporting science-based decision-making in forest conservation and climate mitigation.},
  keywords = {remote sensing ; deep learning ; {C}ongo basin ; carbon ; biomass ; canopy height ; forest typology ; forest degradation ; {REPUBLIQUE} {DEMOCRATIQUE} {DU} {CONGO} ; {CONGO} ; {CENTRAFRIQUE} ; {CAMEROUN} ; {GUINEE} {EQUATORIALE} ; {CONGO} {CUVETTE} ; {ZONE} {TROPICALE}},
  booktitle = {},
  journal = {{F}rontiers in {R}emote {S}ensing},
  volume = {6},
  numero = {},
  pages = {1532280 [19 p.]},
  year = {2025},
  DOI = {10.3389/frsen.2025.1532280},
  URL = {https://www.documentation.ird.fr/hor/fdi:010093352},
}