| Title | Application of the metabolic scaling theory and water-energy balance equation to model large-scale patterns of maximum forest canopy height |
| Authors | Choi, Sungho Kempes, Christopher P. Park, Taejin Ganguly, Sangram Wang, Weile Xu, Liang Basu, Saikat Dungan, Jennifer L. Simard, Marc Saatchi, Sassan S. Piao, Shilong Ni, Xiliang Shi, Yuli Cao, Chunxiang Nemani, Ramakrishna R. Knyazikhin, Yuri Myneni, Ranga B. |
| Affiliation | Boston Univ, Dept Earth & Environm, Boston, MA 02215 USA. CALTECH, Control & Dynam Syst, Pasadena, CA 91125 USA. Santa Fe Inst, Santa Fe, NM 87501 USA. BAERI, Moffett Field, CA 94035 USA. NASA Ames Res Ctr, Moffett Field, CA 94035 USA. Calif State Univ Monterey Bay, Div Sci & Environm Policy, Seaside, CA 93955 USA. NASA Ames Res Ctr, Biospher Sci Branch, Moffett Field, CA 94035 USA. Univ Calif Los Angeles, Inst Environm & Sustainabil, Los Angeles, CA 90095 USA. Louisiana State Univ, Dept Comp Sci, Baton Rouge, LA 70803 USA. NASA Ames Res Ctr, Div Earth Sci, Moffett Field, CA 94035 USA. CALTECH, Jet Prop Lab, 4800 Oak Grove Dr, Pasadena, CA 91109 USA. Peking Univ, Coll Urban & Environm Sci, Beijing 100871, Peoples R China. Peking Univ, Sino French Inst Earth Syst Sci, Beijing 100871, Peoples R China. Chinese Acad Sci, Inst Remote Sensing Applicat, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China. Nanjing Univ Informat Sci & Technol, Sch Remote Sensing, Nanjing 210044, Jiangsu, Peoples R China. NASA Ames Res Ctr, NASA Adv Supercomp Div, Moffett Field, CA 94035 USA. |
| Keywords | Carbon monitoring disturbance history geospatial predictors large-scale modelling maximum forest height mechanistic understanding metabolic scaling theory prognostic applications water-energy balance GENERAL QUANTITATIVE THEORY RESOURCE LIMITATIONS MODEL WAVE-FORM LIDAR TREE GROWTH CARBON RESPIRATION DYNAMICS ECOLOGY BIOLOGY BIOMASS |
| Issue Date | 2016 |
| Publisher | GLOBAL ECOLOGY AND BIOGEOGRAPHY |
| Citation | GLOBAL ECOLOGY AND BIOGEOGRAPHY.2016,25(12),1428-1442. |
| Abstract | AimForest height, an important biophysical property, underlies the distribution of carbon stocks across scales. Because in situ observations are labour intensive and thus impractical for large-scale mapping and monitoring of forest heights, most previous studies adopted statistical approaches to help alleviate measured data discontinuity in space and time. Here, we document an improved modelling approach which links metabolic scaling theory and the water-energy balance equation with actual observations in order to produce large-scale patterns of forest heights. MethodsOur model, called allometric scaling and resource limitations (ASRL), accounts for the size-dependent metabolism of trees whose maximum growth is constrained by local resource availability. Geospatial predictors used in the model are altitude and monthly precipitation, solar radiation, temperature, vapour pressure and wind speed. Disturbance history (i.e. stand age) is also incorporated to estimate contemporary forest heights. ResultsThis study provides a baseline map (c. 2005; 1-km(2) grids) of forest heights over the contiguous United States. The Pacific Northwest/California is predicted as the most favourable region for hosting large trees (c. 100 m) because of sufficient annual precipitation (> 1400 mm), moderate solar radiation (c. 330 W m(-2)) and temperature (c. 14 degrees C). Our results at sub-regional level are generally in good and statistically significant (P-value<0.001) agreement with independent reference datasets: field measurements [mean absolute error (MAE)=4.0 m], airborne/spaceborne lidar (MAE=7.0 m) and an existing global forest height product (MAE=4.9 m). Model uncertainties at county level are also discussed in this study. Main conclusionsWe improved the metabolic scaling theory to address variations in vertical forest structure due to ecoregion and plant functional type. A clear mechanistic understanding embedded within the model allowed synergistic combinations between actual observations and multiple geopredictors in forest height mapping. This approach shows potential for prognostic applications, unlike previous statistical approaches. |
| URI | http://hdl.handle.net/20.500.11897/458332 |
| ISSN | 1466-822X |
| DOI | 10.1111/geb.12503 |
| Indexed | SCI(E) |
| Appears in Collections: | 城市与环境学院 |
