@phdthesis{fdi:010086582,
  title = {{C}ontributions of multi-temporal airborne {L}i{DAR} data to mapping carbon stocks and fluxes in tropical forests},
  author = {{H}uertas-{G}arcia, {C}laudia},
  editor = {},
  language = {{ENG}},
  abstract = {{C}urrent climate change affects tropical forests functioning and might jeopardize their role as a global carbon sink. {A}ccurately documenting forest carbon fluxes at a meaningful scale is therefore a pressing challenge. {A}irborne {L}i{DAR} ({ALS}), which can provide a fine-grained description of canopy structure and dynamics has great potential. {T}his thesis explores the capabilities and limitations of airborne multitemporal {L}i{DAR} to map patterns of {C} fluxes over space and time to reduce uncertainty in models of carbon stocks and fluxes in tropical forests. {W}e relied on a combination of repeated {ALS} overflights extending over ten years and a large network of plots totaling more than 1.2 km2 of field inventories conducted at the {P}ermanent {R}esearch {S}tation of {P}aracou ({F}rench {G}uiana). {T}he first chapter ({Q}1. {E}fflux {M}odeling {M}ortality) addresses the possibility of developing reliable estimates of biomass, basal area and stem number loss (efflux) from observed changes in canopy height with repeated {ALS} overflights and further assesses whether gap dynamics show persistent over time. {A}bsolute basal area loss rate was linearly correlated to gap dynamics at the plot level ({R}2=0.60) and more strongly so when the analysis was restricted to undisturbed forests ({R}2 =0.72). {T}he rate of basal area loss was better predicted from gap dynamics than the rate of stem loss. {A}t the landscape scale, {L}i{DAR} data revealed that spatial patterns of gap creation were related to local topography and canopy height, where high canopy forests and bottomlands had higher mortality rates. {I}t is concluded that gap dynamics allow tracking the change in forest carbon fluxes, complementing the monitoring of net carbon change derived from static carbon estimates. {T}he second chapter ({Q}2. {A}llometry and carbon stock) quantifies the reduction of error in plot-level {AGB} estimates achieved using locally adjusted height-diameter allometries. {T}ree height data were obtained either from individually segmented crown in the {L}i{DAR} point cloud or from an individual-based forest model ({C}anopy {C}onstructor) globally adjusted to the {C}anopy height model. {B}ayesian multilevel modeling approach incorporated local canopy height and species identity as co-variates. {T}he quadratic error in predicting mean height was reduced by a factor of four by replacing the best universal alternative allometry with the locally derived {ALS} allometric {H}-{DBH} relationship, where nearly half of the reduction in quadratic error was due to reduced bias. {A} universal model (not adjusted for site-specific {H}-{DBH} allometry) underestimated {AGB} by 12-13 % at the site level. {T}he inclusion of species identity and canopy height was a considerable improvement, dramatically reducing the uncertainty in {H} prediction.},
  keywords = {{GUYANE} {FRANCAISE} ; {PARACOU}},
  address = {{M}ontpellier},
  publisher = {{IRD} ; {AMAP}},
  pages = {155  multigr.},
  year = {2022},
  URL = {https://www.documentation.ird.fr/hor/fdi:010086582},
}