@inproceedings{fdi:010092549,
  title = {"{F}ire impacts in the {C}errado : integrating {L}i{DAR} and field data to monitor vegetation structure and post-fire recovery" [r{\'e}sum{\'e}]},
  author = {{M}achado, {M}. and da {C}ruz, {W}. and {C}arniello, {M}.{A}. and {S}turdivant, {E}. and {N}avarro-{R}osales, {F}. and {M}acedo, {M}. and {W}alker, {W}. and {O}liveras {M}enor, {I}mma},
  editor = {},
  language = {{ENG}},
  abstract = {{F}ire is a natural disturbance capable of altering plant distributions and community assemblages, influencing species evolution through the selection of traits and strategies, and affecting biogeochemical cycles. {T}his powerful tool of landscape transformation can negatively impact even a fire-dependent ecosystem when natural fire regimes are altered. {I}n recent times, interactions between human activities in the {C}errado (e.g., deforestation and intentional fires used to clear land), and a hotter and drier climate (due to climate change), have altered natural fire regimes causing more frequent and intense fire events, negatively impacting biodiversity, human health, and the regional climate. {T}hese fire-disturbed areas are widespread and highly vulnerable to future degradation from compounding disturbances, but they still harbour valuable biodiversity and carbon stocks that deserve protection and restoration. {M}onitoring the impacts of fire disturbance on vegetation structure and the potential pathways of recovery is critical to understand and protect resilient ecosystems under a rapidly changing climate. {R}obust monitoring requires the integration of modelled and field-based data tools and techniques. {F}ield inventories alone are insufficient to capture the spatiotemporal variability of impacts of fire on native vegetation and should be coupled with remotely sensed data, among which, {L}i{DAR} (light detection and ranging) is unparalleled in characterising 3-{D} vegetation structure. {T}hus, the combination of {L}i{DAR} and forest inventory data is ideally suited for scaling the impacts of fire on forest vegetation and associated carbon stocks. {I}n this study, we are assessing key metrics of vegetation structure derived from a combination of {L}i{DAR} and field data collected at the {E}xperimental {S}tation {S}erra das {A}raras, {M}ato {G}rosso state, {B}razil. {T}his field site comprises {C}errado vegetation that has been subject to three experimental fire treatments: every year, every two years, and every three years beginning in 2017, as well as fire suppression for over three decades. {W}e are investigating whether key vegetation structural metrics can capture different fire treatments and identify spatial patterns of disturbance. {W}e are also assessing if these patterns are different when comparing {L}i{DAR} data collected with a handheld scanner versus an airborne drone. {T}his study aims to refine our methods and improve our understanding of vegetation structure responses across a gradient of fire disturbance regimes and potential post-fire recovery trajectories, which are key not only for ecological studies but also for emerging carbon markets - one of several mechanisms aimed at achieving climate change mitigation, conservation, and sustainable development outcomes. {W}e hope to improve the process of carbon stock mapping in disturbed ecosystems and use the outputs to drive scenarios modelling at larger scales, providing a more comprehensive assessment of what future {C}errado carbon dynamics might look like under a range of possible disturbance/recovery dynamics.},
  keywords = {{BRESIL} ; {MATO} {GROSSO} ; {CERRADO}},
  numero = {},
  pages = {{EGU}24--19716 [2 ]},
  booktitle = {},
  year = {2024},
  DOI = {10.5194/egusphere-egu24-19716},
  URL = {https://www.documentation.ird.fr/hor/fdi:010092549},
}