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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Marvin, David C. Asner, Gregory P. Sinca, Felipe Knapp, David E. Anderson, Christopher B. Martin, Roberta E. Tupayachi, Raul |
| Spatial Coverage | Peru |
| Description | Author Affiliation: Marvin DC ( Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305 dmarvin@carnegiescience.edu.); Asner GP ( Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305.); Knapp DE ( Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305.); Anderson CB ( Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305.); Martin RE ( Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305.); Sinca F ( Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305.); Tupayachi R ( Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305.); |
| 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. |
| ISSN | 00278424 |
| e-ISSN | 10916490 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Issue Number | 48 |
| Volume Number | 111 |
| Language | English |
| Publisher | National Academy of Sciences |
| Publisher Date | 2014-12-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Biomass Ecosystem Growth & Development Algorithms Carbon Cycle Conservation Of Natural Resources Geography Models, Theoretical Population Density Population Dynamics Remote Sensing Technology Reproducibility Of Results Tropical Climate Research Support, Non-U.S. Gov't Multidisciplinary |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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