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Application of the metabolic scaling theory and water-energy balance equation to model large-scale patterns of maximum forest canopy height

Choi, Sungho and Kempes, Christopher P. and Park, Taejin and Ganguly, Sangram and Wang, Weile and Xu, Liang and Basu, Saikat and Dungan, Jennifer L. and Simard, Marc and Saatchi, Sassan S. and Piao, Shilong and Ni, Xiliang and Shi, Yuli and Cao, Chunxiang and Nemani, Ramakrishna R. and Knyazikhin, Yuri and Myneni, Ranga B. (2016) Application of the metabolic scaling theory and water-energy balance equation to model large-scale patterns of maximum forest canopy height. Global Ecology and Biogeography, 25 (12). pp. 1428-1442. ISSN 1466-822X. http://resolver.caltech.edu/CaltechAUTHORS:20161208-083015439

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Abstract

Aim: Forest 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. Methods: Our 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. Results: This 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 °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 conclusions: We 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.


Item Type:Article
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1111/geb.12503DOIArticle
http://onlinelibrary.wiley.com/doi/10.1111/geb.12503/abstractPublisherArticle
Additional Information:© 2016 John Wiley & Sons Ltd. First published: 18 August 2016; Manuscript Accepted: 7 July 2016; Manuscript Revised: 6 July 2016; Manuscript Received: 21 July 2015.
Subject Keywords:Carbon monitoring; disturbance history; geospatial predictors; large-scale modelling; maximum forest height; mechanistic understanding; metabolic scaling theory; prognostic applications; water–energy balance
Record Number:CaltechAUTHORS:20161208-083015439
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20161208-083015439
Official Citation:Choi, S., Kempes, C. P., Park, T., Ganguly, S., Wang, W., Xu, L., Basu, S., Dungan, J. L., Simard, M., Saatchi, S. S., Piao, S., Ni, X., Shi, Y., Cao, C., Nemani, R. R., Knyazikhin, Y. and Myneni, R. B. (2016), Application of the metabolic scaling theory and water–energy balance equation to model large-scale patterns of maximum forest canopy height. Global Ecol. Biogeogr., 25: 1428–1442. doi:10.1111/geb.12503
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:72657
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:08 Dec 2016 21:33
Last Modified:08 Dec 2016 21:33

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