title: Amazonian landscapes and the bias in field studies of forest structure and biomass.
abstract: Tropical forests convert more atmospheric carbon into biomass each year than any terrestrial ecosystem on Earth, underscoring the importance of accurate tropical forest structure and biomass maps for the understanding and management of the global carbon cycle. Ecologists have long used field inventory plots as the main tool for understanding forest structure and biomass at landscape-to-regional scales, under the implicit assumption that these plots accurately represent their surrounding landscape. However, no study has used continuous, high-spatial-resolution data to test whether field plots meet this assumption in tropical forests. Using airborne LiDAR (light detection and ranging) acquired over three regions in Peru, we assessed how representative a typical set of field plots are relative to their surrounding host landscapes. We uncovered substantial mean biases (9-98%) in forest canopy structure (height, gaps, and layers) and aboveground biomass in both lowland Amazonian and montane Andean landscapes. Moreover, simulations reveal that an impractical number of 1-ha field plots (from 10 to more than 100 per landscape) are needed to develop accurate estimates of aboveground biomass at landscape scales. These biases should temper the use of plots for extrapolations of forest dynamics to larger scales, and they demonstrate the need for a fundamental shift to high-resolution active remote sensing techniques as a primary sampling tool in tropical forest biomass studies. The potential decrease in the bias and uncertainty of remotely sensed estimates of forest structure and biomass is a vital step toward successful tropical forest conservation and climate-change mitigation policy.
authors.affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305 dmarvin@carnegiescience.edu.
authors.forename: David C
authors.initials: DC
authors.lastname: Marvin
authors.affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305.
authors.forename: Gregory P
authors.initials: GP
authors.lastname: Asner
authors.affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305.
authors.forename: David E
authors.initials: DE
authors.lastname: Knapp
authors.affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305.
authors.forename: Christopher B
authors.initials: CB
authors.lastname: Anderson
authors.affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305.
authors.forename: Roberta E
authors.initials: RE
authors.lastname: Martin
authors.affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305.
authors.forename: Felipe
authors.initials: F
authors.lastname: Sinca
authors.affiliation: Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305.
authors.forename: Raul
authors.initials: R
authors.lastname: Tupayachi
mesh_terms: D000465:Algorithms; D018533:Biomass; D057486:Carbon Cycle; D003247:Conservation of Natural Resources / Q000379:methods; D017753:Ecosystem; D065928:Forests; D005843:Geography; D008962:Models, Theoretical; D010568:Peru; D011156:Population Density; D011157:Population Dynamics; D058998:Remote Sensing Technology / Q000379:methods; D015203:Reproducibility of Results; D014197:Trees / Q000254:growth & development*; D014329:Tropical Climate
keywords: LiDAR; Peru tropical forest; canopy structure; field inventory plots; forest carbon