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Allometric Scaling and Resource Limitations Model of Tree Heights: Part 1. Model Optimization and Testing over Continental USA
School of Remote Sensing, Nanjing University of Information Science and Technology, Nanjing 210044, China
Department of Earth and Environment, Boston University, 675 Commonwealth Avenue, Boston, MA 02215, USA
State Key Laboratory of Remote Sensing Sciences, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing 100101, China
Bay Area Environmental Research Institute (BAERI)/NASA Ames Research Center, Moffett Field, CA 94035, USA
Department of Watershed Science, Utah State University, Logan, UT 84322, USA
Center for Ecological Analysis of Lidar, Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA
Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Gove Dr., Pasadena, CA 91109, USA
Department of Geography, Hunter College of CUNY, New York, NY 10065, USA
College of Urban and Environmental Sciences and Sino-French Institute for Earth System Science, Peking University, Beijing 100871, China
Biospheric Science Branch, NASA Ames Research Center, Moffett Field, CA 94035, USA
These authors contributed equally to this work.
* Author to whom correspondence should be addressed.
Received: 12 November 2012; in revised form: 14 January 2013 / Accepted: 15 January 2013 / Published: 17 January 2013
Abstract: A methodology to generate spatially continuous fields of tree heights with an optimized Allometric Scaling and Resource Limitations (ASRL) model is reported in this first of a multi-part series of articles. Model optimization is performed with the Geoscience Laser Altimeter System (GLAS) waveform data. This methodology is demonstrated by mapping tree heights over forested lands in the continental USA (CONUS) at 1 km spatial resolution. The study area is divided into 841 eco-climatic zones based on three forest types, annual total precipitation classes (30 mm intervals) and annual average temperature classes (2 °C intervals). Three model parameters (area of single leaf, α, exponent for canopy radius, η, and root absorption efficiency, γ) were selected for optimization, that is, to minimize the difference between actual and potential tree heights in each of the eco-climatic zones over the CONUS. Tree heights predicted by the optimized model were evaluated against GLAS heights using a two-fold cross validation approach (R2 = 0.59; RMSE = 3.31 m). Comparison at the pixel level between GLAS heights (mean = 30.6 m; standard deviation = 10.7) and model predictions (mean = 30.8 m; std. = 8.4) were also performed. Further, the model predictions were compared to existing satellite-based forest height maps. The optimized ASRL model satisfactorily reproduced the pattern of tree heights over the CONUS. Subsequent articles in this series will document further improvements with the ultimate goal of mapping tree heights and forest biomass globally.
Keywords: tree height; allometric scaling law; resource limitations; GLAS; model optimization
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Shi, Y.; Choi, S.; Ni, X.; Ganguly, S.; Zhang, G.; Duong, H.V.; Lefsky, M.A.; Simard, M.; Saatchi, S.S.; Lee, S.; Ni-Meister, W.; Piao, S.; Cao, C.; Nemani, R.R.; Myneni, R.B. Allometric Scaling and Resource Limitations Model of Tree Heights: Part 1. Model Optimization and Testing over Continental USA. Remote Sens. 2013, 5, 284-306.
Shi Y, Choi S, Ni X, Ganguly S, Zhang G, Duong HV, Lefsky MA, Simard M, Saatchi SS, Lee S, Ni-Meister W, Piao S, Cao C, Nemani RR, Myneni RB. Allometric Scaling and Resource Limitations Model of Tree Heights: Part 1. Model Optimization and Testing over Continental USA. Remote Sensing. 2013; 5(1):284-306.
Shi, Yuli; Choi, Sungho; Ni, Xiliang; Ganguly, Sangram; Zhang, Gong; Duong, Hieu V.; Lefsky, Michael A.; Simard, Marc; Saatchi, Sassan S.; Lee, Shihyan; Ni-Meister, Wenge; Piao, Shilong; Cao, Chunxiang; Nemani, Ramakrishna R.; Myneni, Ranga B. 2013. "Allometric Scaling and Resource Limitations Model of Tree Heights: Part 1. Model Optimization and Testing over Continental USA." Remote Sens. 5, no. 1: 284-306.