Beyond potential vegetation: Combining lidar data and a height-structured model for carbon studies

George C. Hurtt, Ralph Dubayah, Jason Drake, Paul R. Moorcroft, Stephen Wilson Pacala, J. Bryan Blair, Matthew G. Fearon

Research output: Contribution to journalArticlepeer-review

125 Scopus citations


Carbon estimates from terrestrial ecosystem models are limited by large uncertainties in the current state of the land surface. Natural and anthropogenic disturbances have important and lasting influences on ecosystem structure and fluxes that can be difficult to detect or assess with conventional methods. In this study, we combined two recent advances in remote sensing and ecosystem modeling to improve model carbon stock and flux estimates at a tropical forest study site at La Selva, Costa Rica (10°25′ N, 84°00′ W). Airborne lidar remote sensing was used to measure spatial heterogeneity in the vertical structure of vegetation. The ecosystem demography model (ED) was used to estimate the consequences of this heterogeneity for regional estimates of carbon stocks and fluxes. Lidar data provided substantial constraints on model estimates of both carbon stocks and net carbon fluxes. Lidar-initialized ED estimates of aboveground biomass were within 1.2% of regression-based approaches, and corresponding model estimates of net carbon fluxes differed substantially from bracketing alternatives. The results of this study provide a promising illustration of the power of combining lidar data on vegetation height with a height-structured ecosystem model. Extending these analyses to larger scales will require the development of regional and global lidar data sets, and the continued development and application of height structured ecosystem models.

Original languageEnglish (US)
Pages (from-to)873-883
Number of pages11
JournalEcological Applications
Issue number3
StatePublished - Jun 2004

All Science Journal Classification (ASJC) codes

  • Ecology


  • Aboveground biomass
  • Carbon fluxes
  • Costa Rica
  • Ecosystem demography
  • Ecosystem modeling
  • La Selva
  • Lidar
  • Regional carbon stocks
  • Remote sensing


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